The Health Belief Model: A Qualitative Study to Understand High-risk Sexual Behavior in Chinese Men Who Have Sex With Men.

PubMed

Li, Xianhong; Lei, Yunxiao; Wang, Honghong; He, Guoping; Williams, Ann Bartley

2016-01-01

The Health Belief Model (HBM) has been widely used to explain rationales for health risk-taking behaviors. Our qualitative study explored the applicability of the HBM to understand high-risk sexual behavior in Chinese men who have sex with men (MSM) and to elaborate each component of the model. HIV knowledge and perception of HIV prevalence contributed to perceived susceptibility. An attitude of treatment optimism versus hard life in reality affected perceived severity. Perceived barriers included discomfort using condoms and condom availability. Perceived benefits included prevention of HIV and other sexually transmitted illnesses. Sociocultural cues for Chinese MSM were elaborated according to each component. The results demonstrated that the HBM could be applied to Chinese MSM. When used with this group, it provided information to help develop a population- and disease-specific HBM scale. Results of our study also suggested behavioral interventions that could be used with Chinese MSM to increase condom use. Copyright © 2016 Association of Nurses in AIDS Care. All rights reserved.

Predicting human papillomavirus vaccine uptake in young adult women: Comparing the Health Belief Model and Theory of Planned Behavior

PubMed Central

Gerend, Mary A.; Shepherd, Janet E.

2012-01-01

Background Although theories of health behavior have guided thousands of studies, relatively few studies have compared these theories against one another. Purpose The purpose of the current study was to compare two classic theories of health behavior—the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB)—in their prediction of human papillomavirus (HPV) vaccination. Methods After watching a gain-framed, loss-framed, or control video, women (N=739) ages 18–26 completed a survey assessing HBM and TPB constructs. HPV vaccine uptake was assessed ten months later. Results Although the message framing intervention had no effect on vaccine uptake, support was observed for both the TPB and HBM. Nevertheless, the TPB consistently outperformed the HBM. Key predictors of uptake included subjective norms, self-efficacy, and vaccine cost. Conclusions Despite the observed advantage of the TPB, findings revealed considerable overlap between the two theories and highlighted the importance of proximal versus distal predictors of health behavior. PMID:22547155

Psychological models for development of motorcycle helmet use among students in Vietnam

NASA Astrophysics Data System (ADS)

Kumphong, J.; Satiennam, T.; Satiennam, W.; Trinh, Tu Anh

2018-04-01

A helmet can reduce head accident severity. The aim of this research study was to study the intention for helmet use of students who ride motorcycles in Vietnam, by Structural Equation Modeling (SEM). Questionnaires developed by several traffic psychology modules, including the Theory of Planned Behaviour (TPB), Traffic Locus of Control (T-LOC), and Health Belief Model (HBM), were distributed to students at Ton Thang University and University of Architecture, Ho Chi Minh City. SEM was used to explain helmet use behaviour. The results indicate that TPB, T-LOC and HBM could explain the variance in helmet use behaviour. However, TPB can explain behaviour (helmet use intention) better than T-LOC and HBM. The outcome of this study is useful for the agencies responsible to improve motorcycle safety.

Effect of alcohol on skin permeation and metabolism of an ester-type prodrug in Yucatan micropig skin.

PubMed

Fujii, Makiko; Ohara, Rieko; Matsumi, Azusa; Ohura, Kayoko; Koizumi, Naoya; Imai, Teruko; Watanabe, Yoshiteru

2017-11-15

We studied the effect that three alcohols, ethanol (EA), propanol (PA), and isopropanol (IPA), have on the skin permeation of p-hydroxy benzoic acid methyl ester (HBM), a model ester-type prodrug. HBM was applied to Yucatan micropig skin in a saturated phosphate buffered solution with or without 10% alcohol, and HBM and related materials in receptor fluid and skin were determined with HPLC. In the absence of alcohol, p-hydroxy benzoic acid (HBA), a metabolite of HBM, permeated the skin the most. The three alcohols enhanced the penetration of HBM at almost the same extent. The addition of 10% EA or PA to the HBM solution led to trans-esterification into the ethyl ester or propyl ester of HBA, and these esters permeated skin as well as HBA and HBM did. In contrast, the addition of 10% IPA promoted very little trans-esterification. Both hydrolysis and trans-esterification in the skin S9 fraction were inhibited by BNPP, an inhibitor of carboxylesterase (CES). Western blot and native PAGE showed the abundant expression of CES in micropig skin. Both hydrolysis and trans-esterification was simultaneously catalyzed by CES during skin permeation. Our data indicate that the alcohol used in dermal drug preparations should be selected not only for its ability to enhance the solubility and permeation of the drug, but also for the effect on metabolism of the drug in the skin. Copyright © 2017 Elsevier B.V. All rights reserved.

Active muscle response using feedback control of a finite element human arm model.

PubMed

Östh, Jonas; Brolin, Karin; Happee, Riender

2012-01-01

Mathematical human body models (HBMs) are important research tools that are used to study the human response in car crash situations. Development of automotive safety systems requires the implementation of active muscle response in HBM, as novel safety systems also interact with vehicle occupants in the pre-crash phase. In this study, active muscle response was implemented using feedback control of a nonlinear muscle model in the right upper extremity of a finite element (FE) HBM. Hill-type line muscle elements were added, and the active and passive properties were assessed. Volunteer tests with low impact loading resulting in elbow flexion motions were performed. Simulations of posture maintenance in a gravity field and the volunteer tests were successfully conducted. It was concluded that feedback control of a nonlinear musculoskeletal model can be used to obtain posture maintenance and human-like reflexive responses in an FE HBM.

Factors influencing seasonal influenza vaccination behaviour among elderly people: a systematic review.

PubMed

Kan, T; Zhang, J

2018-03-01

To explore the behaviour-related factors influencing influenza vaccination among elderly people using a framework derived from the Health Belief Model (HBM) and the Theory of Reasoned Action (TRA). Systematic review. Five databases were searched using predetermined strategies in March 2016, and 1927 citations were identified. Articles were selected according to inclusion and exclusion criteria. Key information was extracted from selected studies using a predesigned sheet. Both authors assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) or Critical Appraisal Skills Programme (CASP) checklist. Thirty-six articles were selected. A new framework was proposed that contributes to shared understanding of factors influencing health behaviour. Possible determinants of influenza vaccination among elderly people were knowledge, health promotion factors, all constructs of the HBM, and some concepts of the TRA. Key factors were threat perception, behavioural beliefs, subjective norms, recommendations, past behaviour and perceived barriers. This is the first systematic review to analyse the factors influencing influenza vaccination behaviour of elderly people using a framework integrating the HBM and the TRA. The framework identified key factors of influenza vaccination and presented the inter-relation of behaviour-related variables. However, further well-designed studies are required to explore the inter-relationships accurately and comprehensively. Copyright © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

In vivo activity assessment of a "honey-bee pollen mix" formulation.

PubMed

Küpeli Akkol, Esra; Orhan, Didem Deliorman; Gürbüz, Ilhan; Yesilada, Erdem

2010-03-01

Honey-bee pollen mix (HBM) formulation is claimed to be effective for the treatment of asthma, bronchitis, cancers, peptic ulcers, colitis, various types of infections including hepatitis B, and rheumatism by the herb dealers in northeast Turkey. In the present study, in vivo antinociceptive, anti-inflammatory, gastroprotective and antioxidant effects of pure honey and HBM formulation were evaluated comparatively. HBM did not show any significant gastroprotective activity in a single administration at 250 mg/kg dose, whereas a weak activity was observed after three days of successive administration at 500 mg/kg dose. On the other hand, HBM displayed significant antinociceptive (p <0.01) and anti-inflammatory (p <0.01) activities at 500 mg/kg dose orally without inducing any apparent acute toxicity or gastric damage. HBM was also shown to possess potent antilipidperoxidant activity (p <0.01) at 500 mg/kg dose against acetaminophen-induced liver necrosis model in mice. On the other hand, pure honey did not exert any remarkable antinociceptive, anti-inflammatory and gastroprotective activity, but a potent antilipidperoxidant activity (p <0.01) was determined. Results have clearly proved that mixing pure honey with bee pollen significantly increased the healing potential of honey and provided additional support for its traditional use. Total phenolic and flavonoid contents of HBM were found to be 145 and 59.3 mg/100 g of honey, which were estimated as gallic acid and quercetin equivalents, respectively.

'Sink or swim': an evaluation of the clinical characteristics of individuals with high bone mass.

PubMed

Gregson, C L; Steel, S A; O'Rourke, K P; Allan, K; Ayuk, J; Bhalla, A; Clunie, G; Crabtree, N; Fogelman, I; Goodby, A; Langman, C M; Linton, S; Marriott, E; McCloskey, E; Moss, K E; Palferman, T; Panthakalam, S; Poole, K E S; Stone, M D; Turton, J; Wallis, D; Warburton, S; Wass, J; Duncan, E L; Brown, M A; Davey-Smith, G; Tobias, J H

2012-02-01

High bone mineral density on routine dual energy X-ray absorptiometry (DXA) may indicate an underlying skeletal dysplasia. Two hundred fifty-eight individuals with unexplained high bone mass (HBM), 236 relatives (41% with HBM) and 58 spouses were studied. Cases could not float, had mandible enlargement, extra bone, broad frames, larger shoe sizes and increased body mass index (BMI). HBM cases may harbour an underlying genetic disorder. High bone mineral density is a sporadic incidental finding on routine DXA scanning of apparently asymptomatic individuals. Such individuals may have an underlying skeletal dysplasia, as seen in LRP5 mutations. We aimed to characterize unexplained HBM and determine the potential for an underlying skeletal dysplasia. Two hundred fifty-eight individuals with unexplained HBM (defined as L1 Z-score ≥ +3.2 plus total hip Z-score ≥ +1.2, or total hip Z-score ≥ +3.2) were recruited from 15 UK centres, by screening 335,115 DXA scans. Unexplained HBM affected 0.181% of DXA scans. Next 236 relatives were recruited of whom 94 (41%) had HBM (defined as L1 Z-score + total hip Z-score ≥ +3.2). Fifty-eight spouses were also recruited together with the unaffected relatives as controls. Phenotypes of cases and controls, obtained from clinical assessment, were compared using random-effects linear and logistic regression models, clustered by family, adjusted for confounders, including age and sex. Individuals with unexplained HBM had an excess of sinking when swimming (7.11 [3.65, 13.84], p < 0.001; adjusted odds ratio with 95% confidence interval shown), mandible enlargement (4.16 [2.34, 7.39], p < 0.001), extra bone at tendon/ligament insertions (2.07 [1.13, 3.78], p = 0.018) and broad frame (3.55 [2.12, 5.95], p < 0.001). HBM cases also had a larger shoe size (mean difference 0.4 [0.1, 0.7] UK sizes, p = 0.009) and increased BMI (mean difference 2.2 [1.3, 3.1] kg/m(2), p < 0.001). Individuals with unexplained HBM have an excess of clinical characteristics associated with skeletal dysplasia and their relatives are commonly affected, suggesting many may harbour an underlying genetic disorder affecting bone mass.

Effect of a Health Belief Model-based nursing intervention on Chinese patients with moderate to severe chronic obstructive pulmonary disease: a randomised controlled trial.

PubMed

Wang, Ying; Zang, Xiao-Ying; Bai, Jinbing; Liu, Su-Yan; Zhao, Yue; Zhang, Qing

2014-05-01

To test the effect of a Health Belief Model-based nursing intervention on healthcare outcomes in Chinese patients with moderate to severe COPD. The Health Belief Model (HBM) has been internationally validated in a variety of chronic conditions. However, nursing intervention based on the HBM is less explored in Chinese patients with COPD. A randomised controlled trial. Enrolled patients were randomly assigned to the intervention and control groups. Patients in the intervention group received a 20- to 30-minute HBM-based nursing intervention every 2 days during the hospitalisation period after disease conditions were stable, with additional follow-ups after discharge. Patients in the control group received routine nursing care. Patients had significantly increased scores of health belief and self-efficacy after receiving the HBM-based nursing intervention. After receiving the 3-month follow-up, patients in the intervention group had significantly higher mean total scores in the Health Belief Scale and the COPD Self-Efficacy Scale, as well as in all the subscales, than those in the control group except the perceived disease seriousness. Results showed that the value of FEV1 /FVC ratio had a significant difference between study groups before and after the intervention. Results also indicated that mean scores of the Dyspnea Scale, 6-minute walking distance and ADL were significantly different between the groups and between the study time-points. Among patients with moderate to severe COPD, nursing intervention based on the HBM can enhance their health belief and self-efficacy towards the disease management, decrease dyspnoea and improve exercise tolerance and ADL. Nurses can use the HBM-based intervention to enhance patients' health belief and self-efficacy towards the management of COPD, and subsequently benefit healthcare outcomes. © 2013 John Wiley & Sons Ltd.

[Substance monograph on bisphenol A (BPA) - reference and human biomonitoring (HBM) values for BPA in urine. Opinion of the Human Biomonitoring Commission of the German Federal Environment Agency (UBA)].

PubMed

2012-09-01

Bisphenol A (BPA) is used for the production of polycarbonates and synthetic resins. Many of the items that contain BPA, for example polycarbonate bottles and coated cans, are commodities from which BPA can migrate into food and drinks, resulting in ubiquitous exposure of the population. Numerous animal studies and in vitro tests have shown that BPA acts as an "endocrine disruptor". Because of the still incomplete understanding of the complex and contradictory effects of BPA at doses below the NOAEL, the toxicological significance of recent findings is uncertain. The German HBM Commission takes notice that the risk assessment is currently in flux and that in the EU and other countries precautionary bans on BPA have been introduced. In the light of the extensive and growing body of literature, the Commission does not see itself in a position to resolve this controversy, nor to answer the question of the relevance of observed effects of low BPA doses on human health. The Commission has derived reference values (RV95) and TDI-based HBM I values for total BPA in urine. The RV95 values are 30 μg/l for 3-5 year olds, 15 μg/l for 6-14 year olds, and 7 μg/l for 20-29 year olds. The HBM I value for children is 1.5 mg/l and 2.5 mg/l for adults, respectively. The Commission emphasizes that the HBM values will require immediate adjustment should the current TDI of 0.05 mg/kg bw/day be changed. For the practical application of HBM, the Commission recommends an assessment based on the RV95. Confirmed exceedance of the RV95 by repeat measurements should prompt a search for the possible source(s), following the ALARA principle.

[Monograph on di-2-propylheptyl phthalate (DPHP) - human biomonitoring (HBM) values for the sum of metabolites oxo-mono-propylheptyl phthalate (oxo-MPHP) and hydroxy-mono-propylheptyl phthalate (OH MPHP) in adult and child urine. Opinion of the Commission "Human Biomonitoring" of the Federal Environment Agency, Germany].

PubMed

2015-07-01

1,2-benzenedicarboxylic acid, bis(2-propylheptyl)ester (bis(2-propylheptyl)phthalate, DPHP) is used as plasticizer for the manufacture of plastics, i.e. mainly polyvinylchloride (PVC). A subchronic feeding study with rats revealed a NOAEL (no observed adverse effect level) of 40 mg/(kg bw · d), which can be used as a point of departure (POD) for the derivation of an HBM-I value. Application of a total assessment factor of 200 leads to an estimation of 200 µg/kg bw as a tolerable daily intake of DPHP. On the basis of the results of metabolism studies with humans it is possible to calculate from the tolerable daily intake of DPHP to the tolerable concentration of specific metabolites in urine. Thus an HBM-I value of 1 mg/L morning urine for children and 1.5 mg/L morning urine for adults was derived for the sum of the oxidized monoesters oxo-MPHP and OH-MPHP, which were identified as robust and conclusive biomarkers for DPHP.

Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model

PubMed Central

Bitzer, Sebastian; Park, Hame; Blankenburg, Felix; Kiebel, Stefan J.

2014-01-01

Behavioral data obtained with perceptual decision making experiments are typically analyzed with the drift-diffusion model. This parsimonious model accumulates noisy pieces of evidence toward a decision bound to explain the accuracy and reaction times of subjects. Recently, Bayesian models have been proposed to explain how the brain extracts information from noisy input as typically presented in perceptual decision making tasks. It has long been known that the drift-diffusion model is tightly linked with such functional Bayesian models but the precise relationship of the two mechanisms was never made explicit. Using a Bayesian model, we derived the equations which relate parameter values between these models. In practice we show that this equivalence is useful when fitting multi-subject data. We further show that the Bayesian model suggests different decision variables which all predict equal responses and discuss how these may be discriminated based on neural correlates of accumulated evidence. In addition, we discuss extensions to the Bayesian model which would be difficult to derive for the drift-diffusion model. We suggest that these and other extensions may be highly useful for deriving new experiments which test novel hypotheses. PMID:24616689

Breast cancer literacy and health beliefs related to breast cancer screening among American Indian women.

PubMed

Roh, Soonhee; Burnette, Catherine E; Lee, Yeon-Shim; Jun, Jung Sim; Lee, Hee Yun; Lee, Kyoung Hag

2018-08-01

The purpose of this article is to examine the health beliefs and literacy about breast cancer and their relationship with breast cancer screening among American Indian (AI) women. Using the Health Belief Model (HBM) and hierarchical logistic regression with data from a sample of 286 AI female adults residing in the Northern Plains, we found that greater awareness of breast cancer screening was linked to breast cancer screening practices. However, perceived barriers, one of the HBM constructs, prevented such screening practices. This study suggested that culturally relevant HBM factors should be targeted when developing culturally sensitive breast cancer prevention efforts.

Health Belief Factors and Dispositional Optimism as Predictors of STD and HIV Preventive Behavior

ERIC Educational Resources Information Center

Zak-Place, Jennifer; Stern, Marilyn

2004-01-01

Identifying factors predictive of youth's engaging in preventive behaviors related to sexually transmitted diseases (STDs) and HIV remains a prominent public health concern. The utility of the Health Belief Model (HBM) continues to be suggested in identifying preventive behaviors. This study sought to examine the full HBM, including self-efficacy,…

Human biomonitoring in Israel: Recent results and lessons learned.

PubMed

Berman, Tamar; Goldsmith, Rebecca; Levine, Hagai; Grotto, Itamar

2017-03-01

The use of human biomonitoring (HBM) as a tool for environmental health policy and research is developing rapidly in Israel. Despite challenges in securing political and financial support for HBM, the Ministry of Health has initiated national HBM studies and has utilized HBM data in environmental health policy decision making. Currently, the Ministry of Health is collecting urine samples from children and adults in the framework of the National Health and Nutrition Study (MABAT), with the goal of ongoing surveillance of population exposure to pesticides and environmental tobacco smoke, and of combining HBM data with data on diet and health behavior. In academic research studies in Israel, biomarkers are used increasingly in environmental epidemiology, including in three active birth cohort studies on adverse health effects of phthalates, brominated flame retardants, and organophosphate pesticides. Future Ministry of Health goals include establishing HBM analytical capabilities, developing a long term national HBM plan for Israel and participating in the proposed HBM4EU project in order to improve data harmonization. One of the lessons learned in Israel is that even in the absence of a formal HBM program, it is possible to collect meaningful HBM data and use it in an ad hoc fashion to support environmental health policy. Copyright © 2016 Elsevier GmbH. All rights reserved.

Application of the Health Belief Model to U.S. Magazine Text and Image Coverage of Skin Cancer and Recreational Tanning (2000-2012).

PubMed

McWhirter, Jennifer E; Hoffman-Goetz, Laurie

2016-01-01

The health belief model (HBM) has been widely used to inform health education, social marketing, and health communication campaigns. Although the HBM can explain and predict an individual's willingness to engage in positive health behaviors, its application to, and penetration of the underlying constructs into, mass media content has not been well characterized. We examined 574 articles and 905 images about skin cancer and tanning risks, behaviors, and screening from 20 U.S. women's and men's magazines (2000-2012) for the presence of HBM constructs: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and cues to action. Susceptibility (48.1%) and severity (60.3%) information was common in text. Perceived benefits (36.4%) and barriers (41.5%) to prevention of skin cancer were fairly equally mentioned in articles. Self-efficacy (48.4%) focused on sunscreen use. There was little emphasis on HBM constructs related to early detection. Few explicit cues to action about skin cancer appeared in text (12.0%) or images (0.1%). HBM constructs were present to a significantly greater extent in text versus images (e.g., severity, 60.3% vs. 11.3%, respectively, χ(2) = 399.51, p < .0001; benefits prevention, 36.4% vs. 8.0%, respectively, χ(2) = 184.80, p < .0001), suggesting that readers are not visually messaged in ways that would effectively promote skin cancer prevention and early detection behaviors.

Parameter study for child injury mitigation in near-side impacts through FE simulations.

PubMed

Andersson, Marianne; Pipkorn, Bengt; Lövsund, Per

2012-01-01

The objective of this study is to investigate the effects of crash-related car parameters on head and chest injury measures for 3- and 12-year-old children in near-side impacts. The evaluation was made using a model of a complete passenger car that was impacted laterally by a barrier. The car model was validated in 2 crash conditions: the Insurance Institute for Highway Safety (IIHS) and the US New Car Assessment Program (NCAP) side impact tests. The Small Side Impact Dummy (SID-IIs) and the human body model 3 (HBM3) (Total HUman Model for Safety [THUMS] 3-year-old) finite element models were used for the parametric investigation (HBM3 on a booster). The car parameters were as follows: vehicle mass, side impact structure stiffness, a head air bag, a thorax-pelvis air bag, and a seat belt with pretensioner. The studied dependent variables were as follows: resultant head linear acceleration, resultant head rotational acceleration, chest viscous criterion, rib deflection, and relative velocity at head impact. The chest measurements were only considered for the SID-IIs. The head air bag had the greatest effect on the head measurements for both of the occupant models. On average, it reduced the peak head linear acceleration by 54 g for the HBM3 and 78 g for the SID-IIs. The seat belt had the second greatest effect on the head measurements; the peak head linear accelerations were reduced on average by 39 g (HBM3) and 44 g (SID-IIs). The high stiffness side structure increased the SID-IIs' head acceleration, whereas it had marginal effect on the HBM3. The vehicle mass had a marginal effect on SID-IIs' head accelerations, whereas the lower vehicle mass caused 18 g higher head acceleration for HBM3 and the greatest rotational acceleration. The thorax-pelvis air bag, vehicle mass, and seat belt pretensioner affected the chest measurements the most. The presence of a thorax-pelvis air bag, high vehicle mass, and a seat belt pretensioner all reduced the chest viscous criterion (VC) and peak rib deflection in the SID-IIs. The head and thorax-pelvis air bags have the potential to reduce injury measurements for both the SID-IIs and the HBM3, provided that the air bag properties are designed to consider these occupant sizes also. The seat belt pretensioner is also effective, provided that the lateral translation of the torso is managed by other features. The importance of lateral movement management is greater the smaller the occupant is. Light vehicles require interior restraint systems of higher performance than heavy vehicles do to achieve the same level of injury measures for a given side structure. Copyright © 2012 Taylor & Francis Group, LLC

Finite element comparison of human and Hybrid III responses in a frontal impact.

PubMed

Danelson, Kerry A; Golman, Adam J; Kemper, Andrew R; Gayzik, F Scott; Clay Gabler, H; Duma, Stefan M; Stitzel, Joel D

2015-12-01

The improvement of finite element (FE) Human Body Models (HBMs) has made them valuable tools for investigating restraint interactions compared to anthropomorphic test devices (ATDs). The objective of this study was to evaluate the effect of various combinations of safety restraint systems on the sensitivity of thoracic injury criteria using matched ATD and Human Body Model (HBM) simulations at two crash severities. A total of seven (7) variables were investigated: 3-point belt with two (2) load limits, frontal airbag, knee bolster airbag, a buckle pretensioner, and two (2) delta-v's - 40kph and 50kph. Twenty four (24) simulations were conducted for the Hybrid III ATD FE model and repeated with a validated HBM for 48 total simulations. Metrics tested in these conditions included sternum deflection, chest acceleration, chest excursion, Viscous Criteria (V*C) criteria, pelvis acceleration, pelvis excursion, and femur forces. Additionally, chest band deflection and rib strain distribution were measured in the HBM for additional restraint condition discrimination. The addition of a frontal airbag had the largest effect on the occupant chest metrics with an increase in chest compression and acceleration but a decrease in excursion. While the THUMS and Hybrid III occupants demonstrated the same trend in the chest compression measurements, there were conflicting results in the V*C, acceleration, and displacement metrics. Similarly, the knee bolster airbag had the largest effect on the pelvis with a decrease in acceleration and excursion. With a knee bolster airbag the simulated occupants gave conflicting results, the THUMS had a decrease in femur force and the ATD had an increase. Preferential use of dummies or HBM's is not debated; however, this study highlights the ability of HBM metrics to capture additional chest response metrics. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Health Belief Model as an Explanatory Framework in Communication Research: Exploring Parallel, Serial, and Moderated Mediation

PubMed Central

Jones, Christina L.; Jensen, Jakob D.; Scherr, Courtney L.; Brown, Natasha R.; Christy, Katheryn; Weaver, Jeremy

2015-01-01

The Health Belief Model (HBM) posits that messages will achieve optimal behavior change if they successfully target perceived barriers, benefits, self-efficacy, and threat. While the model seems to be an ideal explanatory framework for communication research, theoretical limitations have limited its use in the field. Notably, variable ordering is currently undefined in the HBM. Thus, it is unclear whether constructs mediate relationships comparably (parallel mediation), in sequence (serial mediation), or in tandem with a moderator (moderated mediation). To investigate variable ordering, adults (N = 1,377) completed a survey in the aftermath of an 8-month flu vaccine campaign grounded in the HBM. Exposure to the campaign was positively related to vaccination behavior. Statistical evaluation supported a model where the indirect effect of exposure on behavior through perceived barriers and threat was moderated by self-efficacy (moderated mediation). Perceived barriers and benefits also formed a serial mediation chain. The results indicate that variable ordering in the Health Belief Model may be complex, may help to explain conflicting results of the past, and may be a good focus for future research. PMID:25010519

Performances of the PIPER scalable child human body model in accident reconstruction

PubMed Central

Giordano, Chiara; Kleiven, Svein

2017-01-01

Human body models (HBMs) have the potential to provide significant insights into the pediatric response to impact. This study describes a scalable/posable approach to perform child accident reconstructions using the Position and Personalize Advanced Human Body Models for Injury Prediction (PIPER) scalable child HBM of different ages and in different positions obtained by the PIPER tool. Overall, the PIPER scalable child HBM managed reasonably well to predict the injury severity and location of the children involved in real-life crash scenarios documented in the medical records. The developed methodology and workflow is essential for future work to determine child injury tolerances based on the full Child Advanced Safety Project for European Roads (CASPER) accident reconstruction database. With the workflow presented in this study, the open-source PIPER scalable HBM combined with the PIPER tool is also foreseen to have implications for improved safety designs for a better protection of children in traffic accidents. PMID:29135997

Predictors of condom use behaviors based on the Health Belief Model (HBM) among female sex workers: a cross-sectional study in Hubei Province, China.

PubMed

Zhao, Jinzhu; Song, Fujian; Ren, Shuhua; Wang, Yan; Wang, Liang; Liu, Wei; Wan, Ying; Xu, Hong; Zhou, Tao; Hu, Tian; Bazzano, Lydia; Sun, Yi

2012-01-01

HIV infection related to commercial sexual contact is a serious public health issue in China. The objectives of the present study are to explore the predictors of condom use among female sex workers (FSWs) in China and examine the relationship between Health Belief Model (HBM) constructs. A cross-sectional study was conducted in two cities (Wuhan and Suizhou) in Hubei Province, China, between July 2009 and June 2010. A total of 427 FSWs were recruited through mediators from the 'low-tier' entertainment establishments. Data were obtained by self-administered questionnaires. Structural equation models were constructed to examine the association. We collected 363 valid questionnaires. Within the context of HBM, perceived severity of HIV mediated through perceived benefits of condom use had a weak effect on condom use (r=0.07). Perceived benefits and perceived barriers were proximate determinants of condom use (r=0.23 and r=-0.62, respectively). Self-efficacy had a direct effect on perceived severity, perceived benefits, and perceived barriers, which was indirectly associated with condom use behaviors (r=0.36). The HBM provides a useful framework for investigating predictors of condom use behaviors among FSWs. Future HIV prevention interventions should focus on increasing perceived benefits of condom use, reducing barriers to condoms use, and improving self-efficacy among FSWs.

[Monograph for N-Methyl-pyrrolidone (NMP) and human biomonitoring values for the metabolites 5-Hydroxy-NMP and 2-Hydroxy-N-methylsuccinimide].

PubMed

2015-10-01

1-Methyl-pyrrolidone (NMP) is used as a solvent in many technical applications. The general population may be exposed to NMP from the use as ingredient in paint and graffiti remover, indoors also from use in paints and carpeting. Because of developmental toxic effects, the use of NMP in consumer products in the EU is regulated. The developmental effects accompanied by weak maternally toxic effects in animal experiments are considered as the critical effects by the German HBM Commission. Based on these effects, HBM-I values of 10 mg/l urine for children and of 15 mg/l for adults, respectively, were derived for the metabolites 5-Hydroxy-NMP and 2-Hydroxy-N-methylsuccinimide. HBM-II-values were set to 30 mg/l urine for children and 50 mg/l for adults, respectively. Because of similar effects of the structural analogue 1-ethyl-2-pyrrolidone (NEP), the possible mixed exposure to both compounds has to be taken into account when evaluating the total burden.

[Health promotion. Instrument development for the application of the theory of planned behavior].

PubMed

Lee, Y O

1993-01-01

The purpose of this article is to describe operationalization of the Theory of Planned Behavior (TPB). The quest to understand determinants of health behaviors has intensified as evidence accumulates concerning the impact of personal behavior on health. The majority of theory-based research has used the Health Belief Model(HBM). The HBM components have had limited success in explaining health-related behaviors. There are several advantages of the TPB over the HBM. TPB is an expansion of the Theory of Reasoned Action(TRA) with the addition of the construct, perceived behavioral control. The revised model has been shown to yield greater explanatory power than the original TRA for goal-directed behaviors. The process of TPB instrument development was described, using example form the study of smoking cessation behavior in military smokers. It was followed by a discussion of reliability and validity issues in operationalizing the TPB. The TPB is a useful model for understanding and predicting health-related behaviors when carefully operationalized. The model holds promise in the development of prescriptive nursing approaches.

The health belief model and number of peers with internet addiction as inter-related factors of Internet addiction among secondary school students in Hong Kong.

PubMed

Wang, Yanhong; Wu, Anise M S; Lau, Joseph T F

2016-03-16

Students are vulnerable to Internet addiction (IA). Influences of cognitions based on the Health Belief Model (HBM) and perceived number of peers with IA (PNPIA) affecting students' IA, and mediating effects involved, have not been investigated. This cross-sectional study surveyed 9518 Hong Kong Chinese secondary school students in the school setting. In this self-reported study, the majority (82.6%) reported that they had peers with IA. Based on the Chinese Internet Addiction Scale (cut-off =63/64), the prevalence of IA was 16.0% (males: 17.6%; females: 14.0%). Among the non-IA cases, 7.6% (males: 8.7%; females: 6.3%) perceived a chance of developing IA in the next 12 months. Concurring with the HBM, adjusted logistic analysis showed that the Perceived Social Benefits of Internet Use Scale (males: Adjusted odds ratio (ORa) = 1.19; females: ORa = 1.23), Perceived Barriers for Reducing Internet Use Scale (males: ORa = 1.26; females: ORa = 1.36), and Perceived Self-efficacy for Reducing Internet Use Scale (males: ORa = 0.66; females: ORa = 0.56) were significantly associated with IA. Similarly, PNPIA was significantly associated with IA ('quite a number': males: ORa = 2.85; females: ORa = 4.35; 'a large number': males: ORa = 3.90; females: ORa = 9.09). Controlling for these three constructs, PNPIA remained significant but the strength of association diminished ('quite a number': males: multivariate odds ratio (ORm) = 2.07; females: ORm = 2.44; 'a large number': males: ORm = 2.39; females: ORm = 3.56). Hence, the association between PNPIA and IA was partially mediated (explained) by the three HBM constructs. Interventions preventing IA should change these constructs. In sum, prevalence of IA was relatively high and was associated with some HBM constructs and PNPIA, and PNPIA also partially mediated associations between HBM constructs and IA. Huge challenges are expected, as social relationships and an imbalance of cost-benefit for reducing Internet use are involved. Perceived susceptibility and perceived severity of IA were relatively low and the direction of their associations with IA did not concur with the HBM. Group cognitive-behavioral interventions involving peers with IA or peers recovered from IA are potentially useful to modify the HBM constructs and should be tested for efficacy.

Exposure to Theory-Driven Text Messages is Associated with HIV Risk Reduction Among Methamphetamine-Using Men Who have Sex with Men.

PubMed

Reback, Cathy J; Fletcher, Jesse B; Shoptaw, Steven; Mansergh, Gordon

2015-06-01

Fifty-two non-treatment-seeking methamphetamine-using men who have sex with men were enrolled in Project Tech Support, an open-label pilot study to evaluate whether exposure to theory-based [social support theory (SST), social cognitive theory (SCT), and health belief model (HBM)] text messages could promote reductions in HIV sexual risk behaviors and/or methamphetamine use. Multivariable analyses revealed that increased relative exposure to HBM or SCT (vs. SST) text messages was associated with significant reductions in the number of HIV serodiscordant unprotected (i.e., without a condom) anal sex partners, engagement in sex for money and/or drugs, and frequency of recent methamphetamine use; additionally, increased relative exposure to HBM (vs. SCT or SST) messages was uniquely associated with reductions in the overall number of non-primary anal sex partners (all p ≤ 0.05, two-tailed). Pilot data demonstrated that text messages based on the principles of HBM and SCT reduced sentinel HIV risk and drug use behaviors in active methamphetamine users.

Health Blief Model-based intervention to improve nutritional behavior among elderly women.

PubMed

Iranagh, Jamileh Amirzadeh; Rahman, Hejar Abdul; Motalebi, Seyedeh Ameneh

2016-06-01

Nutrition is a determinant factor of health in elderly people. Independent living in elderly people can be maintained or enhanced by improvement of nutritional behavior. Hence, the present study was conducted to determine the impact of Health Belief Model (HBM)-based intervention on the nutritional behavior of elderly women. Cluster-random sampling was used to assess the sample of this clinical trial study. The participants of this study attended a 12-week nutrition education program consisting of two (2) sessions per week. There was also a follow-up for another three (3) months. Smart PLS 3.5 and SPSS 19 were used for structural equation modeling, determination of model fitness, and hypotheses testing. The findings indicate that intervention had a significant effect on knowledge improvement as well as the behavior of elderly women. The model explained 5 to 70% of the variance in nutritional behavior. In addition, nutritional behavior was positively affected by the HBM constructs comprised of perceived susceptibility, self-efficacy, perceived benefits, and barriers after the intervention program. The results of this study show that HBM-based educational intervention has a significant effect in improving nutritional knowledge and behavior among elderly women.

Policy recommendations and cost implications for a more sustainable framework for European human biomonitoring surveys.

PubMed

Joas, Anke; Knudsen, Lisbeth E; Kolossa-Gehring, Marike; Sepai, Ovnair; Casteleyn, Ludwine; Schoeters, Greet; Angerer, Jürgen; Castaño, Argelia; Aerts, Dominique; Biot, Pierre; Horvat, Milena; Bloemen, Louis; Reis, M Fátima; Lupsa, Ioana-Rodica; Katsonouri, Andromachi; Cerna, Milena; Berglund, Marika; Crettaz, Pierre; Rudnai, Peter; Halzlova, Katarina; Mulcahy, Maurice; Gutleb, Arno C; Fischer, Marc E; Becher, Georg; Fréry, Nadine; Jensen, Genon; Van Vliet, Lisette; Koch, Holger M; Den Hond, Elly; Fiddicke, Ulrike; Esteban, Marta; Exley, Karen; Schwedler, Gerda; Seiwert, Margarete; Ligocka, Danuta; Hohenblum, Philipp; Kyrtopoulos, Soterios; Botsivali, Maria; DeFelip, Elena; Guillou, Claude; Reniero, Fabiano; Grazuleviciene, Regina; Veidebaum, Toomas; Mørck, Thit A; Nielsen, Jeanette K S; Jensen, Janne F; Rivas, Teresa C; Sanchez, Jinny; Koppen, Gudrun; Smolders, Roel; Kozepesy, Szilvia; Hadjipanayis, Adamos; Krskova, Andrea; Mannion, Rory; Jakubowski, Marek; Fucic, J Aleksandra; Pereira-Miguel, Jose; Gurzau, Anca E; Jajcaj, Michal; Mazej, Darja; Tratnik, Janja Snoj; Lehmann, Andrea; Larsson, Kristin; Dumez, Birgit; Joas, Reinhard

2015-08-01

The potential of Human Biomonitoring (HBM) in exposure characterisation and risk assessment is well established in the scientific HBM community and regulatory arena by many publications. The European Environment and Health Strategy as well as the Environment and Health Action Plan 2004-2010 of the European Commission recognised the value of HBM and the relevance and importance of coordination of HBM programmes in Europe. Based on existing and planned HBM projects and programmes of work and capabilities in Europe the Seventh Framework Programme (FP 7) funded COPHES (COnsortium to Perform Human Biomonitoring on a European Scale) to advance and improve comparability of HBM data across Europe. The pilot study protocol was tested in 17 European countries in the DEMOCOPHES feasibility study (DEMOnstration of a study to COordinate and Perform Human biomonitoring on a European Scale) cofunded (50%) under the LIFE+ programme of the European Commission. The potential of HBM in supporting and evaluating policy making (including e.g. REACH) and in awareness raising on environmental health, should significantly advance the process towards a fully operational, continuous, sustainable and scientifically based EU HBM programme. From a number of stakeholder activities during the past 10 years and the national engagement, a framework for sustainable HBM structure in Europe is recommended involving national institutions within environment, health and food as well as European institutions such as ECHA, EEA, and EFSA. An economic frame with shared cost implications for national and European institutions is suggested benefitting from the capacity building set up by COPHES/DEMOCOPHES. Copyright © 2014 Elsevier Inc. All rights reserved.

Perceived risks of HIV/AIDS and first sexual intercourse among youth in Cape Town, South Africa.

PubMed

Tenkorang, Eric Y; Rajulton, Fernando; Maticka-Tyndale, Eleanor

2009-04-01

The 'Health Belief Model' (HBM) identifies perception of HIV/AIDS risks, recognition of its seriousness, and knowledge about prevention as predictors of safer sexual activity. Using data from the Cape Area Panel Survey (CAPS) and hazard models, this study examines the impact of risk perception, considered the first step in HIV prevention, set within the context of the HBM and socio-economic, familial and school factors, on the timing of first sexual intercourse among youth aged 14-22 in Cape Town, South Africa. Of the HBM components, female youth who perceive their risk as 'very small' and males with higher knowledge, experience their sexual debut later than comparison groups, net of other influences. For both males and females socio-economic and familial factors also influence timing of sexual debut, confirming the need to consider the social embeddedness of this sexual behavior as well as the rational components of decision making when designing prevention programs.

Health belief model based evaluation of school health education programme for injury prevention among high school students in the community context.

PubMed

Cao, Zhi-Juan; Chen, Yue; Wang, Shu-Mei

2014-01-10

Although multifaceted community-based programmes have been widely developed, there remains a paucity of evaluation of the effectiveness of multifaceted injury prevention programmes implemented in different settings in the community context. This study was to provide information for the evaluation of community-based health education programmes of injury prevention among high school students. The pre-intervention survey was conducted in November 2009. Health belief model (HBM) based health education for injury prevention started in January 2010 and stopped in the end of 2011 among high school students in the community context in Shanghai, China. A post-intervention survey was conducted six weeks after the completion of intervention. Injury-related health belief indicators were captured by a short questionnaire before and after the intervention. Health belief scores were calculated and compared using the simple sum score (SSS) method and the confirmatory factor analysis weighted score (CFAWS) method, respectively. The average reliability coefficient for the questionnaire was 0.89. The factor structure of HBM was given and the data fit HBM in the confirmatory factor analysis (CFA) very well. The result of CFA showed that Perceived Benefits of Taking Action (BEN) and Perceived Seriousness (SER) had the greatest impact on the health belief, Perceived Susceptibility (SUS) and Cues to Action (CTA) were the second and third most important components of HBM respectively. Barriers to Taking Action (BAR) had no notable impact on HBM. The standardized path coefficient was only 0.35, with only a small impact on CTA. The health belief score was significantly higher after intervention (p < 0.001), which was similar in the CFAWS method and in the SSS method. However, the 95% confidential interval in the CFAWS method was narrower than that in the SSS method. The results of CFA provide further empirical support for the HBM in injury intervention. The CFAWS method can be used to calculate the health belief scores and evaluate the injury related intervention. The community-based school health education might improve injury-related health belief among high school students; however, this preliminary observation needs to be confirmed in further research.

Effect of educational intervention based on the Health Belief Model on promoting self-care behaviors of type-2 diabetes patients.

PubMed

Shabibi, Parisa; Zavareh, Mohammad Sadegh Abedzadeh; Sayehmiri, Kourosh; Qorbani, Mostafa; Safari, Omid; Rastegarimehr, Babak; Mansourian, Morteza

2017-12-01

Diabetes is a chronic disease in which patients require lifelong self-care behaviors. The present study offset to determine the effect of educational intervention based on the Health Belief Model (HBM) on promoting self-care behaviors of type 2 diabetes patients in Ilam, Iran 2014. A quasi-experimental research was conducted based on HBM in which 70 type 2 diabetic patients from Ilam, western Iran in 2014 were selected by multi-stage random sampling. A self-designed questionnaire was used, and pre-test was administered, subsequently, the educational intervention sessions were provided in the form of presentation, questions and answers, group discussion, and practical demonstrations in four sessions over a period of one month. Two months after the intervention, the post-tests were administered. The data were analyzed via SPSS 20 applying independent samples t-test, paired samples t-test, and univariate and multivariate regressions at a significance level of less than 0.05. The mean scores of susceptibility, severity, perceived benefits and barriers, self-efficacy, and self-care behaviors were at average and lower levels before the intervention; nonetheless, after the educational intervention, the mean score of each HBM construct and the self-care behaviors significantly increased (p<0.001). Health education through HBM promotes the self-care behaviors of patients with type 2 diabetes.

Factors influencing healthy eating habits among college students: an application of the health belief model.

PubMed

Deshpande, Sameer; Basil, Michael D; Basil, Debra Z

2009-01-01

Poor eating habits are an important public health issue that has large health and economic implications. Many food preferences are established early, but because people make more and more independent eating decisions as they move through adolescence, the transition to independent living during the university days is an important event. To study the phenomenon of food selection, the heath belief model was applied to predict the likelihood of healthy eating among university students. Structural equation modeling was used to investigate the validity of the health belief model (HBM) among 194 students, followed by gender-based analyses. The data strongly supported the HBM. Social change campaign implications are discussed.

Comparacion de modelos de Educacion Sexual en el conocimiento y cambio de actitudes en practicas sexuales por alumnos de nivel superior en la region de Caguas, Puerto Rico

NASA Astrophysics Data System (ADS)

Juan, Vallejo Ramos L.

In opposition to the Sexual Education Traditional Model (SETM) that is used in the state schools of Puerto Rico, the Health Beliefs Model (HBM) appears. It facilitates a curricular design that improves the ability of the students to respond to the group pressure by means of attitudes that stimulate sexual conducts of smaller risk of propagation of the Sexually Transmitted Diseases (STD). In addition, it provides activities to increase the self-esteem, the communication and the decision making. This investigation had the intention to compare the SETM and the HBM in the increase of knowledge and change of attitudes of high risk of propagation of the STD using a validated questionnaire (Agency of the United States for the International-USAID Development), named "Endesa 2007" and, adapted to Puerto Rico by the Dra.Marta Collazo to a sample of students between the 17 and 19 years of 2 state schools of San Lorenzo, as a pretest, and, selected by convenience. Then, a 10 hours training was administered to half of the students using the SETM to STD and condom use lessons. The other half of the students received additional lessons using the HBM. Finally, both groups took the questionnaire again as a posttest. The sample of students, in average, did not reach the knowledge and basic levels of attitudes towards the STD in the pretest. This reflected 2 possible implications on the SETM. In first place, that the way in which the STD is implemented as part of the Sexual Education curriculum is inefficient. Secondly, the possibility that the acquired information or attitudes does not have permanence. Culminated the questionnaire, the HBM increase the knowledge of the STD in 0.41 points (average) over the SETM. There was not a significant difference between both models, in attitudes, implying that both models are equally effective. The findings suggests that the HBM is more effective increasing the knowledge on the STD, but equally effective than the SETM in attitude change for the Puerto Rican youth.

Directional and sectional ride comfort estimation using an integrated human biomechanical-seat foam model

NASA Astrophysics Data System (ADS)

Mohajer, Navid; Abdi, Hamid; Nahavandi, Saeid; Nelson, Kyle

2017-09-01

In the methodology of objective measurement of ride comfort, application of a Human Biomechanical Model (HBM) is valuable for Whole Body Vibration (WBV) analysis. In this study, using a computational Multibody System (MBS) approach, development of a 3D passive HBM for a seated human is considered. For this purpose, the existing MBS-based HBMs of seated human are briefly reviewed first. The Equations of Motion (EoM) for the proposed model are then obtained and the simulation results are shown and compared with idealised ranges of experimental results suggested in the literature. The human-seat interaction is established using a nonlinear vibration model of foam with respect to the sectional behaviour of the seat foam. The developed system is then used for ride comfort estimation offered by a ride dynamic model. The effects of human weight, road class, and vehicle speed on the vibration of the human body segments in different directions are studied. It is shown that the there is a high correlation (more than 99.2%) between the vibration indices of the proposed HBM-foam model and the corresponding ISO 2631 WBV indices. In addition, relevant ISO 2631 indices that show a high correlation with the directional vibration of the head are identified.

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition.

PubMed

Jones, Matt; Love, Bradley C

2011-08-01

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.

Differential efficacy of human mesenchymal stem cells based on source of origin

PubMed Central

Collins, Erin; Gu, Fei; Qi, Maosong; Molano, Ivan; Ruiz, Phillip; Sun, Linyun; Gilkeson, Gary S.

2014-01-01

Mesenchymal stem cells (MSCs) are useful in tissue repair, but also possess immunomodulatory properties. Murine and uncontrolled human trials suggest efficacy of MSCs in treating lupus. Autologous cells are preferable, however, recent studies suggest that lupus derived MSCs lack efficacy in treating disease. Thus, the optimum derivation of MSCs for use in lupus is unknown. It is also unknown which in vitro assays of MSC function predict in vivo efficacy. The objectives for this study were to provide insight into the optimum source of MSCs and to identify in vitro assays that predict in vivo efficacy. We derived MSCs from four umbilical cords (UC), four healthy bone marrows (HBM) and four lupus bone marrows (LBM). In diseased MRL/lpr mice, MSCs from HBM and UC significantly decreased renal disease, while LBM-MSCs only delayed disease. Current in vitro assays did not differentiate efficacy of the different MSCs. Inhibition of B cell proliferation did differentiate based on efficacy. Our results suggest that autologous MSCs from lupus patients are not effective in treating disease. Furthermore, standard in vitro assays for MSC licensing are not predictive of in vivo efficacy, while inhibiting B cell proliferation appears to differentiate effective from ineffective MSCs. PMID:25274529

Rapid Osteogenic Enhancement of Stem Cells in Human Bone Marrow Using a Glycogen-Synthease-Kinase-3-Beta Inhibitor Improves Osteogenic Efficacy In Vitro and In Vivo.

PubMed

Clough, Bret H; Zeitouni, Suzanne; Krause, Ulf; Chaput, Christopher D; Cross, Lauren M; Gaharwar, Akhilesh K; Gregory, Carl A

2018-04-01

Non-union defects of bone are a major problem in orthopedics, especially for patients with a low healing capacity. Fixation devices and osteoconductive materials are used to provide a stable environment for osteogenesis and an osteogenic component such as autologous human bone marrow (hBM) is then used, but robust bone formation is contingent on the healing capacity of the patients. A safe and rapid procedure for improvement of the osteoanabolic properties of hBM is, therefore, sought after in the field of orthopedics, especially if it can be performed within the temporal limitations of the surgical procedure, with minimal manipulation, and at point-of-care. One way to achieve this goal is to stimulate canonical Wingless (cWnt) signaling in bone marrow-resident human mesenchymal stem cells (hMSCs), the presumptive precursors of osteoblasts in bone marrow. Herein, we report that the effects of cWnt stimulation can be achieved by transient (1-2 hours) exposure of osteoprogenitors to the GSK3β-inhibitor (2'Z,3'E)-6-bromoindirubin-3'-oxime (BIO) at a concentration of 800 nM. Very-rapid-exposure-to-BIO (VRE-BIO) on either hMSCs or whole hBM resulted in the long-term establishment of an osteogenic phenotype associated with accelerated alkaline phosphatase activity and enhanced transcription of the master regulator of osteogenesis, Runx2. When VRE-BIO treated hBM was tested in a rat spinal fusion model, VRE-BIO caused the formation of a denser, stiffer, fusion mass as compared with vehicle treated hBM. Collectively, these data indicate that the VRE-BIO procedure may represent a rapid, safe, and point-of-care strategy for the osteogenic enhancement of autologous hBM for use in clinical orthopedic procedures. Stem Cells Translational Medicine 2018;7:342-353. © 2018 The Authors Stem Cells Translational Medicine published by Wiley Periodicals, Inc. on behalf of AlphaMed Press.

Rapid Osteogenic Enhancement of Stem Cells in Human Bone Marrow Using a Glycogen‐Synthease‐Kinase‐3‐Beta Inhibitor Improves Osteogenic Efficacy In Vitro and In Vivo

PubMed Central

Clough, Bret H.; Zeitouni, Suzanne; Krause, Ulf; Chaput, Christopher D.; Cross, Lauren M.; Gaharwar, Akhilesh K.

2018-01-01

Abstract Non‐union defects of bone are a major problem in orthopedics, especially for patients with a low healing capacity. Fixation devices and osteoconductive materials are used to provide a stable environment for osteogenesis and an osteogenic component such as autologous human bone marrow (hBM) is then used, but robust bone formation is contingent on the healing capacity of the patients. A safe and rapid procedure for improvement of the osteoanabolic properties of hBM is, therefore, sought after in the field of orthopedics, especially if it can be performed within the temporal limitations of the surgical procedure, with minimal manipulation, and at point‐of‐care. One way to achieve this goal is to stimulate canonical Wingless (cWnt) signaling in bone marrow‐resident human mesenchymal stem cells (hMSCs), the presumptive precursors of osteoblasts in bone marrow. Herein, we report that the effects of cWnt stimulation can be achieved by transient (1–2 hours) exposure of osteoprogenitors to the GSK3β‐inhibitor (2′Z,3′E)‐6‐bromoindirubin‐3′‐oxime (BIO) at a concentration of 800 nM. Very‐rapid‐exposure‐to‐BIO (VRE‐BIO) on either hMSCs or whole hBM resulted in the long‐term establishment of an osteogenic phenotype associated with accelerated alkaline phosphatase activity and enhanced transcription of the master regulator of osteogenesis, Runx2. When VRE‐BIO treated hBM was tested in a rat spinal fusion model, VRE‐BIO caused the formation of a denser, stiffer, fusion mass as compared with vehicle treated hBM. Collectively, these data indicate that the VRE‐BIO procedure may represent a rapid, safe, and point‐of‐care strategy for the osteogenic enhancement of autologous hBM for use in clinical orthopedic procedures. stem cells translational medicine 2018;7:342–353 PMID:29405665

Doctor-Shopping Behavior among Patients with Eye Floaters

PubMed Central

Tseng, Gow-Lieng; Chen, Cheng-Yu

2015-01-01

Patients suffering from eye floaters often resort to consulting more than one ophthalmologist. The purpose of this study, using the Health Belief Model (HBM), was to identify the factors that influence doctor-shopping behavior among patients with eye floaters. In this cross-sectional survey, 175 outpatients who presented floaters symptoms were enrolled. Data from 143 patients (77 first time visitors and 66 doctor-shoppers) who completed the questionnaire were analyzed. Descriptive and logistic regression analyses were performed. We found that women and non-myopia patients were significantly related with frequent attendance and doctor switching. Though the HBM has performed well in a number of health behaviors studies, but most of the conceptual constructors of HBM did not show significant differences between the first time visitors and true doctor-shoppers in this study. Motivation was the only significant category affecting doctor-shopping behavior of patients with eye floaters. PMID:26184266

Doctor-Shopping Behavior among Patients with Eye Floaters.

PubMed

Tseng, Gow-Lieng; Chen, Cheng-Yu

2015-07-13

Patients suffering from eye floaters often resort to consulting more than one ophthalmologist. The purpose of this study, using the Health Belief Model (HBM), was to identify the factors that influence doctor-shopping behavior among patients with eye floaters. In this cross-sectional survey, 175 outpatients who presented floaters symptoms were enrolled. Data from 143 patients (77 first time visitors and 66 doctor-shoppers) who completed the questionnaire were analyzed. Descriptive and logistic regression analyses were performed. We found that women and non-myopia patients were significantly related with frequent attendance and doctor switching. Though the HBM has performed well in a number of health behaviors studies, but most of the conceptual constructors of HBM did not show significant differences between the first time visitors and true doctor-shoppers in this study. Motivation was the only significant category affecting doctor-shopping behavior of patients with eye floaters.

Application of the Health Belief Model to customers' use of menu labels in restaurants.

PubMed

Jeong, Jin-Yi; Ham, Sunny

2018-04-01

Some countries require the provision of menu labels on restaurant menus to fight the increasing prevalence of obesity and related chronic diseases. This study views customers' use of menu labels as a preventive health behavior and applies the Health Belief Model (HBM) with the aim of determining the health belief factors that influence customers' use of menu labels. A self-administered survey was distributed for data collection. Responses were collected from 335 restaurant customers who experienced menu labels in restaurants within three months prior to the survey. The results of a structural equation model showed that all the HBM variables (perceived threats, perceived benefits, and perceived barriers of using menu labels) positively affected the customers' use of menu labels. Perceived threats were influenced by cues to action and cues to action had an indirect influence on menu label use through perceived threats. In conclusion, health beliefs were good predictors of menu label use on restaurant menus. This study validated the application of the HBM to menu labeling in restaurants, and its findings could offer guidelines for the industry and government in developing strategies to expand the use of menu labels among the public. Copyright © 2017 Elsevier Ltd. All rights reserved.

Readability, Suitability and Health Content Assessment of Cancer Screening Announcements in Municipal Newspapers in Japan.

PubMed

Okuhara, Tsuyoshi; Ishikawa, Hirono; Okada, Hiroko; Kiuchi, Takahiro

2015-01-01

The objective of this study was to assess the readability, suitability, and health content of cancer screening information in municipal newspapers in Japan. Suitability Assessment of Materials (SAM) and the framework of Health Belief Model (HBM) were used for assessment of municipal newspapers that were published in central Tokyo (23 wards) from January to December 2013. The mean domain SAM scores of content, literacy demand, and layout/typography were considered superior. The SAM scores of interaction with readers, an indication of the models of desirable actions, and elaboration to enhance readers' self-efficacy were low. According to the HBM coding, messages of medical/clinical severity, of social severity, of social benefits, and of barriers of fear were scarce. The articles were generally well written and suitable. However, learning stimulation/motivation was scarce and the HBM constructs were not fully addressed. Articles can be improved to motivate readers to obtain cancer screening by increasing interaction with readers, introducing models of desirable actions and devices to raise readers' self-efficacy, and providing statements of perceived barriers of fear for pain and time constraints, perceived severity, and social benefits and losses.

A review of human biomonitoring in selected Southeast Asian countries.

PubMed

Barnett-Itzhaki, Zohar; Esteban López, Marta; Puttaswamy, Naveen; Berman, Tamar

2018-07-01

Rapid development and industrialization in Southeast (SE) Asia has led to environmental pollution, potentially exposing the general population to environmental contaminants. Human biomonitoring (HBM), measurement of chemical and/or their metabolites in human tissues and fluids, is an important tool for assessing cumulative exposure to complex mixtures of chemicals and for monitoring chemical exposures in the general population. While there are national HBM programs in several developed countries, there are no such national programs in most of the SE Asian countries. However, in recent years there has been progress in the field of HBM in many of the SE Asian countries. In this review, we present recent HBM studies in five selected SE Asian countries: Bangladesh, Indonesia, Malaysia, Myanmar and Thailand. While there is extensive HBM research in several SE Asian countries, such as Thailand, in other countries HBM studies are limited and focus on traditional environmental pollutants (such as lead, arsenic and mercury). Further development of this field in SE Asia would be benefited by establishment of laboratory capacity, improving quality control and assurance, collaboration with international experts and consortiums, and sharing of protocols and training both for pre-analytical and analytical phases. This review highlights the impressive progress in HBM research in selected SE Asian countries and provides recommendations for development of this field. Copyright © 2018 Elsevier Ltd. All rights reserved.

Intention of Mothers in Israel to Vaccinate their Sons against the Human Papilloma Virus.

PubMed

Ben Natan, Merav; Midlej, Kareem; Mitelman, Olga; Vafiliev, Katya

This study investigated the intention of mothers in Israel to vaccinate their sons against HPV, using the Health Belief Model (HBM) as a framework, while comparing between Arab and Jewish mothers. The study has a quantitative cross-sectional design. A convenience sample of 200 Jewish and Arab mothers of boys aged 5-18 completed a questionnaire based on the HBM. The research findings indicate that only 14% of the mothers, constituting mostly Arab mothers, vaccinated their sons against HPV. Moreover, mothers showed a moderate level of intention to vaccinate their sons. This level was similar among Arab and Jewish mothers. However, the health beliefs of Jewish and Arab mothers differed. The HBM was found to explain 68% of mothers' intention to vaccinate their sons against HPV, and the perceived benefits of the vaccine were the factor most affecting this intention. Although mothers' health beliefs concerning vaccinating their sons against HPV may vary between sectors, the HBM can be used to explain what motivates mothers to vaccinate their sons. The research findings can assist in designing a national project among mothers of boys aimed at raising HPV vaccination rates, in both the Jewish and the Arab sector. Copyright © 2017 Elsevier Inc. All rights reserved.

Prevalence of radiographic hip osteoarthritis is increased in high bone mass.

PubMed

Hardcastle, S A; Dieppe, P; Gregson, C L; Hunter, D; Thomas, G E R; Arden, N K; Spector, T D; Hart, D J; Laugharne, M J; Clague, G A; Edwards, M H; Dennison, E M; Cooper, C; Williams, M; Davey Smith, G; Tobias, J H

2014-08-01

Epidemiological studies have shown an association between increased bone mineral density (BMD) and osteoarthritis (OA), but whether this represents cause or effect remains unclear. In this study, we used a novel approach to investigate this question, determining whether individuals with High Bone Mass (HBM) have a higher prevalence of radiographic hip OA compared with controls. HBM cases came from the UK-based HBM study: HBM was defined by BMD Z-score. Unaffected relatives of index cases were recruited as family controls. Age-stratified random sampling was used to select further population controls from the Chingford and Hertfordshire cohort studies. Pelvic radiographs were pooled and assessed by a single observer blinded to case-control status. Analyses used logistic regression, adjusted for age, gender and body mass index (BMI). 530 HBM hips in 272 cases (mean age 62.9 years, 74% female) and 1702 control hips in 863 controls (mean age 64.8 years, 84% female) were analysed. The prevalence of radiographic OA, defined as Croft score ≥3, was higher in cases compared with controls (20.0% vs 13.6%), with adjusted odds ratio (OR) [95% CI] 1.52 [1.09, 2.11], P = 0.013. Osteophytes (OR 2.12 [1.61, 2.79], P < 0.001) and subchondral sclerosis (OR 2.78 [1.49, 5.18], P = 0.001) were more prevalent in cases. However, no difference in the prevalence of joint space narrowing (JSN) was seen (OR 0.97 [0.72, 1.33], P = 0.869). An increased prevalence of radiographic hip OA and osteophytosis was observed in HBM cases compared with controls, in keeping with a positive association between HBM and OA and suggesting that OA in HBM has a hypertrophic phenotype. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Bayesian Regression with Network Prior: Optimal Bayesian Filtering Perspective

PubMed Central

Qian, Xiaoning; Dougherty, Edward R.

2017-01-01

The recently introduced intrinsically Bayesian robust filter (IBRF) provides fully optimal filtering relative to a prior distribution over an uncertainty class ofjoint random process models, whereas formerly the theory was limited to model-constrained Bayesian robust filters, for which optimization was limited to the filters that are optimal for models in the uncertainty class. This paper extends the IBRF theory to the situation where there are both a prior on the uncertainty class and sample data. The result is optimal Bayesian filtering (OBF), where optimality is relative to the posterior distribution derived from the prior and the data. The IBRF theories for effective characteristics and canonical expansions extend to the OBF setting. A salient focus of the present work is to demonstrate the advantages of Bayesian regression within the OBF setting over the classical Bayesian approach in the context otlinear Gaussian models. PMID:28824268

Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

NASA Astrophysics Data System (ADS)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

Bayesian model for matching the radiometric measurements of aerospace and field ocean color sensors.

PubMed

Salama, Mhd Suhyb; Su, Zhongbo

2010-01-01

A Bayesian model is developed to match aerospace ocean color observation to field measurements and derive the spatial variability of match-up sites. The performance of the model is tested against populations of synthesized spectra and full and reduced resolutions of MERIS data. The model derived the scale difference between synthesized satellite pixel and point measurements with R(2) > 0.88 and relative error < 21% in the spectral range from 400 nm to 695 nm. The sub-pixel variabilities of reduced resolution MERIS image are derived with less than 12% of relative errors in heterogeneous region. The method is generic and applicable to different sensors.

Factors That Influence Israeli Muslim Arab Parents' Intention to Vaccinate Their Children Against Influenza.

PubMed

Ben Natan, Merav; Kabha, Samih; Yehia, Mamon; Hamza, Omar

2016-01-01

The purpose of the current study was to explore factors related to the intention of parents from the Muslim Arab ethnic minority in Israel to vaccinate their children against influenza, using the Health Belief Model (HBM). This study is a cross sectional quantitative study. A convenience sample of 200 parents of children aged 12 and younger completed a questionnaire based on the HBM. Perceived susceptibility, severity, benefits, and barriers predicted 88% of parents' intention to vaccinate their children. Parents who vaccinated their children in the past year were younger and had fewer children. Community nurses and physicians were identified as important cues to action. The HBM components predicted a high percentage of parents' intention to vaccinate their children Interventions to raise vaccination coverage rates among children belonging to an ethnic minority of Israeli Muslim Arabs should begin on the micro level of the parent-health care professional encounter. Copyright © 2016 Elsevier Inc. All rights reserved.

A Thematic Analysis of Health Care Workers' Adoption of Mindfulness Practices.

PubMed

Valley, Morgan; Stallones, Lorann

2018-05-01

Mindfulness training, which teaches individuals to bring awareness and acceptance to the present moment, has been effective in improving the well-being of health care workers. Limited research examines the adoption of mindfulness practices using health behavior theories. The current study sought to conceptualize hospital health care workers' experiences in adopting mindfulness practices using the Health Belief Model (HBM), a theoretical framework used by health promotion practitioners to design and implement health behavior change interventions. Hospital health care workers in Colorado participated in an 8-week Mindfulness-Based Stress Reduction (MBSR) course. Participants ( n = 19) answered open-ended questions about their experiences adopting mindfulness practices. A theory-driven thematic analysis approach was used to analyze data with key constructs of the HBM acting as the framework for the analysis. Results showed that HBM constructs, including internal cues to action, perceived benefits and barriers, and self-efficacy, helped portray the participants' experiences and challenges in adopting and adhering to the mindfulness practices taught in the MBSR course.

The role of anticipated regret and health beliefs in HPV vaccination intentions among young adults.

PubMed

Christy, Shannon M; Winger, Joseph G; Raffanello, Elizabeth W; Halpern, Leslie F; Danoff-Burg, Sharon; Mosher, Catherine E

2016-06-01

Although cognitions have predicted young adults' human papillomavirus (HPV) vaccine decision-making, emotion-based theories of healthcare decision-making suggest that anticipatory emotions may be more predictive. This study examined whether anticipated regret was associated with young adults' intentions to receive the HPV vaccine above and beyond the effects of commonly studied cognitions. Unvaccinated undergraduates (N = 233) completed a survey assessing Health Belief Model (HBM) variables (i.e., perceived severity of HPV-related diseases, perceived risk of developing these diseases, and perceived benefits of HPV vaccination), anticipatory emotions (i.e., anticipated regret if one were unvaccinated and later developed genital warts or HPV-related cancer), and HPV vaccine intentions. Anticipated regret was associated with HPV vaccine intentions above and beyond the effects of HBM variables among men. Among women, neither anticipated regret nor HBM variables showed consistent associations with HPV vaccine intentions. Findings suggest that anticipatory emotions should be considered when designing interventions to increase HPV vaccination among college men.

The role of anticipated regret and health beliefs in HPV vaccination intentions among young adults

PubMed Central

Christy, Shannon M.; Winger, Joseph G.; Raffanello, Elizabeth W.; Halpern, Leslie F.; Danoff-Burg, Sharon; Mosher, Catherine E.

2016-01-01

Although cognitions have predicted young adults’ human papillomavirus (HPV) vaccine decision-making, emotion-based theories of healthcare decision-making suggest that anticipatory emotions may be more predictive. This study examined whether anticipated regret was associated with young adults’ intentions to receive the HPV vaccine above and beyond the effects of commonly studied cognitions. Unvaccinated undergraduates (N = 233) completed a survey assessing Health Belief Model (HBM) variables (i.e., perceived severity of HPV-related diseases, perceived risk of developing these diseases, and perceived benefits of HPV vaccination), anticipatory emotions (i.e., anticipated regret if one were unvaccinated and later developed genital warts or HPV-related cancer), and HPV vaccine intentions. Anticipated regret was associated with HPV vaccine intentions above and beyond the effects of HBM variables among men. Among women, neither anticipated regret nor HBM variables showed consistent associations with HPV vaccine intentions. Findings suggest that anticipatory emotions should be considered when designing interventions to increase HPV vaccination among college men. PMID:26782668

Familial congenital cyanosis caused by Hb-MYantai(α-76 GAC → TAC, Asp → Tyr)

PubMed Central

2010-01-01

Methemoglobin (Hb-M) is a rare hemoglobinopathy in China. We hereby report on a family living in Yantai, East China, with congenital cyanosis due to Hb-M mutation. The proband, a 65-year-old female, presented 63% oxygen saturation. Both Hb-M concentration and arterial oxygen saturation remained unchanged, even following intravenous treatment with methylene blue. There was also no change in blood-color (chocolate-brown) after adding 0.1% KCN. A fast-moving band (Hb-X) in hemolysates was found by cellulose acetate electrophoresis, the Hb-X/Hb-A ratio exceeding 10%. GT transition at 131nt of exon 2, although present in one of the α2 -globin alleles, was not found in α1 -globin alleles as a whole. This mutation leads to the aspartic acid to tyrosine substitution (Asp76Tyr). In this family, the novel mutation in the α2 -globin gene resulted in a rare form of congenital cyanosis due to Hb-M. This hemoglobin was named Hb-M Yantai . PMID:21637412

Factors affecting nurses' decision to get the flu vaccine.

PubMed

Shahrabani, Shosh; Benzion, Uri; Yom Din, Gregory

2009-05-01

The objective of this study was to identify factors that influence the decision whether or not to get the influenza (flu) vaccine among nurses in Israel by using the health belief model (HBM). A questionnaire distributed among 299 nurses in Israel in winter 2005/2006 included (1) socio-demographic information; (2) variables based on the HBM, including susceptibility, seriousness, benefits, barriers and cues to action; and (3) knowledge about influenza and the vaccine, and health motivation. A probit model was used to analyze the data. In Israel, the significant HBM categories affecting nurses' decision to get a flu shot are the perceived benefits from vaccination and cues to action. In addition, nurses who are vaccinated have higher levels of (1) knowledge regarding the vaccine and influenza, (2) perceived seriousness of the illness, (3) perceived susceptibility, and (4) health motivation than do those who do not get the vaccine. Immunization of healthcare workers may reduce the risk of flu outbreaks in all types of healthcare facilities and reduce morbidity and mortality among high-risk patients. In order to increase vaccination rates among nurses, efforts should be made to educate them regarding the benefits of vaccination and the potential health consequences of influenza for their patients, and themselves.

A Health Belief Model-Social Learning Theory approach to adolescents' fertility control: findings from a controlled field trial.

PubMed

Eisen, M; Zellman, G L; McAlister, A L

1992-01-01

We evaluated an 8- to 12-hour Health Belief Model-Social Learning Theory (HBM-SLT)-based sex education program against several community- and school-based interventions in a controlled field experiment. Data on sexual and contraceptive behavior were collected from 1,444 adolescents unselected for gender, race/ethnicity, or virginity status in a pretest-posttest design. Over 60% completed the one-year follow-up. Multivariate analyses were conducted separately for each preintervention virginity status by gender grouping. The results revealed differential program impacts. First, for preintervention virgins, there were no gender or intervention differences in abstinence maintenance over the follow-up year. Second, female preintervention Comparison program virgins used effective contraceptive methods more consistently than those who attended the HBM-SLT program (p less than 0.01); among males, the intervention programs were equally effective. Third, both interventions significantly increased contraceptive efficiency for teenagers who were sexually active before attending the programs. For males, the HBM-SLT program led to significantly greater follow-up contraceptive efficiency than the Comparison program with preintervention contraceptive efficiency controlled (p less than 0.05); for females, the programs produced equivalent improvement. Implications for program planning and evaluation are discussed.

Societal and ethical issues in human biomonitoring--a view from science studies.

PubMed

Bauer, Susanne

2008-06-05

Human biomonitoring (HBM) has rapidly gained importance. In some epidemiological studies, the measurement and use of biomarkers of exposure, susceptibility and disease have replaced traditional environmental indicators. While in HBM, ethical issues have mostly been addressed in terms of informed consent and confidentiality, this paper maps out a larger array of societal issues from an epistemological perspective, i.e. bringing into focus the conditions of how and what is known in environmental health science. In order to analyse the effects of HBM and the shift towards biomarker research in the assessment of environmental pollution in a broader societal context, selected analytical frameworks of science studies are introduced. To develop the epistemological perspective, concepts from "biomedical platform sociology" and the notion of "epistemic cultures" and "thought styles" are applied to the research infrastructures of HBM. Further, concepts of "biocitizenship" and "civic epistemologies" are drawn upon as analytical tools to discuss the visions and promises of HBM as well as related ethical problematisations. In human biomonitoring, two different epistemological cultures meet; these are environmental science with for instance pollution surveys and toxicological assessments on the one hand, and analytical epidemiology investigating the association between exposure and disease in probabilistic risk estimation on the other hand. The surveillance of exposure and dose via biomarkers as envisioned in HBM is shifting the site of exposure monitoring to the human body. Establishing an HBM platform faces not only the need to consider individual decision autonomy as an ethics issue, but also larger epistemological and societal questions, such as the mode of evidence demanded in science, policy and regulation. The shift of exposure monitoring towards the biosurveillance of human populations involves fundamental changes in the ways environment, health and disease are conceptualised; this may lead to an individualisation of responsibilities for health risks and preventive action. Attention to the conditions of scientific knowledge generation and to their broader societal context is critical in order to make HBM contribute to environmental justice.

K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution

DOE PAGES

DeChant, Lawrence; Ray, Jaideep; Lefantzi, Sophia; ...

2017-06-09

The k-ε turbulence model has been described as perhaps “the most widely used complete turbulence model.” This family of heuristic Reynolds Averaged Navier-Stokes (RANS) turbulence closures is supported by a suite of model parameters that have been estimated by demanding the satisfaction of well-established canonical flows such as homogeneous shear flow, log-law behavior, etc. While this procedure does yield a set of so-called nominal parameters, it is abundantly clear that they do not provide a universally satisfactory turbulence model that is capable of simulating complex flows. Recent work on the Bayesian calibration of the k-ε model using jet-in-crossflow wind tunnelmore » data has yielded parameter estimates that are far more predictive than nominal parameter values. In this paper, we develop a self-similar asymptotic solution for axisymmetric jet-in-crossflow interactions and derive analytical estimates of the parameters that were inferred using Bayesian calibration. The self-similar method utilizes a near field approach to estimate the turbulence model parameters while retaining the classical far-field scaling to model flow field quantities. Our parameter values are seen to be far more predictive than the nominal values, as checked using RANS simulations and experimental measurements. They are also closer to the Bayesian estimates than the nominal parameters. A traditional simplified jet trajectory model is explicitly related to the turbulence model parameters and is shown to yield good agreement with measurement when utilizing the analytical derived turbulence model coefficients. Finally, the close agreement between the turbulence model coefficients obtained via Bayesian calibration and the analytically estimated coefficients derived in this paper is consistent with the contention that the Bayesian calibration approach is firmly rooted in the underlying physical description.« less

Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis.

PubMed

Xie, Yuanchang; Lord, Dominique; Zhang, Yunlong

2007-09-01

Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM) and hierarchical Bayes models (HBM) have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to "over-fitting" the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models was estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the negative binomial (NB) regression models. The results of this study show that in general both types of neural network models perform better than the NB regression model in terms of data prediction. Although the BPNN model can occasionally provide better or approximately equivalent prediction performance compared to the BNN model, in most cases its prediction performance is worse than the BNN model. In addition, the data fitting performance of the BPNN model is consistently worse than the BNN model, which suggests that the BNN model has better generalization abilities than the BPNN model and can effectively alleviate the over-fitting problem without significantly compromising the nonlinear approximation ability. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.

The Effect of Health Education Program Based on Health Belief Model on Oral Health Behaviors in Pregnant Women of Fasa City, Fars Province, South of Iran.

PubMed

Jeihooni, Ali Khani; Jamshidi, Hassan; Kashfi, Seyyed Mansour; Avand, Abolghasem; Khiyali, Zahra

2017-01-01

Pregnant women are at risk of dental caries and periodontal disease. The purpose of this study was to assess the effectiveness of health education program based on health belief model (HBM) on oral and dental hygiene behaviors in pregnant women in Fasa city. This is a clinical trial study carried out on 110 pregnant women selected using random sampling method from health centers in Fasa city in 2016 (55 patients in the experimental group and 55 individuals in control group). Data collection with questionnaire was based on construct HBM, as well as their performance about oral health. At first, two groups completed the questionnaires. And then, the intervention was conducted for the experimental group based on HBM. Four months after intervention, two groups completed the questionnaires twice. To analyze the collected data, the researchers used SPSS version 22 and descriptive and analytical statistics tests such as independent t -test and Chi-square and Mann-Whitney test. The age of the pregnant mothers was 28.25 ± 3.02 years in the experimental group and 27.8 ± 4.20 years in the control group. Compared to the control group, the experimental group showed a significant increase in their knowledge, perceived susceptibility, perceived severity, perceived benefits, self-efficacy, cues to action, and performance and decrease in perceived barriers 4 months after the intervention. Applying the HBM is very effective for developing an educational program for oral health in pregnant women. Moreover, in the implementation of these programs, control, monitoring, and follow-up educational are recommended.

Efficacy of HBM-Based Dietary Education Intervention on Knowledge, Attitude, and Behavior in Medical Students.

PubMed

Tavakoli, Hamid Reza; Dini-Talatappeh, Hossein; Rahmati-Najarkolaei, Fatemeh; Gholami Fesharaki, Mohammad

2016-11-01

Using various models of behavior change, a number of studies in the area of nutrition education have confirmed that nutrition habits and behaviors can be improved. This study sought to determine the effects of education on patterns of dietary consumption among medical students at the military university of Tehran, with a view to correcting those patterns. In this quasi-experimental study, 242 medical students from the Military University of Tehran were chosen by convenience sampling and then divided into control (n = 107) and intervention groups (n = 135) by block randomization. The self-administered questionnaire involving six categories of item (knowledge, perceived benefits, perceived barriers, perceived threats, self-efficacy and behavior) has been validated (Cronbach alpha > 0.7 for each). Following the educational intervention, the mean score of knowledge, health belief model (HBM) structure, and behavior of students in relation to healthy patterns of food intake increased significantly (P < 0.05). The mean pre-intervention knowledge score was 6.76 (1.452), referring to threats to HBM constructs including perceived threat 2.93 (1.147), perceived benefits 7.28 (1.07), perceived barriers 5.44 (1.831), self- efficacy 4.28 (1.479), and behavior 8.84 (2.527). The post-intervention scores all improved as follows: knowledge 8.3 (1.503), perceived threats 3.29 (1.196), perceived benefits 7.71 (0.762), perceived barriers 5.9 (1.719), self- efficacy 4.6 (1.472), and behavior 9.45 (2.324). This difference in mean scores for knowledge, health belief structures and employee behavior before and after educational intervention was significant (P ≤ 0.05). The significant improvement in the experimental group's mean knowledge, HBM structures , and behavior scores indicates the positive effect of the intervention.

Application of the health belief model and social cognitive theory for osteoporosis preventive nutritional behaviors in a sample of Iranian women

PubMed Central

Jeihooni, Ali Khani; Hidarnia, Alireza; Kaveh, Mohammad Hossein; Hajizadeh, Ebrahim; Askari, Alireza

2016-01-01

Background: Osteoporosis is the most common metabolic bone disease. The purpose of this study is to investigate the health belief model (HBM) and social cognitive theory (SCT) for osteoporosis preventive nutritional behaviors in women. Materials and Methods: In this quasi-experimental study, 120 patients who were women and registered under the health centers in Fasa City, Fars Province, Iran were selected. A questionnaire consisting of HBM constructs and the constructs of self-regulation and social support from SCT was used to measure nutrition performance. Bone mineral density was recorded at the lumbar spine and femur. The intervention for the experimental group included 10 educational sessions of 55-60 min of speech, group discussion, questions and answers, as well as posters and educational pamphlets, film screenings, and PowerPoint displays. Data were analyzed using SPSS 19 via Chi-square test, independent t-test, and repeated measures analysis of variance (ANOVA) at a significance level of 0.05. Results: After intervention, the experimental group showed a significant increase in the HBM constructs, self-regulation, social support, and nutrition performance, compared to the control group. Six months after the intervention, the value of lumbar spine bone mineral density (BMD) T-score increased to 0.127 in the experimental group, while it reduced to −0.043 in the control group. The value of the hip BMD T-score increased to 0.125 in the intervention group, but it decreased to −0.028 in the control group. Conclusions: This study showed the effectiveness of HBM and constructs of self-regulation and social support on adoption of nutrition behaviors and increase in the bone density to prevent osteoporosis. PMID:27095985

Bayesian Model for Matching the Radiometric Measurements of Aerospace and Field Ocean Color Sensors

PubMed Central

Salama, Mhd. Suhyb; Su, Zhongbo

2010-01-01

A Bayesian model is developed to match aerospace ocean color observation to field measurements and derive the spatial variability of match-up sites. The performance of the model is tested against populations of synthesized spectra and full and reduced resolutions of MERIS data. The model derived the scale difference between synthesized satellite pixel and point measurements with R2 > 0.88 and relative error < 21% in the spectral range from 400 nm to 695 nm. The sub-pixel variabilities of reduced resolution MERIS image are derived with less than 12% of relative errors in heterogeneous region. The method is generic and applicable to different sensors. PMID:22163615

New human biomonitoring methods for chemicals of concern-the German approach to enhance relevance.

PubMed

Kolossa-Gehring, Marike; Fiddicke, Ulrike; Leng, Gabriele; Angerer, Jürgen; Wolz, Birgit

2017-03-01

In Germany strong efforts have been made within the last years to develop new methods for human biomonitoring (HBM). The German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) and the German Chemical Industry Association e. V. (VCI) cooperate since 2010 to increase the knowledge on the internal exposure of the general population to chemicals. The projects aim is to promote human biomonitoring by developing new analytical methods Key partner of the cooperation is the German Environment Agency (UBA) which has been entrusted with the scientific coordination. Another key partner is the "HBM Expert Panel" which each year puts together a list of chemicals of interest to the project from which the Steering Committee of the project choses up to five substances for which method development will be started. Emphasis is placed on substances with either a potential health relevance or on substances to which the general population is potentially exposed to a considerable extent. The HBM Expert Panel also advises on method development. Once a method is developed, it is usually first applied to about 40 non-occupationally exposed individuals. A next step is applying the methods to different samples. Either, if the time trend is of major interest, to samples from the German Environmental Specimen Bank, or, in case exposure sources and distribution of exposure levels in the general population are the focus, the new methods are applied to samples from children and adolescents from the population representative 5th German Environmental Survey (GerES V). Results are expected in late 2018. This article describes the challenges faced during method development and solutions found. An overview presents the 34 selected substances, the 14 methods developed and the 7 HBM-I values derived in the period from 2010 to mid 2016. Copyright © 2016 The Authors. Published by Elsevier GmbH.. All rights reserved.

Incorporation of diet information derived from Bayesian stable isotope mixing models into mass-balanced marine ecosystem models: A case study from the Marennes-Oleron Estuary, France

EPA Science Inventory

We investigated the use of output from Bayesian stable isotope mixing models as constraints for a linear inverse food web model of a temperate intertidal seagrass system in the Marennes-Oléron Bay, France. Linear inverse modeling (LIM) is a technique that estimates a complete net...

Mucopolysaccharidosis enzyme production by bone marrow and dental pulp derived human mesenchymal stem cells.

PubMed

Jackson, Matilda; Derrick Roberts, Ainslie; Martin, Ellenore; Rout-Pitt, Nathan; Gronthos, Stan; Byers, Sharon

2015-04-01

Mucopolysaccharidoses (MPS) are inherited metabolic disorders that arise from a complete loss or a reduction in one of eleven specific lysosomal enzymes. MPS children display pathology in multiple cell types leading to tissue and organ failure and early death. Mesenchymal stem cells (MSCs) give rise to many of the cell types affected in MPS, including those that are refractory to current treatment protocols such as hematopoietic stem cell (HSC) based therapy. In this study we compared multiple MPS enzyme production by bone marrow derived (hBM) and dental pulp derived (hDP) MSCs to enzyme production by HSCs. hBM MSCs produce significantly higher levels of MPS I, II, IIIA, IVA, VI and VII enzyme than HSCs, while hDP MSCs produce significantly higher levels of MPS I, IIIA, IVA, VI and VII enzymes. Higher transfection efficiency was observed in MSCs (89%) compared to HSCs (23%) using a lentiviral vector. Over-expression of four different lysosomal enzymes resulted in up to 9303-fold and up to 5559-fold greater levels in MSC cell layer and media respectively. Stable, persistent transduction of MSCs and sustained over-expression of MPS VII enzyme was observed in vitro. Transduction of MSCs did not affect the ability of the cells to differentiate down osteogenic, adipogenic or chondrogenic lineages, but did partially delay differentiation down the non-mesodermal neurogenic lineage. Copyright © 2015 Elsevier Inc. All rights reserved.

Societal and ethical issues in human biomonitoring – a view from science studies

PubMed Central

Bauer, Susanne

2008-01-01

Background Human biomonitoring (HBM) has rapidly gained importance. In some epidemiological studies, the measurement and use of biomarkers of exposure, susceptibility and disease have replaced traditional environmental indicators. While in HBM, ethical issues have mostly been addressed in terms of informed consent and confidentiality, this paper maps out a larger array of societal issues from an epistemological perspective, i.e. bringing into focus the conditions of how and what is known in environmental health science. Methods In order to analyse the effects of HBM and the shift towards biomarker research in the assessment of environmental pollution in a broader societal context, selected analytical frameworks of science studies are introduced. To develop the epistemological perspective, concepts from "biomedical platform sociology" and the notion of "epistemic cultures" and "thought styles" are applied to the research infrastructures of HBM. Further, concepts of "biocitizenship" and "civic epistemologies" are drawn upon as analytical tools to discuss the visions and promises of HBM as well as related ethical problematisations. Results In human biomonitoring, two different epistemological cultures meet; these are environmental science with for instance pollution surveys and toxicological assessments on the one hand, and analytical epidemiology investigating the association between exposure and disease in probabilistic risk estimation on the other hand. The surveillance of exposure and dose via biomarkers as envisioned in HBM is shifting the site of exposure monitoring to the human body. Establishing an HBM platform faces not only the need to consider individual decision autonomy as an ethics issue, but also larger epistemological and societal questions, such as the mode of evidence demanded in science, policy and regulation. Conclusion The shift of exposure monitoring towards the biosurveillance of human populations involves fundamental changes in the ways environment, health and disease are conceptualised; this may lead to an individualisation of responsibilities for health risks and preventive action. Attention to the conditions of scientific knowledge generation and to their broader societal context is critical in order to make HBM contribute to environmental justice. PMID:18541064

Community knowledge, health beliefs, practices and experiences related to dengue fever and its association with IgG seropositivity.

PubMed

Wong, Li Ping; AbuBakar, Sazaly; Chinna, Karuthan

2014-05-01

Demographic, economic and behavioural factors are central features underpinning the successful management and biological control of dengue. This study aimed to examine these factors and their association with the seroprevalence of this disease. We conducted a cross-sectional telephone survey of households in a 3 km radius of the schools where we had conducted serological tests on the student population in a previous study. Households were surveyed about their socio-demographics, knowledge, practices, and Health Belief Model (HBM) constructs. The results were then associated with the prevalence rate of dengue in the community, as marked by IgG seropositivity of the students who attended school there. A total of 1,400 complete responses were obtained. The community's IgG seropositivity was significantly positively associated with high household monthly income, high-rise residential building type, high surrounding vegetation density, rural locality, high perceived severity and susceptibility, perceived barriers to prevention, knowing that a neighbour has dengue, frequent fogging and a higher level of knowledge about dengue. In the multivariate analyses, three major correlates of the presence of IgG seropositivity in the community: (1) high-rise residential apartment house type or condominium buildings; (2) the main construct of the HBM, perceived severity and susceptibility; and (3) the additional constructs of the HBM, lack of preventive measures from the community level and having a neighbour with dengue as a cue to action. Weak correlations were found between self-practices to prevent dengue and the level of dengue seropositivity in the community, and between HBM constructs and knowledge (r = 0.09). The residential environment factor and the constructs of the HBM are useful and important elements in developing interventions to prevent and control dengue. The study also sheds light on the importance of the need for approaches that ensure the translation of knowledge into practice.

Organizational Twitter Use: Content Analysis of Tweets during Breast Cancer Awareness Month.

PubMed

Diddi, Pratiti; Lundy, Lisa K

2017-03-01

On an average, at least one out of eight women are at risk of falling prey to breast cancer during their lifespan. Amongst varied initiatives to spread awareness about breast cancer, the most well-known campaign is Breast Cancer Awareness Month. This article explored, through content analysis, how four different health-related organizations-Susan G. Komen, U.S. News Health, Woman's Hospital, and Breast Cancer Social Media-used their Twitter accounts to talk about varied aspects of breast cancer during the month of October, which is observed as Breast Cancer Awareness Month. All the tweets by these organizations were analyzed for the presence or absence of the theoretical parameters of the Health Belief Model (HBM). A content analysis of 2916 tweets based on the HBM revealed that the content posted by these organizations reflected the use of varied theoretical constructs of the framework. Overall, the study demonstrated that while different organizations shared valuable breast cancer-related content on Twitter, each used the social media platform in a different fashion, evident through focus on different types of HBM constructs while publishing breast cancer-related tweets.

Cariogenicity and acidogenicity of human milk, plain and sweetened bovine milk: an in vitro study.

PubMed

Prabhakar, A R; Kurthukoti, Ameet J; Gupta, Pranjali

2010-01-01

The objective of the present study was to determine the acidogenicity and cariogenicity of human breast milk and plain and sweetened packaged bovine milk. First all milk specimens were inoculated with a cariogenic strain of Streptococcus mutans (SM). The culture pH and number of colony forming units (cfus) was assessed. Second, the buffer capacity of all milk specimens was evaluated by mixing with acid. Finally, enamel windows were created on extracted primary maxillary incisors and colonized with SM. Enamel demineralization and caries progression were assessed visually, histologically, and radiographically at the end of twelve weeks. Plain and sweetened packaged bovine milk (BM) supported greater bacterial growth and caused more fermentation than human breast milk (HBM). The buffer capacity values for plain and sweetened bovine milk were highest; HBM, however had poor buffering capacity. The progression of the carious lesions into the dentin was most severe for the sweetened bovine milk. HBM and plain bovine milk are relatively cariogenic in an in vitro caries model in the absence of saliva. However, supplementation with sugar exponentially enhances the cariogenic potential of the natural milk.

From Evidence-based Medicine to Human-based Medicine in Psychosomatics.

PubMed

Musalek, Michael

2016-08-23

Human-based medicine (HbM), a form of psychiatry that focuses not only on fragments and constructs but on the whole person, no longer finds its theoretical basis in the positivism of the modern era, but rather owes its central maxims to the post-modernist ideal that ultimate truths or objectivity in identifying the final cause of illness remain hidden from us for theoretical reasons alone. Evidence-based medicine (EbM) and HbM are thus not mutually exclusive opposites; rather, despite superficial differences in methods of diagnosis and treatment, EbM must be integrated into HbM as an indispensable component of the latter. Probably the most important difference between EbM and HbM lies in the aims and methods of treatment. In HbM the goal is no longer simply to make illnesses disappear but rather to allow the patient to return to a life that is as autonomous and happy as possible. The human being with all his or her potential and limitations once again becomes the measure of all things. This also implies, however, that the multidimensional diagnostics of HbM are oriented not only towards symptoms, pathogenesis, process and understanding but also to a greater degree towards the patient's resources. Treatment options and forms of therapy do not put the disease construct at the centre of the diagnostic and therapeutic interest, but have as their primary aim the reopening of the possibility of a largely autonomous and joyful life for the patient.

Factor structure and internal reliability of an exercise health belief model scale in a Mexican population.

PubMed

Villar, Oscar Armando Esparza-Del; Montañez-Alvarado, Priscila; Gutiérrez-Vega, Marisela; Carrillo-Saucedo, Irene Concepción; Gurrola-Peña, Gloria Margarita; Ruvalcaba-Romero, Norma Alicia; García-Sánchez, María Dolores; Ochoa-Alcaraz, Sergio Gabriel

2017-03-01

Mexico is one of the countries with the highest rates of overweight and obesity around the world, with 68.8% of men and 73% of women reporting both. This is a public health problem since there are several health related consequences of not exercising, like having cardiovascular diseases or some types of cancers. All of these problems can be prevented by promoting exercise, so it is important to evaluate models of health behaviors to achieve this goal. Among several models the Health Belief Model is one of the most studied models to promote health related behaviors. This study validates the first exercise scale based on the Health Belief Model (HBM) in Mexicans with the objective of studying and analyzing this model in Mexico. Items for the scale called the Exercise Health Belief Model Scale (EHBMS) were developed by a health research team, then the items were applied to a sample of 746 participants, male and female, from five cities in Mexico. The factor structure of the items was analyzed with an exploratory factor analysis and the internal reliability with Cronbach's alpha. The exploratory factor analysis reported the expected factor structure based in the HBM. The KMO index (0.92) and the Barlett's sphericity test (p < 0.01) indicated an adequate and normally distributed sample. Items had adequate factor loadings, ranging from 0.31 to 0.92, and the internal consistencies of the factors were also acceptable, with alpha values ranging from 0.67 to 0.91. The EHBMS is a validated scale that can be used to measure exercise based on the HBM in Mexican populations.

Effect of health belief model and health promotion model on breast cancer early diagnosis behavior: a systematic review.

PubMed

Ersin, Fatma; Bahar, Zuhal

2011-01-01

Breast cancer is an important public health problem on the grounds that it is frequently seen and it is a fatal disease. The objective of this systematic analysis is to indicate the effects of interventions performed by nurses by using the Health Belief Model (HBM) and Health Promotion Model (HPM) on the breast cancer early diagnosis behaviors and on the components of the Health Belief Model and Health Promotion Model. The reveiw was created in line with the Centre for Reviews and Dissemination guide dated 2009 (CRD) and developed by York University National Institute of Health Researches. Review was conducted by using PUBMED, OVID, EBSCO and COCHRANE databases. Six hundred seventy eight studies (PUBMED: 236, OVID: 162, EBSCO: 175, COCHRANE:105) were found in total at the end of the review. Abstracts and full texts of these six hundred seventy eight studies were evaluated in terms of inclusion and exclusion criteria and 9 studies were determined to meet the criteria. Samplings of the studies varied between ninety four and one thousand six hundred fifty five. It was detected in the studies that educations provided by taking the theories as basis became effective on the breast cancer early diagnosis behaviors. When the literature is examined, it is observed that the experimental researches which compare the concepts of Health Belief Model (HBM) and Health Promotion Model (HPM) preoperatively and postoperatively and show the effect of these concepts on education and are conducted by nurses are limited in number. Randomized controlled studies which compare HBM and HPM concepts preoperatively and postoperatively and show the efficiency of the interventions can be useful in evaluating the efficiency of the interventions.

Knowledge, barriers, and motivators related to cervical cancer screening among Korean-American women. A focus group approach.

PubMed

Lee, M C

2000-06-01

Cervical cancer is a significant health problem for Korean-American women. It currently is the number one female cancer diagnosed among women in South Korea. Despite this fact, Korean-American women have very low rates of cervical cancer screening. The purpose of this research were to gain an understanding of Korean women's knowledge about cervical cancer, and to identify major barriers to early screening for cervical cancer and the motivators for prevention and early detection. It is hoped that the findings will guide the development of community-based cervical cancer education and screening programs for adult Korean-American women. The health belief model (HBM) provided the theoretical basis for the study. A qualitative study with eight focus groups (n = 102) was conducted using 11 questions derived from the HBM. Focus group discussions revealed that there was misinformation and a lack of knowledge about cervical cancer. The women therefore were confused about the causative factors and preventive strategies related to cervical cancer. The findings showed that major structural barriers were economic and time factors along with language problems. Many participants were recent immigrants with no medical insurance and long work hours. The main psychosocial barriers were fear/fatalism, denial, and Confucian thinking. Participants stated that medical advice and education would influence them most to undergo a Pap test. Recommendations were made to reduce certain barriers and to increase knowledge and motivations.

Heliotropium bacciferum Forssk. (Boraginaceae) extracts: chemical constituents, antioxidant activity and cytotoxic effect in human cancer cell lines.

PubMed

Aïssaoui, Hanane; Mencherini, Teresa; Esposito, Tiziana; De Tommasi, Nunziatina; Gazzerro, Patrizia; Benayache, Samir; Benayache, Fadila; Mekkiou, Ratiba

2018-02-12

Heliotropium bacciferum (Boraginaceae) is a perennial herb, growing in the Bechar region of Algeria, where it is traditionally used for skin diseases and tonsillitis. Herein, we report the isolation and characterization of sixteen secondary metabolites from the aerial part extracts. They include a sterol (1), megastigman type nor-isoprenoids (2, 3, 4, 6, 8, 10), C-11 terpene lactones (5 and 9), and a monoterpene (7) from the chloroform extract (HB-C); monoterpene glucoside (14), and phenolic compounds (11-13, 15, 16) from the methanol one (HB-M). Their structures were elucidated by spectroscopic methods including 1D and 2D NMR experiments, and ESIMS analysis. HB-M showed a significant and concentration dependent scavenging activity in vitro against the radicals DPPH and ABTS, related to the phenol derivatives (11-13, and 15-16), and HB-C inhibited the growth of colon cancer cell lines, mainly for the presence of the antiproliferative C-11 terpene lactones (5 and 9).

A review of Human Biomonitoring studies of trace elements in Pakistan.

PubMed

Waseem, Amir; Arshad, Jahanzaib

2016-11-01

Human biomonitoring (HBM) measures the concentration levels of substances or their metabolites in human body fluids and tissues. HBM of dose and biochemical effect monitoring is an effective way of measuring human exposure to chemical substances. Many countries have conducted HBM studies to develop a data base for many chemicals including trace metals of health concern for their risk assessment and risk management. However, in Pakistan, HBM program on large scale for general population does not exist at present or in the past has been reported. Various individual HBM studies have been reported on the assessment of trace elements (usually heavy metals) from Pakistan; most of them are epidemiological cross sectional surveys. In this current review we tried to develop a data base of HBM studies of trace elements namely arsenic, cadmium, copper, chromium, iron, lead, manganese, nickel, and zinc in biological fluids (blood, urine) and tissues (hair, nails) in general population of Pakistan. Studies from all available sources have been explored, discussed and presented in the form of tables and figures. The results of these studies were critically compared with large scale HBM programs of other countries, (US & European communities etc). It was observed from the present study that the most of the toxic metals in biological fluids/tissues in general population of Pakistan, have higher background values comparatively. For example the mean values of toxic metals like As, Cd, Cr, Ni, and Pb in blood of general population were found as 2.08 μg/L, 4.24 μg/L, 60.5 μg/L, 1.95 μg/L, 198 μg/L respectively. Similarly, the urine mean values of 67.6 μg/L, 3.2 μg/L, 16.4 μg/L, 6.2 μg/L and 86.5 μg/L were observed for As, Cd, Cr, Ni, and Pb respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Identification and isolation from either adult human bone marrow or G-CSF-mobilized peripheral blood of CD34(+)/CD133(+)/CXCR4(+)/ Lin(-)CD45(-) cells, featuring morphological, molecular, and phenotypic characteristics of very small embryonic-like (VSEL) stem cells.

PubMed

Sovalat, Hanna; Scrofani, Maurice; Eidenschenk, Antoinette; Pasquet, Stéphanie; Rimelen, Valérie; Hénon, Philippe

2011-04-01

Recently, we demonstrated that normal human bone marrow (hBM)-derived CD34(+) cells, released into the peripheral blood after granulocyte colony-stimulating factor mobilization, contain cell subpopulations committed along endothelial and cardiac differentiation pathways. These subpopulations could play a key role in the regeneration of post-ischemic myocardial lesion after their direct intracardiac delivery. We hypothesized that these relevant cells might be issued from very small embryonic-like stem cells deposited in the BM during ontogenesis and reside lifelong in the adult BM, and that they could be mobilized into peripheral blood by granulocyte colony-stimulating factor. Samples of normal hBM and leukapheresis products harvested from cancer patients after granulocyte colony-stimulating factor mobilization were analyzed and sorted by multiparameter flow cytometry strategy. Immunofluorescence and reverse transcription quantitative polymerase chain reaction assays were performed to analyze the expression of typical pluripotent stem cells markers. A population of CD34(+)/CD133(+)/CXCR4(+)/Lin(-) CD45(-) immature cells was first isolated from the hBM or from leukapheresis products. Among this population, very small (2-5 μm) cells expressing Oct-4, Nanog, and stage-specific embryonic antigen-4 at protein and messenger RNA levels were identified. Our study supports the hypothesis that very small embryonic-like stem cells constitute a "mobile" pool of primitive/pluripotent stem cells that could be released from the BM into the peripheral blood under the influence of various physiological or pathological stimuli. In order to fully support that hBM- and leukapheresis product-derived very small embryonic-like stem cells are actually pluripotent, we are currently testing their ability to differentiate in vitro into cells from all three germ layers. Copyright © 2011 ISEH - Society for Hematology and Stem Cells. Published by Elsevier Inc. All rights reserved.

Human biomonitoring: Science and policy for a healthy future, April 17-19, 2016, Berlin, Germany.

PubMed

Joas, Anke; Schwedler, Gerda; Choi, Judy; Kolossa-Gehring, Marike

2017-03-01

Following the success of the 1st International Conference on Human Biomonitoring (HBM) in Berlin in 2010, the 2nd International Conference on Human Biomonitoring took place in Berlin from April 17-19, 2016 for an exchange and updates among participants on all aspects relating to HBM. Entitled "Science and Policy for a Healthy Future", the conference brought together international experts from the scientific sector, politics, authorities, industry, non-governmental organizations (NGOs), and other involved associations. The conference took a critical look at today's chemicals that have a potential impact on human health and should be investigated as a matter of priority. It also discussed current activities and research efforts on HBM occurring worldwide, presented HBM success stories, and emphasized areas, where further research and focus are needed to improve the use of HBM for policy making. In many countries, HBM has been proven to be a useful tool and warning system to indicate problematic human exposure to pollutants and to evaluate the effectiveness of existing chemicals policy and regulations. However, important challenges remain such as exposure assessment of mixtures of chemicals, the development of analytical methods to detect new chemicals of concern (e.g., substitutes for phthalates), the identification of exposure sources, and the assessment of the impact of exposure on health. This brief report summarizes the discussions and contributions from this conference, which was jointly organized by the German Federal Environment Agency (UBA) and the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMUB). Copyright © 2017.

Generation of insulin-producing cells from human bone marrow-derived mesenchymal stem cells: comparison of three differentiation protocols.

PubMed

Gabr, Mahmoud M; Zakaria, Mahmoud M; Refaie, Ayman F; Khater, Sherry M; Ashamallah, Sylvia A; Ismail, Amani M; El-Badri, Nagwa; Ghoneim, Mohamed A

2014-01-01

Many protocols were utilized for directed differentiation of mesenchymal stem cells (MSCs) to form insulin-producing cells (IPCs). We compared the relative efficiency of three differentiation protocols. Human bone marrow-derived MSCs (HBM-MSCs) were obtained from three insulin-dependent type 2 diabetic patients. Differentiation into IPCs was carried out by three protocols: conophylline-based (one-step protocol), trichostatin-A-based (two-step protocol), and β -mercaptoethanol-based (three-step protocol). At the end of differentiation, cells were evaluated by immunolabeling for insulin production, expression of pancreatic endocrine genes, and release of insulin and c-peptide in response to increasing glucose concentrations. By immunolabeling, the proportion of generated IPCs was modest ( ≃ 3%) in all the three protocols. All relevant pancreatic endocrine genes, insulin, glucagon, and somatostatin, were expressed. There was a stepwise increase in insulin and c-peptide release in response to glucose challenge, but the released amounts were low when compared with those of pancreatic islets. The yield of functional IPCs following directed differentiation of HBM-MSCs was modest and was comparable among the three tested protocols. Protocols for directed differentiation of MSCs need further optimization in order to be clinically meaningful. To this end, addition of an extracellular matrix and/or a suitable template should be attempted.

A Bayesian Hierarchical Model for Glacial Dynamics Based on the Shallow Ice Approximation and its Evaluation Using Analytical Solutions

NASA Astrophysics Data System (ADS)

Gopalan, Giri; Hrafnkelsson, Birgir; Aðalgeirsdóttir, Guðfinna; Jarosch, Alexander H.; Pálsson, Finnur

2018-03-01

Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatio-temporal coordinates, and it will also allow for the derivation of posterior probability distributions for key physical parameters such as ice viscosity and basal sliding. The goal of this paper is to develop a proof of concept for a Bayesian hierarchical model constructed, which uses exact analytical solutions for the shallow ice approximation (SIA) introduced by Bueler et al. (2005). A suite of test simulations utilizing these exact solutions suggests that this approach is able to adequately model numerical errors and produce useful physical parameter posterior distributions and predictions. A byproduct of the development of the Bayesian hierarchical model is the derivation of a novel finite difference method for solving the SIA partial differential equation (PDE). An additional novelty of this work is the correction of numerical errors induced through a numerical solution using a statistical model. This error correcting process models numerical errors that accumulate forward in time and spatial variation of numerical errors between the dome, interior, and margin of a glacier.

Reduction of microhemorrhages in the spinal cord of symptomatic ALS mice after intravenous human bone marrow stem cell transplantation accompanies repair of the blood-spinal cord barrier.

PubMed

Eve, David J; Steiner, George; Mahendrasah, Ajay; Sanberg, Paul R; Kurien, Crupa; Thomson, Avery; Borlongan, Cesar V; Garbuzova-Davis, Svitlana

2018-02-13

Blood-spinal cord barrier (BSCB) alterations, including capillary rupture, have been demonstrated in animal models of amyotrophic lateral sclerosis (ALS) and ALS patients. To date, treatment to restore BSCB in ALS is underexplored. Here, we evaluated whether intravenous transplantation of human bone marrow CD34 + (hBM34 + ) cells into symptomatic ALS mice leads to restoration of capillary integrity in the spinal cord as determined by detection of microhemorrhages. Three different doses of hBM34 + cells (5 × 10 4 , 5 × 10 5 or 1 × 10 6 ) or media were intravenously injected into symptomatic G93A SOD1 mice at 13 weeks of age. Microhemorrhages were determined in the cervical and lumbar spinal cords of mice at 4 weeks post-treatment, as revealed by Perls' Prussian blue staining for ferric iron. Numerous microhemorrhages were observed in the gray and white matter of the spinal cords in media-treated mice, with a greater number of capillary ruptures within the ventral horn of both segments. In cell-treated mice, microhemorrhage numbers in the cervical and lumbar spinal cords were inversely related to administered cell doses. In particular, the pervasive microvascular ruptures determined in the spinal cords in late symptomatic ALS mice were significantly decreased by the highest cell dose, suggestive of BSCB repair by grafted hBM34 + cells. The study results provide translational outcomes supporting transplantation of hBM34 + cells at an optimal dose as a potential therapeutic strategy for BSCB repair in ALS patients.

Using NASA Satellite Aerosol Optical Depth to Enhance PM2.5 Concentration Datasets for Use in Human Health and Epidemiology Studies

NASA Astrophysics Data System (ADS)

Huff, A. K.; Weber, S.; Braggio, J.; Talbot, T.; Hall, E.

2012-12-01

Fine particulate matter (PM2.5) is a criterion air pollutant, and its adverse impacts on human health are well established. Traditionally, studies that analyze the health effects of human exposure to PM2.5 use concentration measurements from ground-based monitors and predicted PM2.5 concentrations from air quality models, such as the U.S. EPA's Community Multi-scale Air Quality (CMAQ) model. There are shortcomings associated with these datasets, however. Monitors are not distributed uniformly across the U.S., which causes spatially inhomogeneous measurements of pollutant concentrations. There are often temporal variations as well, since not all monitors make daily measurements. Air quality model output, while spatially and temporally uniform, represents predictions of PM2.5 concentrations, not actual measurements. This study is exploring the potential of combining Aerosol Optical Depth (AOD) data from the MODIS instrument on NASA's Terra and Aqua satellites with PM2.5 monitor data and CMAQ predictions to create PM2.5 datasets that more accurately reflect the spatial and temporal variations in ambient PM2.5 concentrations on the metropolitan scale, with the overall goal of enhancing capabilities for environmental public health decision-making. AOD data provide regional information about particulate concentrations that can fill in the spatial and temporal gaps in the national PM2.5 monitor network. Furthermore, AOD is a measurement, so it reflects actual concentrations of particulates in the atmosphere, in contrast to PM2.5 predictions from air quality models. Results will be presented from the Battelle/U.S. EPA statistical Hierarchical Bayesian Model (HBM), which was used to combine three PM2.5 concentration datasets: monitor measurements, AOD data, and CMAQ model predictions. The study is focusing on the Baltimore, MD and New York City, NY metropolitan regions for the period 2004-2006. For each region, combined monitor/AOD/CMAQ PM2.5 datasets generated by the HBM are being correlated with data on inpatient hospitalizations and emergency room visits for seven respiratory and cardiovascular diseases using statistical case-crossover analyses. Preliminary results will be discussed regarding the potential for the addition of AOD data to increase the correlation between PM2.5 concentrations and health outcomes. Environmental public health tracking programs associated with the Maryland Department of Health and Mental Hygiene, the New York State Department of Health, the CDC, and the U.S. EPA have expressed interest in using the results of this study to enhance their existing environmental health surveillance activities.

The transtheoretical model, health belief model, and breast cancer screening among Iranian women with a family history of breast cancer.

PubMed

Farajzadegan, Ziba; Fathollahi-Dehkordi, Fariba; Hematti, Simin; Sirous, Reza; Tavakoli, Neda; Rouzbahani, Reza

2016-01-01

Participation of Iranian women with a family history of breast cancer in breast cancer screening programs is low. This study evaluates the compliance of women having a family history of breast cancer with clinical breast exam (CBE) according to the stage of transtheoretical model (TTM) and health belief model (HBM). In this cross-sectional study, we used Persian version of champion's HBM scale to collect factors associated with TTM stages applied to screening from women over 20 years and older. The obtained data were analyzed by SPSS, using descriptive statistics, Chi-square test, independent t -test, and analysis of covariance. Final sample size was 162 women. Thirty-three percent were in action/maintenance stage. Older women, family history of breast cancer in first-degree relatives, personal history of breast disease, insurance coverage, and a history of breast self-examination were associated with action/maintenance stage. Furthermore, women in action/maintenance stages had significantly fewer perceived barriers in terms of CBE in comparison to women in other stages ( P < 0.05). There was no significant difference in other HBM subscales scores between various stages of CBE screening behavior ( P > 0.05). The finding indicates that the rate of women in action/maintenance stage of CBE is low. Moreover, results show a strong association between perceived barriers and having a regular CBE. These clarify the necessity of promoting national target programs for breast cancer screening, which should be considered as the first preference for reducing CBE barriers.

Prediction of safe driving Behaviours based on health belief model: the case of taxi drivers in Bandar Abbas, Iran.

PubMed

Razmara, Asghar; Aghamolaei, Teamur; Madani, Abdoulhossain; Hosseini, Zahra; Zare, Shahram

2018-03-20

Road accidents are among the main causes of mortality. As safe and secure driving is a key strategy to reduce car injuries and offenses, the present research aimed to explore safe driving behaviours among taxi drivers based on the Health Belief Model (HBM). This study was conducted on 184 taxi drivers in Bandar Abbas who were selected based on a multiple stratified sampling method. Data were collected by a questionnaire comprised of a demographic information section along with the constructs of the HBM. Data were analysed by SPSS ver19 via a Pearson's correlation coefficient and multiple regressions. The mean age of the participants was 45.1 years (SD = 11.1). They all had, on average, 10.3 (SD = 7/5) years of taxi driving experience. Among the HBM components, cues to action and perceived benefits were shown to be positively correlated with safe driving behaviours, while perceived barriers were negatively correlated. Cues to action, perceived barriers and perceived benefits were shown to be the strongest predictors of a safe drivers' behaviour. Based on the results of this study in designing health promotion programmes to improve safe driving behaviours among taxi drivers, cues to action, perceived benefits and perceived barriers are important. Therefore, advertising, the design of information campaigns, emphasis on the benefits of safe driving behaviours and modification barriers are recommended.

Local SAR in High Pass Birdcage and TEM Body Coils for Multiple Human Body Models in Clinical Landmark Positions at 3T

PubMed Central

Yeo, Desmond TB; Wang, Zhangwei; Loew, Wolfgang; Vogel, Mika W; Hancu, Ileana

2011-01-01

Purpose To use EM simulations to study the effects of body type, landmark position, and RF body coil type on peak local SAR in 3T MRI. Materials and Methods Numerically computed peak local SAR for four human body models (HBMs) in three landmark positions (head, heart, pelvic) were compared for a high-pass birdcage and a transverse electromagnetic 3T body coil. Local SAR values were normalized to the IEC whole-body average SAR limit of 2.0 W/kg for normal scan mode. Results Local SAR distributions were highly variable. Consistent with previous reports, the peak local SAR values generally occurred in the neck-shoulder area, near rungs, or between tissues of greatly differing electrical properties. The HBM type significantly influenced the peak local SAR, with stockier HBMs, extending extremities towards rungs, displaying the highest SAR. There was also a trend for higher peak SAR in the head-centric and heart-centric positions. The impact of the coil-types studied was not statistically significant. Conclusion The large variability in peak local SAR indicates the need to include more than one HBM or landmark position when evaluating safety of body coils. It is recommended that a HBM with arms near the rungs be included, to create physically realizable high-SAR scenarios. PMID:21509880

Bayesian model checking: A comparison of tests

NASA Astrophysics Data System (ADS)

Lucy, L. B.

2018-06-01

Two procedures for checking Bayesian models are compared using a simple test problem based on the local Hubble expansion. Over four orders of magnitude, p-values derived from a global goodness-of-fit criterion for posterior probability density functions agree closely with posterior predictive p-values. The former can therefore serve as an effective proxy for the difficult-to-calculate posterior predictive p-values.

Vaccine perception among acceptors and non-acceptors in Sokoto State, Nigeria.

PubMed

Murele, Bola; Vaz, Rui; Gasasira, Alex; Mkanda, Pascal; Erbeto, Tesfaye; Okeibunor, Joseph

2014-05-30

Vaccine perceptions among acceptors and non-acceptors of childhood vaccination were explored. Seventy-two care givers, among them, acceptors and non-acceptors were interviewed in-depth with an interview guide that assessed vaccine acceptance, social and personality factors, and health belief model (HBM) categories in relation to oral polio vaccine (perceived susceptibility, severity, cost barriers, general barriers, benefits, knowledge, and engagement in preventative health behaviours). Community leaders were purposively selected while parents were selected on the basis of availability while ensuring the different attitude to vaccines was covered. Results showed that the HBM framework was found to be appropriate for identifying and distinguishing vaccine acceptors and non-acceptors. In addition, the HBM categories of benefits and susceptibility were found to influence oral polio vaccine acceptance. Second, the opinion of family members about the oral polio vaccine moderated the relationship between number of social ties and vaccine acceptance. Further, oral polio vaccine acceptance was related to outbreaks of paralysis of any sort, but not aggregate scores of other preventative health behaviours. Implications of this study include the investigation of vaccine acceptance in a high risk population. Research was done to investigate vaccine acceptance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Health beliefs and breast self-examination in a sample of Turkish women academicians in a university.

PubMed

Ceber, Esin; Yücel, Ummahan; Mermer, Gülengül; Ozentürk, Gülsün

2009-01-01

The purpose of this study was to evaluate health beliefs and BSE behavior of female academicians in a Turkish university. This descriptive study was conducted at various faculties located in Ege University, Izmir, Turkey, in 2005. The sample consisted of 224 female academicians. Data were collected using a self-administered questionnaire and the Turkish version of Champion's Health Belief Model Scales (HBM). Descriptive statistics, t-test and Mann Whitney u analysis were conducted. The percentage of participants who regularly performed BSE was 27.7 %. Benefits and health motivation related to BSE ranked either first or second, along with confidence. Perceived barriers to BSE had the lowest item mean subscale score in academicians. Single academicians perceived susceptibility and seriousness higher than their married counterparts. Family history of breast cancer of participants affected their health beliefs subscale. BSE performance among participants was more likely in women academicians who exhibited higher confidence and those who perceived fewer barriers related to BSE performance, complying with the conceptual structure of the HBM. Therefore, it is recommended that in order to increase the rates of regular breast cancer screening, mass health protective programs based on the HBM should be executed for women.

Recent Performance Results of VPIC on Trinity

NASA Astrophysics Data System (ADS)

Nystrom, W. D.; Bergen, B.; Bird, R. F.; Bowers, K. J.; Daughton, W. S.; Guo, F.; Le, A.; Li, H.; Nam, H.; Pang, X.; Stark, D. J.; Rust, W. N., III; Yin, L.; Albright, B. J.

2017-10-01

Trinity is a new DOE compute resource now in production at Los Alamos National Laboratory. Trinity has several new and unique features including two compute partitions, one with dual socket Intel Haswell Xeon compute nodes and one with Intel Knights Landing (KNL) Xeon Phi compute nodes, use of on package high bandwidth memory (HBM) for KNL nodes, ability to configure KNL nodes with respect to HBM model and on die network topology in a variety of operational modes at run time, and use of solid state storage via burst buffer technology to reduce time required to perform I/O. An effort is in progress to optimize VPIC on Trinity by taking advantage of these new architectural features. Results of work will be presented on performance of VPIC on Haswell and KNL partitions for single node runs and runs at scale. Results include use of burst buffers at scale to optimize I/O, comparison of strategies for using MPI and threads, performance benefits using HBM and effectiveness of using intrinsics for vectorization. Work performed under auspices of U.S. Dept. of Energy by Los Alamos National Security, LLC Los Alamos National Laboratory under contract DE-AC52-06NA25396 and supported by LANL LDRD program.

A Fast Surrogate-facilitated Data-driven Bayesian Approach to Uncertainty Quantification of a Regional Groundwater Flow Model with Structural Error

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Ye, M.; Liang, F.

2016-12-01

Due to simplification and/or misrepresentation of the real aquifer system, numerical groundwater flow and solute transport models are usually subject to model structural error. During model calibration, the hydrogeological parameters may be overly adjusted to compensate for unknown structural error. This may result in biased predictions when models are used to forecast aquifer response to new forcing. In this study, we extend a fully Bayesian method [Xu and Valocchi, 2015] to calibrate a real-world, regional groundwater flow model. The method uses a data-driven error model to describe model structural error and jointly infers model parameters and structural error. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models. The surrogate models are constructed using machine learning techniques to emulate the response simulated by the computationally expensive groundwater model. We demonstrate in the real-world case study that explicitly accounting for model structural error yields parameter posterior distributions that are substantially different from those derived by the classical Bayesian calibration that does not account for model structural error. In addition, the Bayesian with error model method gives significantly more accurate prediction along with reasonable credible intervals.

Identification of exposure to environmental chemicals in children and older adults using human biomonitoring data sorted by age: Results from a literature review.

PubMed

Choi, Judy; Knudsen, Lisbeth E; Mizrak, Seher; Joas, Anke

2017-03-01

Human biomonitoring (HBM) provides the tools for exposure assessment by direct measurements of biological specimens such as blood and urine. HBM can identify new chemical exposures, trends and changes in exposure, establish distribution of exposure among the general population, and identify vulnerable groups and populations with distinct exposures such as children and older adults. The objective of this review is to demonstrate the use of HBM to identify environmental chemicals that might be of concern for children or older adults due to higher body burden. To do so, an extensive literature search was performed, and using a set of defined criteria, ten large-scale, cross-sectional national HBM programs were selected for data review and evaluation. A comparative analysis of the age-stratified data from these programs and other relevant HBM studies indicated twelve chemicals/classes of chemicals with potentially higher body burden in children or older adults. Children appear to have higher body burden of bisphenol A (BPA), some phytoestrogens, perchlorate, and some metabolites of polycyclic aromatic hydrocarbons and benzene. On the other hand, older adults appear to have higher body burden of heavy metals and organochlorine pesticides. For perfluoroalkyl substances, polybrominated diphenyl ethers, parabens, and phthalates, both children and older adults have higher body burden depending on the specific biomarkers analyzed, and this might be due to the exposure period and/or sources from different countries. Published data from the DEMOCOPHES project (a pilot study to harmonize HBM efforts across Europe) also showed elevated exposures to BPA and some phthalate metabolites in children across several European countries. In summary, age-stratified HBM data can provide useful knowledge of identifying environmental chemicals that might be of concern for children and older adults, which, combined with additional efforts to identify potential sources of exposure, could assist policy makers in prioritizing their actions in order to reduce chemical exposure and potential risks of adverse health effects. Copyright © 2016 Elsevier GmbH. All rights reserved.

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction

PubMed Central

Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José Cricelio; Luna-Vázquez, Francisco Javier; Salinas-Ruiz, Josafhat; Herrera-Morales, José R.; Buenrostro-Mariscal, Raymundo

2017-01-01

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments. PMID:28391241

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José Cricelio; Luna-Vázquez, Francisco Javier; Salinas-Ruiz, Josafhat; Herrera-Morales, José R; Buenrostro-Mariscal, Raymundo

2017-06-07

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments. Copyright © 2017 Montesinos-López et al.

Randomized comparison of group versus individual educational interventions for pregnant women to reduce their secondhand smoke exposure.

PubMed

Chi, Ying-Chen; Sha, Feng; Yip, Paul S F; Chen, Jiunn-Liang; Chen, Ying-Yeh

2016-10-01

Secondhand smoke (SHS) exposure is deleterious to pregnant women and their unborn children. The prevalence of SHS exposure among pregnant women is particularly high in many Asian countries where approximately half of the male population smokes. We aim to investigate the efficacy of an intervention based on an expanded Health Belief Model (HBM) incorporating self-efficacy to educate and empower pregnant women to reduce their SHS exposure. We conducted a 3-arm randomized controlled trial (N = 50 in each arm) comparing the effectiveness of group-based and individual-based interventions with a treatment-as-usual group. A questionnaire tapping into constructs of the expanded HBM was administered at baseline and 1- and 2-month follow-ups. Exhaled carbon monoxide was used to determine SHS exposure (>=6 ppm). ANOVA was used to compare HBM construct scores, self-efficacy for rejecting SHS exposure, and SHS rejection behavior among the 3 groups at baseline and the 1- and 2-month follow-ups, while logistic regression analysis was used to compare the risk of exposure to SHS at each follow-up. The group-based intervention significantly improved health beliefs, self-efficacy, and self-reported rejection behaviors. The individual-based intervention effect was limited to some health belief constructs and SHS rejection behaviors. Both group- and individual-based interventions showed significant reductions in SHS exposure 2 months after the intervention (P < 0.0001). Group-based educational interventions based on the HBM are particularly effective in training pregnant women to avoid and refuse exposure to SHS. Policy makers should consider offering group-delivered programs to educate and empower pregnant women to reduce their SHS exposure.

Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

NASA Astrophysics Data System (ADS)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

2016-05-01

Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert elicitation methodology is developed and applied to the real-world test case in order to provide a road map for the use of fuzzy Bayesian inference in groundwater modeling applications.

Incidence and predictive factors of Internet addiction among Chinese secondary school students in Hong Kong: a longitudinal study.

PubMed

Lau, Joseph T F; Gross, Danielle L; Wu, Anise M S; Cheng, Kit-Man; Lau, Mason M C

2017-06-01

Internet use has global influences on all aspects of life and has become a growing concern. Cross-sectional studies on Internet addiction (IA) have been reported but causality is often unclear. More longitudinal studies are warranted. We investigated incidence and predictors of IA conversion among secondary school students. A 12-month longitudinal study was conducted among Hong Kong Chinese Secondary 1-4 students (N = 8286). Using the 26-item Chen Internet Addiction Scale (CIAS; cut-off >63), non-IA cases were identified at baseline. Conversion to IA during the follow-up period was detected, with incidence and predictors derived using multi-level models. Prevalence of IA was 16.0% at baseline and incidence of IA was 11.81 per 100 person-years (13.74 for males and 9.78 for females). Risk background factors were male sex, higher school forms, and living with only one parent, while protective background factors were having a mother/father with university education. Adjusted for all background factors, higher baseline CIAS score (ORa = 1.07), longer hours spent online for entertainment and social communication (ORa = 1.92 and 1.63 respectively), and Health Belief Model (HBM) constructs (except perceived severity of IA and perceived self-efficacy to reduce use) were significant predictors of conversion to IA (ORa = 1.07-1.45). Prevalence and incidence of IA conversion were high and need attention. Interventions should take into account risk predictors identified, such as those of the HBM, and time management skills should be enhanced. Screening is warranted to identify those at high risk (e.g. high CIAS score) and provide them with primary and secondary interventions.

Efficacy of Peer Education for Adopting Preventive Behaviors against Head Lice Infestation in Female Elementary School Students: A Randomised Controlled Trial

PubMed Central

Zamani-Alavijeh, Fereshteh; Mojadam, Mehdi

2017-01-01

Background Pediculosis is a common parasitic infestation in students worldwide, including Iran. This condition is more prevalent in populous and deprived communities with poor personal hygiene. This study sought to assess the efficacy of peer education for adopting preventive behaviors against pediculosis in female elementary school students based on the Health Belief Model (HBM). Methods A total of 179 female fifth grade students were selected using multistage random sampling and were randomly allocated to control and intervention groups. A standard questionnaire was designed and administered to collect baseline information. An educational intervention was then designed based on the conducted needs assessment. The educational program consisted of three sessions, held by peers for the intervention group. The questionnaire was re-administered one month after the intervention. Independent and paired t-test, Pearson’s correlation coefficient, and regression analysis were applied as appropriate. Results The two groups had no significant differences in the scores of knowledge, HBM constructs, or behavior before the intervention. After the intervention, however, the mean scores of all parameters significantly improved in the intervention group. Conclusion Peer education based on HBM is an effective strategy to promote preventive behaviors against pediculosis in among fifth grade female elementary school students in Iran. PMID:28072852

Barriers to Self-Management Behaviors in College Students with Food Allergies

ERIC Educational Resources Information Center

Duncan, Sarah E.; Annunziato, Rachel A.

2018-01-01

Objective: This study examined barriers to engagement in self-management behaviors among food-allergic college students (1) within the frameworks of the health belief model (HBM) and common sense self-regulation model (CS-SRM) and (2) in the context of overall risky behaviors. Participants: Undergraduate college students who reported having a…

Using the Health Belief Model to Predict Bystander Behavior among College Students

ERIC Educational Resources Information Center

Blavos, Alexis A.; Glassman, Tavis; Sheu, Jiunn-Jye; Diehr, Aaron; Deakins, Bethany

2014-01-01

This investigation used the Health Belief Model (HBM) to examine perceived barriers and benefits college students hold concerning medical amnesty. Researchers employed a cross-sectional research design with 369 students completing the survey (97% response rate). A path analysis revealed that college students are more likely to seek help during an…

Managing Dog Waste: Campaign Insights from the Health Belief Model

ERIC Educational Resources Information Center

Typhina, Eli; Yan, Changmin

2014-01-01

Aiming to help municipalities develop effective education and outreach campaigns to reduce stormwater pollutants, such as pet waste, this study applied the Health Belief Model (HBM) to identify perceptions of dog waste and corresponding collection behaviors from dog owners living in a small U.S. city. Results of 455 online survey responses…

Evaluation of 6 and 10 Year-Old Child Human Body Models in Emergency Events.

PubMed

Gras, Laure-Lise; Stockman, Isabelle; Brolin, Karin

2017-01-01

Emergency events can influence a child's kinematics prior to a car-crash, and thus its interaction with the restraint system. Numerical Human Body Models (HBMs) can help understand the behaviour of children in emergency events. The kinematic responses of two child HBMs-MADYMO 6 and 10 year-old models-were evaluated and compared with child volunteers' data during emergency events-braking and steering-with a focus on the forehead and sternum displacements. The response of the 6 year-old HBM was similar to the response of the 10 year-old HBM, however both models had a different response compared with the volunteers. The forward and lateral displacements were within the range of volunteer data up to approximately 0.3 s; but then, the HBMs head and sternum moved significantly downwards, while the volunteers experienced smaller displacement and tended to come back to their initial posture. Therefore, these HBMs, originally intended for crash simulations, are not too stiff and could be able to reproduce properly emergency events thanks, for instance, to postural control.

Conceptual framework for a Danish human biomonitoring program

PubMed Central

Thomsen, Marianne; Knudsen, Lisbeth E; Vorkamp, Katrin; Frederiksen, Marie; Bach, Hanne; Bonefeld-Jorgensen, Eva Cecilie; Rastogi, Suresch; Fauser, Patrik; Krongaard, Teddy; Sorensen, Peter Borgen

2008-01-01

The aim of this paper is to present the conceptual framework for a Danish human biomonitoring (HBM) program. The EU and national science-policy interface, that is fundamental for a realization of the national and European environment and human health strategies, is discussed, including the need for a structured and integrated environmental and human health surveillance program at national level. In Denmark, the initiative to implement such activities has been taken. The proposed framework of the Danish monitoring program constitutes four scientific expert groups, i.e. i. Prioritization of the strategy for the monitoring program, ii. Collection of human samples, iii. Analysis and data management and iv. Dissemination of results produced within the program. This paper presents the overall framework for data requirements and information flow in the integrated environment and health surveillance program. The added value of an HBM program, and in this respect the objectives of national and European HBM programs supporting environmental health integrated policy-decisions and human health targeted policies, are discussed. In Denmark environmental monitoring has been prioritized by extensive surveillance systems of pollution in oceans, lakes and soil as well as ground and drinking water. Human biomonitoring has only taken place in research programs and few incidences of e.g. lead contamination. However an arctic program for HBM has been in force for decades and from the preparations of the EU-pilot project on HBM increasing political interest in a Danish program has developed. PMID:18541069

Acute lung injury after tracheal instillation of acidified soya-based or Enfalac formula or human breast milk in rabbits.

PubMed

Chin, C; Lerman, J; Endo, J

1999-03-01

The aim of this study was to compare the severity of the acute lung injury after tracheal instillation of acidified soya-based or Enfalac infant formula, or human breast milk (HBM) in anesthetized rabbits. Alveolar-arterial oxygen tension gradient (A-aDO2) and dynamic compliance were measured before (baseline) and hourly for four hours after tracheal instillation of 0.8 ml x kg(-1) soya-based or Enfalac infant formula or HBM (all acidified to pH 1.8 with hydrochloric acid) or no fluid (control) in 24 anesthetized and tracheotomized adult rabbits. The A-aDO2, the difference between alveolar and arterial oxygen tensions, was corrected for barometric pressure and carbon dioxide tension. Dynamic compliance was the ratio of the expired tidal volume to the peak inspiratory airway pressure, normalized to body weight. Baseline A-aDO2 and dynamic compliance were similar among the four groups. In the control rabbits, A-aDO2 remained unchanged throughout the four hours, whereas mean A-aDO2 increased 180 mm Hg in the soya-based group (P < 0.0025) and 350 and 275 mm Hg in the Enfalac and HBM groups respectively (P < 0.0002). The order of the A-aDO2 post-instillation was Enfalac approximately/= HBM > soya > control (P < 0.0002). Dynamic compliance decreased 10-12% in the control rabbits during the four post-instillation measurements compared with baseline (P < 0.033), decreased 20% in the soya-based group (P < 0.0002) and 40-50% in the Enfalac and HBM groups (P < 0.0002). The severity of the acute lung injury after intratracheal instillation of infant feeds in a volume of 0.8 ml x kg(-1) and at pH 1.8 in rabbits depends in part, on the type of feed: Enfalac approximately/= HBM > soya > control.

Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging.

PubMed

Gençay, R; Qi, M

2001-01-01

We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available.

Testicular Self-Examination: A Test of the Health Belief Model and the Theory of Planned Behaviour

ERIC Educational Resources Information Center

McClenahan, Carol; Shevlin, Mark; Adamson, Gary; Bennett, Cara; O'Neill, Brenda

2007-01-01

The aim of this study was to test the utility and efficiency of the theory of planned behaviour (TPB) and the health belief model (HBM) in predicting testicular self-examination (TSE) behaviour. A questionnaire was administered to an opportunistic sample of 195 undergraduates aged 18-39 years. Structural equation modelling indicated that, on the…

The Bicycle Helmet Attitudes Scale: Using the Health Belief Model to Predict Helmet Use among Undergraduates

ERIC Educational Resources Information Center

Ross, Thomas P.; Ross, Lisa Thomson; Rahman, Annalise; Cataldo, Shayla

2010-01-01

Objective: This study examined bicycle helmet attitudes and practices of college undergraduates and developed the Bicycle Helmet Attitudes Scale, which was guided by the Health Belief Model (HBM; Rosenstock, 1974, in Becker MH, ed. "The Health Belief Model and Personal Health Behavior". Thorofare, NJ: Charles B. Slack; 1974:328-335) to predict…

Health beliefs affect the correct replacement of daily disposable contact lenses: Predicting compliance with the Health Belief Model and the Theory of Planned Behaviour.

PubMed

Livi, Stefano; Zeri, Fabrizio; Baroni, Rossella

2017-02-01

To assess the compliance of Daily Disposable Contact Lenses (DDCLs) wearers with replacing lenses at a manufacturer-recommended replacement frequency. To evaluate the ability of two different Health Behavioural Theories (HBT), The Health Belief Model (HBM) and The Theory of Planned Behaviour (TPB), in predicting compliance. A multi-centre survey was conducted using a questionnaire completed anonymously by contact lens wearers during the purchase of DDCLs. Three hundred and fifty-four questionnaires were returned. The survey comprised 58.5% females and 41.5% males (mean age 34±12years). Twenty-three percent of respondents were non-compliant with manufacturer-recommended replacement frequency (re-using DDCLs at least once). The main reason for re-using DDCLs was "to save money" (35%). Predictions of compliance behaviour (past behaviour or future intentions) on the basis of the two HBT was investigated through logistic regression analysis: both TPB factors (subjective norms and perceived behavioural control) were significant (p<0.01); HBM was less predictive with only the severity (past behaviour and future intentions) and perceived benefit (only for past behaviour) as significant factors (p<0.05). Non-compliance with DDCLs replacement is widespread, affecting 1 out of 4 Italian wearers. Results from the TPB model show that the involvement of persons socially close to the wearers (subjective norms) and the improvement of the procedure of behavioural control of daily replacement (behavioural control) are of paramount importance in improving compliance. With reference to the HBM, it is important to warn DDCLs wearers of the severity of a contact-lens-related eye infection, and to underline the possibility of its prevention. Copyright © 2016 British Contact Lens Association. Published by Elsevier Ltd. All rights reserved.

Bayesian exponential random graph modelling of interhospital patient referral networks.

PubMed

Caimo, Alberto; Pallotti, Francesca; Lomi, Alessandro

2017-08-15

Using original data that we have collected on referral relations between 110 hospitals serving a large regional community, we show how recently derived Bayesian exponential random graph models may be adopted to illuminate core empirical issues in research on relational coordination among healthcare organisations. We show how a rigorous Bayesian computation approach supports a fully probabilistic analytical framework that alleviates well-known problems in the estimation of model parameters of exponential random graph models. We also show how the main structural features of interhospital patient referral networks that prior studies have described can be reproduced with accuracy by specifying the system of local dependencies that produce - but at the same time are induced by - decentralised collaborative arrangements between hospitals. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Competitive testing of health behavior theories: how do benefits, barriers, subjective norm, and intention influence mammography behavior?

PubMed Central

Murphy, Caitlin C.; Vernon, Sally W.; Diamond, Pamela M.; Tiro, Jasmin A.

2013-01-01

Background Competitive hypothesis testing may explain differences in predictive power across multiple health behavior theories. Purpose We tested competing hypotheses of the Health Belief Model (HBM) and Theory of Reasoned Action (TRA) to quantify pathways linking subjective norm, benefits, barriers, intention, and mammography behavior. Methods We analyzed longitudinal surveys of women veterans randomized to the control group of a mammography intervention trial (n=704). We compared direct, partial mediation, and full mediation models with Satorra-Bentler χ2 difference testing. Results Barriers had a direct and indirect negative effect on mammography behavior; intention only partially mediated barriers. Benefits had little to no effect on behavior and intention; however, it was negatively correlated with barriers. Subjective norm directly affected behavior and indirectly affected intention through barriers. Conclusions Our results provide empiric support for different assertions of HBM and TRA. Future interventions should test whether building subjective norm and reducing negative attitudes increases regular mammography. PMID:23868613

Use of health services in Hill villages in central Nepal.

PubMed

Niraula, B B

1994-10-01

This paper reports the use and non-use of health care facilities in the Hill villages in central Nepal. The health behaviour model (HBM) is applied to test the significance of socioeconomic variables on the use of the modern health care system. The study finds that all three characteristics of the HBM model, predisposing, enabling and need, are significantly related to use and non-use of the modern health care system. The analysis shows that number of living children, respondent's education, nearness to the road and service centre, value of land, knowledge about health workers and experience of child loss are some of the variables that are positively and significantly related to the use of modern health care. Age of the respondents and household size were found to be negatively associated with health-care use. Contrary to expectation, caste is unimportant. Making use of the qualitative data, this paper argues that the health care system is unnecessarily bureaucratic and patriarchal, which favours the socio-economically well-off.

A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging

PubMed Central

Zhang, Yin; Zhang, Yongchao; Huang, Yulin; Yang, Jianyu

2017-01-01

This paper presents a sparse superresolution approach for high cross-range resolution imaging of forward-looking scanning radar based on the Bayesian criterion. First, a novel forward-looking signal model is established as the product of the measurement matrix and the cross-range target distribution, which is more accurate than the conventional convolution model. Then, based on the Bayesian criterion, the widely-used sparse regularization is considered as the penalty term to recover the target distribution. The derivation of the cost function is described, and finally, an iterative expression for minimizing this function is presented. Alternatively, this paper discusses how to estimate the single parameter of Gaussian noise. With the advantage of a more accurate model, the proposed sparse Bayesian approach enjoys a lower model error. Meanwhile, when compared with the conventional superresolution methods, the proposed approach shows high cross-range resolution and small location error. The superresolution results for the simulated point target, scene data, and real measured data are presented to demonstrate the superior performance of the proposed approach. PMID:28604583

Enhanced Wnt signaling improves bone mass and strength, but not brittleness, in the Col1a1(+/mov13) mouse model of type I Osteogenesis Imperfecta.

PubMed

Jacobsen, Christina M; Schwartz, Marissa A; Roberts, Heather J; Lim, Kyung-Eun; Spevak, Lyudmila; Boskey, Adele L; Zurakowski, David; Robling, Alexander G; Warman, Matthew L

2016-09-01

Osteogenesis Imperfecta (OI) comprises a group of genetic skeletal fragility disorders. The mildest form of OI, Osteogenesis Imperfecta type I, is frequently caused by haploinsufficiency mutations in COL1A1, the gene encoding the α1(I) chain of type 1 collagen. Children with OI type I have a 95-fold higher fracture rate compared to unaffected children. Therapies for OI type I in the pediatric population are limited to anti-catabolic agents. In adults with osteoporosis, anabolic therapies that enhance Wnt signaling in bone improve bone mass, and ongoing clinical trials are determining if these therapies also reduce fracture risk. We performed a proof-of-principle experiment in mice to determine whether enhancing Wnt signaling in bone could benefit children with OI type I. We crossed a mouse model of OI type I (Col1a1(+/Mov13)) with a high bone mass (HBM) mouse (Lrp5(+/p.A214V)) that has increased bone strength from enhanced Wnt signaling. Offspring that inherited the OI and HBM alleles had higher bone mass and strength than mice that inherited the OI allele alone. However, OI+HBM and OI mice still had bones with lower ductility compared to wild-type mice. We conclude that enhancing Wnt signaling does not make OI bone normal, but does improve bone properties that could reduce fracture risk. Therefore, agents that enhance Wnt signaling are likely to benefit children and adults with OI type 1. Copyright © 2016 Elsevier Inc. All rights reserved.

First reported case of Haemoglobin-M Hyde Park in a Malay family living in Malaysia.

PubMed

Loong, Tang Yee; Chong, Doris Lau Sie; Jamal, A Rahman A; Murad, Nor Azian Abdul; Sabudin, Raja Zahratul Azma Raja; Fun, Leong Chooi

2016-01-01

Haemoglobin (Hb)-M Hyde Park, also known as Hb-M Akita is a rare type of hereditary Hb M due to autosomal dominant mutation of CAC>TAC on codon 92 of β globin gene resulting in the replacement of histidine by tyrosine on β globin chain. This variant Hb has a tendency to form methaemoglobin (metHb). The iron ion in metHb is oxidized to ferric (Fe3+) which is unable to carry oxygen and the patients manifest as cyanosis clinically. A 9-year-old Malay girl was incidentally found to be cyanotic when she presented to a health clinic. Laboratory investigations revealed raised methaemoglobin levels and Hb analysis findings were consistent with Hb-M Hyde Park. β gene sequencing confirmed a point mutation of CAC>TAC on codon 92 in one of the β genes. The family study done on the individuals with cyanosis showed similar findings. A diagnosis of heterozygous Hb-M Hyde Park was made. Patients with this variant Hb usually presented with cyanosis with mild haemolysis and maybe misdiagnosed as congenital heart disease. No further treatment is needed as patients are relatively asymptomatic. Although the disease is harmless in the heterozygous carriers but the offspring of the carriers may suffer severe haemolytic anaemia when the offspring also inherit other β haemoglobinopathies/thalassemia. This can happen due to high prevalence of β thalassemia carrier (3.5-4 %) found in Malaysia. At the time of writing, this is the first case of hereditary Hb-M Hyde Park diagnosed in a Malay family living in Malaysia.

Wronski Brackets and the Ferris Wheel

NASA Astrophysics Data System (ADS)

Martin, Keye

2005-11-01

We connect the Bayesian order on classical states to a certain Lie algebra on C^infty[0,1]. This special Lie algebra structure, made precise by an idea we introduce called a Wronski bracket, suggests new phenomena the Bayesian order naturally models. We then study Wronski brackets on associative algebras, and in the commutative case, discover the beautiful result that they are equivalent to derivations.

Evaluation of 6 and 10 Year-Old Child Human Body Models in Emergency Events

PubMed Central

2017-01-01

Emergency events can influence a child’s kinematics prior to a car-crash, and thus its interaction with the restraint system. Numerical Human Body Models (HBMs) can help understand the behaviour of children in emergency events. The kinematic responses of two child HBMs–MADYMO 6 and 10 year-old models–were evaluated and compared with child volunteers’ data during emergency events–braking and steering–with a focus on the forehead and sternum displacements. The response of the 6 year-old HBM was similar to the response of the 10 year-old HBM, however both models had a different response compared with the volunteers. The forward and lateral displacements were within the range of volunteer data up to approximately 0.3 s; but then, the HBMs head and sternum moved significantly downwards, while the volunteers experienced smaller displacement and tended to come back to their initial posture. Therefore, these HBMs, originally intended for crash simulations, are not too stiff and could be able to reproduce properly emergency events thanks, for instance, to postural control. PMID:28099505

Bayesian model selection: Evidence estimation based on DREAM simulation and bridge sampling

NASA Astrophysics Data System (ADS)

Volpi, Elena; Schoups, Gerrit; Firmani, Giovanni; Vrugt, Jasper A.

2017-04-01

Bayesian inference has found widespread application in Earth and Environmental Systems Modeling, providing an effective tool for prediction, data assimilation, parameter estimation, uncertainty analysis and hypothesis testing. Under multiple competing hypotheses, the Bayesian approach also provides an attractive alternative to traditional information criteria (e.g. AIC, BIC) for model selection. The key variable for Bayesian model selection is the evidence (or marginal likelihood) that is the normalizing constant in the denominator of Bayes theorem; while it is fundamental for model selection, the evidence is not required for Bayesian inference. It is computed for each hypothesis (model) by averaging the likelihood function over the prior parameter distribution, rather than maximizing it as by information criteria; the larger a model evidence the more support it receives among a collection of hypothesis as the simulated values assign relatively high probability density to the observed data. Hence, the evidence naturally acts as an Occam's razor, preferring simpler and more constrained models against the selection of over-fitted ones by information criteria that incorporate only the likelihood maximum. Since it is not particularly easy to estimate the evidence in practice, Bayesian model selection via the marginal likelihood has not yet found mainstream use. We illustrate here the properties of a new estimator of the Bayesian model evidence, which provides robust and unbiased estimates of the marginal likelihood; the method is coined Gaussian Mixture Importance Sampling (GMIS). GMIS uses multidimensional numerical integration of the posterior parameter distribution via bridge sampling (a generalization of importance sampling) of a mixture distribution fitted to samples of the posterior distribution derived from the DREAM algorithm (Vrugt et al., 2008; 2009). Some illustrative examples are presented to show the robustness and superiority of the GMIS estimator with respect to other commonly used approaches in the literature.

Storm drains are sources of human fecal pollution during dry weather in three urban southern California watersheds.

PubMed

Sercu, Bram; Van De Werfhorst, Laurie C; Murray, Jill; Holden, Patricia A

2009-01-15

Coastal urbanized areas in Southern California experience frequent beach water quality warnings in summer due to high concentrations of fecal indicator bacteria (FIB). Remediation can be difficult, as sources are often unknown. During two summers, we sampled three urbanized watersheds in Santa Barbara, CA at sites with historically high FIB concentrations to determine if human fecal matter was influencing water quality. By quantification of a human-specific Bacteroides marker (HBM), human waste was evidenced throughout both transects, and concentrations were highest in the discharges of several flowing storm drains. The HBM concentrations in storm drain discharges varied by up to 5 orders of magnitude on the same day. While the exact points of entry into the storm drain systems were not definitively determined, further inspection of the drain infrastructure suggested exfiltrating sanitary sewers as possible sources. The HBM and FIB concentrations were not consistently correlated, although the exclusive occurrence of high HBM concentrations with high FIB concentrations warrants the use of FIB analyses for a first tier of sampling. The association of human fecal pollution with dry weather drainage could be a window into a larger problem for other urbanized coastal areas with Mediterranean-type climates.

Efficacy of topical application of human breast milk on atopic eczema healing among infants: a randomized clinical trial.

PubMed

Kasrae, Hengameh; Amiri Farahani, Leila; Yousefi, Parsa

2015-08-01

Infant atopic eczema is an inflammatory lesion usually involving the epidermis of the skin. About 50% of infants are affected by this lesion in the first years of their lives. Studies show human breast milk (HBM) as a preventive measure and effective treatment of some sores and infections. This article evaluates the short-term efficacy of HBM versus hydrocortisone 1% ointment in infants with mild to moderate atopic dermatitis (AD). We conducted a randomized clinical trial among infants with diagnosed AD within a pediatrics unit. The majority of AD cases in both groups were considered moderate severity. There were no significant differences between these two groups at days 0, 7, 14, and 21, and the interventions of both groups were found to have the same effects. The external validity and consequently the ability to generalize the findings may be diminished as this study was conducted in a single site. Owing to HBM and the hydrocortisone 1% ointment providing the same results in the healing of AD, HBM was used because of low cost and accessibility. © 2015 The International Society of Dermatology.

Adherence barriers and facilitators for cervical screening amongst currently disadvantaged women in the greater Cape Town region of South Africa

PubMed Central

De Abreu, Chantelle; Horsfall, Hannah

2013-01-01

Abstract Background In South Africa cervical cancer is the second most commonly occurring cancer amongst women, and black African women have the highest risk of developing this disease. Unfortunately, the majority of South African women do not adhere to recommended regular cervical screening. Objectives The purpose of this research was to explore the perceptions, experiences and knowledge regarding cervical screening of disadvantaged women in two informal settlements in South African urban areas. Method The Health Belief Model (HBM) provided a theoretical framework for this study. Four focus groups (n = 21) were conducted, using questions derived from the HBM, and thematic analysis was used to analyse the data. The ages of the women who participated ranged from 21 to 53 years. Results The analysis revealed lack of knowledge about screening as a key structural barrier to treatment. Other structural barriers were: time, age at which free screening is available, and health education. The psychosocial barriers that were identified included: fear of the screening procedure and of the stigmatisation in attending screening. The presence of physical symptoms, the perception that screening provides symptom relief, HIV status, and the desire to know one's physical health status were identified as facilitators of cervical screening adherence. Conclusion This knowledge has the potential to inform healthcare policy and services in South Africa. As globalisation persists and individuals continue to immigrate or seek refugee status in foreign countries, increased understanding and knowledge is required for successful acculturation and integration. Developed countries may therefore also benefit from research findings in developing countries.

Use of hand-held computers to determine the relative contribution of different cognitive, attitudinal, social, and organizational factors on health care workers' decision to decontaminate hands.

PubMed

Lee, Karen; Burnett, Emma; Morrison, Kenny; Ricketts, Ian

2014-02-01

Observational and survey methods have limitations in measuring hand hygiene behavior. The ability of a personal digital assistant to anonymously gather data at the point of decision making could potentially address these. Participants were provided with a personal digital assistant to be used for three 2-hour periods and asked to rate influential factors of the Health Belief Model (HBM). Participants were also required to enter what they thought they should do and what they actually did. A total of 741 hand hygiene opportunities was recorded. All HBM constructs were higher for hand hygiene opportunities where there was compliance versus noncompliance, with a significant difference for patient pressure, my risk, perceived benefits, perceived seriousness, and availability of good facilities. Only 20% of doctors, 28% of nurses, and 66% of physiotherapists always did what they thought they should. There was no correlation between self-reported and actual compliance. The HBM appeared to be a useful theoretical framework. Surprisingly, participants rated their compliance as high despite having recorded instances where they did not do what they thought they should do. This suggests that staff may have a different definition of compliance than strict observation of the guidelines. Copyright © 2014 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

A psychosocial approach to dentistry for the underserved: incorporating theory into practice.

PubMed

Flaer, Paul J; Younis, Mustafa Z; Benjamin, Paul L; Al Hajeri, Maha

2010-01-01

Dentistry for the underserved is more than an egalitarian social issue--it is a key factor in the health and social progress of our nation. The first signs or manifestations of several diseases such as varicella (i.e., chicken pox and shingles), STDs, and influenza become apparent in the oral cavity. The value of access to quality dentistry is an immeasurable factor in maintaining general medical health of people and fulfilling their psychosocial needs of pain reduction and enhanced cosmetics. In the United States, for the most part, only the middle and upper classes receive non-extraction, restorative, and prosthetic dentistry that is economically within their ability to pay. In addition, uninsured and poverty-level individuals often must face overwhelming long waiting lists, unnecessary referrals, lack of choice, and bureaucratic hurdles when seeking primary dental care. Therefore, it seems pertinent to put forth the question: What are the critical values and beliefs of psychosocial theory that can underscore the practice of dentistry for underserved populations in the United States? The widely employed public health theory, the health belief model (HBM), is applied to evaluate psychosocial factors in dental care for the underserved. The HBM is used to predict and explain behavioral changes in dental health and associated belief patterns. The HBM as applied to dentistry for the underserved predicts self-perceptions of susceptibility and seriousness of dental disease, health status, cues to action, and self-efficacy. Furthermore, patients can make judgments about benefits, costs, and risks of dental treatment. A theoretical approach to dentistry employing the HBM, mediated by values and culture, can provide significant insights into patient thinking, beliefs, and perceptions. These insights can mediate access to and use of primary care dental services by underserved populations. Evidence-based practice (i.e., based on research using the scientific method) has been put forth as the future of modern dentistry. However, the practice of dentistry need not just be evidence-based, but have its roots clearly grounded in theory.

Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models.

PubMed

Daunizeau, J; Friston, K J; Kiebel, S J

2009-11-01

In this paper, we describe a general variational Bayesian approach for approximate inference on nonlinear stochastic dynamic models. This scheme extends established approximate inference on hidden-states to cover: (i) nonlinear evolution and observation functions, (ii) unknown parameters and (precision) hyperparameters and (iii) model comparison and prediction under uncertainty. Model identification or inversion entails the estimation of the marginal likelihood or evidence of a model. This difficult integration problem can be finessed by optimising a free-energy bound on the evidence using results from variational calculus. This yields a deterministic update scheme that optimises an approximation to the posterior density on the unknown model variables. We derive such a variational Bayesian scheme in the context of nonlinear stochastic dynamic hierarchical models, for both model identification and time-series prediction. The computational complexity of the scheme is comparable to that of an extended Kalman filter, which is critical when inverting high dimensional models or long time-series. Using Monte-Carlo simulations, we assess the estimation efficiency of this variational Bayesian approach using three stochastic variants of chaotic dynamic systems. We also demonstrate the model comparison capabilities of the method, its self-consistency and its predictive power.

Integrating health belief model and technology acceptance model: an investigation of health-related internet use.

PubMed

Ahadzadeh, Ashraf Sadat; Pahlevan Sharif, Saeed; Ong, Fon Sim; Khong, Kok Wei

2015-02-19

Today, people use the Internet to satisfy health-related information and communication needs. In Malaysia, Internet use for health management has become increasingly significant due to the increase in the incidence of chronic diseases, in particular among urban women and their desire to stay healthy. Past studies adopted the Technology Acceptance Model (TAM) and Health Belief Model (HBM) independently to explain Internet use for health-related purposes. Although both the TAM and HBM have their own merits, independently they lack the ability to explain the cognition and the related mechanism in which individuals use the Internet for health purposes. This study aimed to examine the influence of perceived health risk and health consciousness on health-related Internet use based on the HBM. Drawing on the TAM, it also tested the mediating effects of perceived usefulness of the Internet for health information and attitude toward Internet use for health purposes for the relationship between health-related factors, namely perceived health risk and health consciousness on health-related Internet use. Data obtained for the current study were collected using purposive sampling; the sample consisted of women in Malaysia who had Internet access. The partial least squares structural equation modeling method was used to test the research hypotheses developed. Perceived health risk (β=.135, t1999=2.676) and health consciousness (β=.447, t1999=9.168) had a positive influence on health-related Internet use. Moreover, perceived usefulness of the Internet and attitude toward Internet use for health-related purposes partially mediated the influence of health consciousness on health-related Internet use (β=.025, t1999=3.234), whereas the effect of perceived health risk on health-related Internet use was fully mediated by perceived usefulness of the Internet and attitude (β=.029, t1999=3.609). These results suggest the central role of perceived usefulness of the Internet and attitude toward Internet use for health purposes for women who were health conscious and who perceived their health to be at risk. The integrated model proposed and tested in this study shows that the HBM, when combined with the TAM, is able to predict Internet use for health purposes. For women who subjectively evaluate their health as vulnerable to diseases and are concerned about their health, cognition beliefs in and positive affective feelings about the Internet come into play in determining the use of health-related Internet use. Furthermore, this study shows that engaging in health-related Internet use is a proactive behavior rather than a reactive behavior, suggesting that TAM dimensions have a significant mediating role in Internet health management.

Integrating Health Belief Model and Technology Acceptance Model: An Investigation of Health-Related Internet Use

PubMed Central

2015-01-01

Background Today, people use the Internet to satisfy health-related information and communication needs. In Malaysia, Internet use for health management has become increasingly significant due to the increase in the incidence of chronic diseases, in particular among urban women and their desire to stay healthy. Past studies adopted the Technology Acceptance Model (TAM) and Health Belief Model (HBM) independently to explain Internet use for health-related purposes. Although both the TAM and HBM have their own merits, independently they lack the ability to explain the cognition and the related mechanism in which individuals use the Internet for health purposes. Objective This study aimed to examine the influence of perceived health risk and health consciousness on health-related Internet use based on the HBM. Drawing on the TAM, it also tested the mediating effects of perceived usefulness of the Internet for health information and attitude toward Internet use for health purposes for the relationship between health-related factors, namely perceived health risk and health consciousness on health-related Internet use. Methods Data obtained for the current study were collected using purposive sampling; the sample consisted of women in Malaysia who had Internet access. The partial least squares structural equation modeling method was used to test the research hypotheses developed. Results Perceived health risk (β=.135, t 1999=2.676) and health consciousness (β=.447, t 1999=9.168) had a positive influence on health-related Internet use. Moreover, perceived usefulness of the Internet and attitude toward Internet use for health-related purposes partially mediated the influence of health consciousness on health-related Internet use (β=.025, t 1999=3.234), whereas the effect of perceived health risk on health-related Internet use was fully mediated by perceived usefulness of the Internet and attitude (β=.029, t 1999=3.609). These results suggest the central role of perceived usefulness of the Internet and attitude toward Internet use for health purposes for women who were health conscious and who perceived their health to be at risk. Conclusions The integrated model proposed and tested in this study shows that the HBM, when combined with the TAM, is able to predict Internet use for health purposes. For women who subjectively evaluate their health as vulnerable to diseases and are concerned about their health, cognition beliefs in and positive affective feelings about the Internet come into play in determining the use of health-related Internet use. Furthermore, this study shows that engaging in health-related Internet use is a proactive behavior rather than a reactive behavior, suggesting that TAM dimensions have a significant mediating role in Internet health management. PMID:25700481

Integration of Five Health Behaviour Models: Common Strengths and Unique Contributions to Understanding Condom Use

PubMed Central

Reid, Allecia E.; Aiken, Leona S.

2011-01-01

The purpose of this research was to select from the health belief model (HBM), theories of reasoned action (TRA) and planned behaviour (TPB), information-motivation-behavioural skills model (IMB), and social cognitive theory (SCT) the strongest longitudinal predictors of women’s condom use and to combine these constructs into a single integrated model of condom use. The integrated model was evaluated for prediction of condom use among young women who had steady versus casual partners. At Time 1, all constructs of the five models and condom use were assessed in an initial and a replication sample (n= 193, n= 161). Condom use reassessed 8 weeks later (Time 2) served as the main outcome. Information from IMB, perceived susceptibility, benefits, and barriers from HBM, self-efficacy and self-evaluative expectancies from SCT, and partner norm and attitudes from TPB served as indirect or direct predictors of condom use. All paths replicated across samples. Direct predictors of behaviour varied with relationship status: self-efficacy significantly predicted condom use for women with casual partners, while attitude and partner norm predicted for those with steady partners. Integrated psychosocial models, rich in constructs and relationships drawn from multiple theories of behaviour, may provide a more complete characterization of health protective behaviour. PMID:21678166

Neonatal High Bone Mass With First Mutation of the NF-κB Complex: Heterozygous De Novo Missense (p.Asp512Ser) RELA (Rela/p65).

PubMed

Frederiksen, Anja L; Larsen, Martin J; Brusgaard, Klaus; Novack, Deborah V; Knudsen, Peter Juel Thiis; Schrøder, Henrik Daa; Qiu, Weimin; Eckhardt, Christina; McAlister, William H; Kassem, Moustapha; Mumm, Steven; Frost, Morten; Whyte, Michael P

2016-01-01

Heritable disorders that feature high bone mass (HBM) are rare. The etiology is typically a mutation(s) within a gene that regulates the differentiation and function of osteoblasts (OBs) or osteoclasts (OCs). Nevertheless, the molecular basis is unknown for approximately one-fifth of such entities. NF-κB signaling is a key regulator of bone remodeling and acts by enhancing OC survival while impairing OB maturation and function. The NF-κB transcription complex comprises five subunits. In mice, deletion of the p50 and p52 subunits together causes osteopetrosis (OPT). In humans, however, mutations within the genes that encode the NF-κB complex, including the Rela/p65 subunit, have not been reported. We describe a neonate who died suddenly and unexpectedly and was found at postmortem to have HBM documented radiographically and by skeletal histopathology. Serum was not available for study. Radiographic changes resembled malignant OPT, but histopathological investigation showed morphologically normal OCs and evidence of intact bone resorption excluding OPT. Furthermore, mutation analysis was negative for eight genes associated with OPT or HBM. Instead, accelerated bone formation appeared to account for the HBM. Subsequently, trio-based whole exome sequencing revealed a heterozygous de novo missense mutation (c.1534_1535delinsAG, p.Asp512Ser) in exon 11 of RELA encoding Rela/p65. The mutation was then verified using bidirectional Sanger sequencing. Lipopolysaccharide stimulation of patient fibroblasts elicited impaired NF-κB responses compared with healthy control fibroblasts. Five unrelated patients with unexplained HBM did not show a RELA defect. Ours is apparently the first report of a mutation within the NF-κB complex in humans. The missense change is associated with neonatal osteosclerosis from in utero increased OB function rather than failed OC action. These findings demonstrate the importance of the Rela/p65 subunit within the NF-κB pathway for human skeletal homeostasis and represent a new genetic cause of HBM. © 2015 American Society for Bone and Mineral Research.

Predicting intention to attend and actual attendance at a universal parent-training programme: a comparison of social cognition models.

PubMed

Thornton, Sarah; Calam, Rachel

2011-07-01

The predictive validity of the Health Belief Model (HBM) and the Theory of Planned Behaviour (TPB) were examined in relation to 'intention to attend' and 'actual attendance' at a universal parent-training intervention for parents of children with behavioural difficulties. A validation and reliability study was conducted to develop two questionnaires (N = 108 parents of children aged 4-7).These questionnaires were then used to investigate the predictive validity of the two models in relation to 'intention to attend' and 'actual attendance' at a parent-training intervention ( N = 53 parents of children aged 4-7). Both models significantly predicted 'intention to attend a parent-training group'; however, the TPB accounted for more variance in the outcome variable compared to the HBM. Preliminary investigations highlighted that attendees were more likely to intend to attend the groups, have positive attitudes towards the groups, perceive important others as having positive attitudes towards the groups, and report elevated child problem behaviour scores. These findings provide useful information regarding the belief-based factors that affect attendance at universal parent-training groups. Possible interventions aimed at increasing 'intention to attend' and 'actual attendance' at parent-training groups are discussed.

A Framework for Engaging Parents in Prevention

ERIC Educational Resources Information Center

Randolph, Karen A.; Fincham, Frank; Radey, Melissa

2009-01-01

The literature on engaging families in prevention programs is informed by the Health Beliefs Model (HBM), Theory of Reasoned Action (TRA), and Family Systems theory. Although useful, these frameworks have not facilitated the development of prevention-based practice strategies that recognize different levels of prevention (i.e., universal,…

Application of the health belief model in promotion of self-care in heart failure patients.

PubMed

Baghianimoghadam, Mohammad Hosein; Shogafard, Golamreza; Sanati, Hamid Reza; Baghianimoghadam, Behnam; Mazloomy, Seyed Saeed; Askarshahi, Mohsen

2013-01-01

Heart failure (HF) is a condition due to a problem with the structure or function of the heart impairs its ability to supply sufficient blood flow to meet the body's needs. In developing countries, around 2% of adults suffer from heart failure, but in people over the age of 65, this rate increases to 6-10%. In Iran, around 3.3% of adults suffer from heart failure. The Health Belief Model (HBM) is one of the most widely used models in public health theoretical framework. This was a cohort experimental study, in which education as intervention factor was presented to case group. 180 Heart failure patients were randomly selected from patients who were referred to the Shahid Rajaee center of Heart Research in Tehran and allocated to two groups (90 patients in the case group and 90 in the control group). HBM was used to compare health behaviors. The questionnaire included 69 questions. All data were collected before and 2 months after intervention. About 38% of participants don't know what, the heart failure is and 43% don't know that using the salt is not suitable for them. More than 40% of participants didn't weigh any time their selves. There was significant differences between the mean grades score of variables (perceived susceptibility, perceived threat, knowledge, Perceived benefits, Perceived severity, self-efficacy Perceived barriers, cues to action, self- behavior) in the case and control groups after intervention that was not significant before it. Based on our study and also many other studies, HBM has the potential to be used as a tool to establish educational programs for individuals and communities. Therefore, this model can be used effectively to prevent different diseases and their complications including heart failure. © 2013 Tehran University of Medical Sciences. All rights reserved.

A Bayesian network approach to the database search problem in criminal proceedings

PubMed Central

2012-01-01

Background The ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method’s graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication. PMID:22849390

Novel method for metalloproteins determination in human breast milk by size exclusion chromatography coupled to inductively coupled plasma mass spectrometry.

PubMed

Acosta, Mariano; Torres, Sabier; Mariño-Repizo, Leonardo; Martinez, Luis D; Gil, Raúl A

2018-06-02

Levels of essential metals in human breast milk (HBM) have been determined by different analytical techniques, but there is few woks about human whey milk fractions. However, the current trend lies in metalloproteomic and identification of different metalloproteins. In this sense, native separative techniques (N-PAGE and SEC) coupled to ICP-MS provide us with valuable information. Besides it is necessary the development of new methodologies in order to determine with accuracy and precision the profile of such metals and metalloproteins in the different whey protein fractions of HBM. Thus, the aim of this work was to develop a new method for metals and metalloproteins determination by SEC-ICP-MS in whey protein fractions of HBM. Human whey fractions were obtained of HBM samples by ultracentrifugation. Then, protein fractions of whey milk were separated by SEC coupled to ICP-MS for metalloproteins and Mn, Co, Cu and Se quantification. Besides, protein profile of whey milk was determined by N-PAGE and computer assisted image analysis. SEC-ICP-MS results indicated that first and second protein fractions showed detectable levels of the Mn, Co, Cu, and Se. Protein profile determined by N-PAGE and image analysis showed that molecular weight of protein fractions ranged between 68,878-1,228.277 Da. In this work, metalloproteins were analyzed by SEC coupled to ICP-MS, with adequate sensitivity and accuracy. Our study has shown the presence of Mn, Co, Cu and Se bound to two protein fractions in whey milk of HBM. Metals levels analyzed were within the ranges reported in the literature. Copyright © 2018 Elsevier B.V. All rights reserved.

Inference of emission rates from multiple sources using Bayesian probability theory.

PubMed

Yee, Eugene; Flesch, Thomas K

2010-03-01

The determination of atmospheric emission rates from multiple sources using inversion (regularized least-squares or best-fit technique) is known to be very susceptible to measurement and model errors in the problem, rendering the solution unusable. In this paper, a new perspective is offered for this problem: namely, it is argued that the problem should be addressed as one of inference rather than inversion. Towards this objective, Bayesian probability theory is used to estimate the emission rates from multiple sources. The posterior probability distribution for the emission rates is derived, accounting fully for the measurement errors in the concentration data and the model errors in the dispersion model used to interpret the data. The Bayesian inferential methodology for emission rate recovery is validated against real dispersion data, obtained from a field experiment involving various source-sensor geometries (scenarios) consisting of four synthetic area sources and eight concentration sensors. The recovery of discrete emission rates from three different scenarios obtained using Bayesian inference and singular value decomposition inversion are compared and contrasted.

Determining distinct circuit in complete graphs using permutation

NASA Astrophysics Data System (ADS)

Karim, Sharmila; Ibrahim, Haslinda; Darus, Maizon Mohd

2017-11-01

A Half Butterfly Method (HBM) is a method introduced to construct the distinct circuits in complete graphs where used the concept of isomorphism. The Half Butterfly Method was applied in the field of combinatorics such as in listing permutations of n elements. However the method of determining distinct circuit using HBM for n > 4 is become tedious. Thus, in this paper, we present the method of generating distinct circuit using permutation.

Bayesian Monte Carlo and Maximum Likelihood Approach for ...

EPA Pesticide Factsheets

Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood estimation (BMCML) to calibrate a lake oxygen recovery model. We first derive an analytical solution of the differential equation governing lake-averaged oxygen dynamics as a function of time-variable wind speed. Statistical inferences on model parameters and predictive uncertainty are then drawn by Bayesian conditioning of the analytical solution on observed daily wind speed and oxygen concentration data obtained from an earlier study during two recovery periods on a eutrophic lake in upper state New York. The model is calibrated using oxygen recovery data for one year and statistical inferences were validated using recovery data for another year. Compared with essentially two-step, regression and optimization approach, the BMCML results are more comprehensive and performed relatively better in predicting the observed temporal dissolved oxygen levels (DO) in the lake. BMCML also produced comparable calibration and validation results with those obtained using popular Markov Chain Monte Carlo technique (MCMC) and is computationally simpler and easier to implement than the MCMC. Next, using the calibrated model, we derive an optimal relationship between liquid film-transfer coefficien

Computational statistics using the Bayesian Inference Engine

NASA Astrophysics Data System (ADS)

Weinberg, Martin D.

2013-09-01

This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimized software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organize and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasizes hybrid tempered Markov chain Monte Carlo schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE implements a full persistence or serialization system that stores the full byte-level image of the running inference and previously characterized posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU General Public License.

Bayesian estimation of dynamic matching function for U-V analysis in Japan

NASA Astrophysics Data System (ADS)

Kyo, Koki; Noda, Hideo; Kitagawa, Genshiro

2012-05-01

In this paper we propose a Bayesian method for analyzing unemployment dynamics. We derive a Beveridge curve for unemployment and vacancy (U-V) analysis from a Bayesian model based on a labor market matching function. In our framework, the efficiency of matching and the elasticities of new hiring with respect to unemployment and vacancy are regarded as time varying parameters. To construct a flexible model and obtain reasonable estimates in an underdetermined estimation problem, we treat the time varying parameters as random variables and introduce smoothness priors. The model is then described in a state space representation, enabling the parameter estimation to be carried out using Kalman filter and fixed interval smoothing. In such a representation, dynamic features of the cyclic unemployment rate and the structural-frictional unemployment rate can be accurately captured.

Estimation of methylmercury intake doses in the South Korea population using a PBPK model

EPA Science Inventory

Recently, South Korea has measured total mercury (Hg) in blood as part of the Korean National Environmental Health Survey (koNEHS) in 6311 subjects representing Korean general population. About 25% of the biomarker measurements were above the Germany HBM1 of 5 µg Hg/L; and about...

Predictors of Medication Adherence in an AIDS Clinical Trial: Patient and Clinician Perceptions

ERIC Educational Resources Information Center

Cox, Lisa E.

2009-01-01

This article presents data from an AIDS clinical trial that evaluated 238 (60 percent nonwhite) patients infected with HIV and their clinician's perceptions of medication adherence and visit attendance in relationship to lifestyle, psychosocial, and health belief model (HBM) variables. Twelve sites collected data via a prospective, multisite…

How can the health belief model and self-determination theory predict both influenza vaccination and vaccination intention ? A longitudinal study among university students.

PubMed

Fall, Estelle; Izaute, Marie; Chakroun-Baggioni, Nadia

2018-06-01

Background and objective Seasonal influenza is frequent among students and often responsible for impaired academic performance and lower levels of general health. However, the vaccination rate in this population is very low. As the seasonal influenza vaccine is not compulsory in France, it is important to improve the vaccination uptake by identifying predictors of both intention and behaviour. This study investigated the effect of decisional balance, motivation and self-efficacy on vaccination acceptance using the Extended Health Belief Model (HBM) and Self-Determination Theory (SDT). Design and Main Outcome Measures University students were invited to fill in an online survey to answer questions about their influenza vaccination intention, and HBM and SDT constructs. A one-year longitudinal follow-up study investigated vaccination behaviour. Results Autonomous motivation and self-efficacy significantly influenced the intention to have the influenza vaccine, and vaccine behaviour at one-year follow-up. Intention predicted a significant proportion of variation (51%) in behaviour, and mediated the effect of these predictors on vaccination behaviour. Conclusion These results suggest that motivation concepts of the Self-Determination Theory can be adequately combined with the Health Belief Model to understand vaccination behaviour.

Application of the Health Belief Model in a study on parents' intentions to utilize prenatal diagnosis of cleft lip and/or palate.

PubMed

Sagi, M; Shiloh, S; Cohen, T

1992-10-01

Parents of children with cleft lip and/or palate (42 women and 35 men) participated in a study on intentions to use prenatal diagnosis of cleft by ultrasound in subsequent pregnancies. Based on the Health Belief Model (HBM) [Rosenstock, 1974], parents' cognitions on 4 factors were measured by questionnaires: "susceptibility" and "severity perceptions," "benefits" and "barriers" evaluations. Most parents perceived the defect as severe. Over-estimation of recurrence risks was predominant even among parents who had received genetic counseling. Results showed that most parents intend to utilize prenatal diagnosis but do not intend to abort an affected fetus. Subjects' reported reasons represented 3 thematic categories: cognitive (the need to know), emotional, and behavioral. Parents' intentions to diagnose and to terminate were related to the factors predicted by the HBM model. Regression analyses indicated that 38% of the variance in intentions to diagnose and 56% of the variance in intentions to terminate could be explained by the studied variables. The best predictor of both intentions was the perceived benefits of the diagnosis. Implications of these findings for genetic counseling are discussed.

Quantifying uncertainty in Bayesian calibrated animal-to-human PBPK models with informative prior distributions

EPA Science Inventory

Understanding and quantifying the uncertainty of model parameters and predictions has gained more interest in recent years with the increased use of computational models in chemical risk assessment. Fully characterizing the uncertainty in risk metrics derived from linked quantita...

Foundational workplace safety and health competencies for the emerging workforce.

PubMed

Okun, Andrea H; Guerin, Rebecca J; Schulte, Paul A

2016-12-01

Young workers (aged 15-24) suffer disproportionately from workplace injuries, with a nonfatal injury rate estimated to be two times higher than among workers age 25 or over. These workers make up approximately 9% of the U.S. workforce and studies have shown that nearly 80% of high school students work at some point during high school. Although young worker injuries are a pressing public health problem, the critical knowledge and skills needed to prepare youth for safe and healthy work are missing from most frameworks used to prepare the emerging U.S. workforce. A framework of foundational workplace safety and health knowledge and skills (the NIOSH 8 Core Competencies) was developed based on the Health Belief Model (HBM). The proposed NIOSH Core Competencies utilize the HBM to provide a framework for foundational workplace safety and health knowledge and skills. An examination of how these competencies and the HBM apply to actions that workers take to protect themselves is provided. The social and physical environments that influence these actions are also discussed. The NIOSH 8 Core Competencies, grounded in one of the most widely used health behavior theories, fill a critical gap in preparing the emerging U.S. workforce to be cognizant of workplace risks. Integration of the NIOSH 8 Core Competencies into school curricula is one way to ensure that every young person has the foundational workplace safety and health knowledge and skills to participate in, and benefit from, safe and healthy work. Published by Elsevier Ltd.

Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes.

PubMed

Yang, Jingjing; Cox, Dennis D; Lee, Jong Soo; Ren, Peng; Choi, Taeryon

2017-12-01

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo methods. Compared to the standard Bayesian inference that suffers serious computational burden and instability in analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results to those obtainable by the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids when the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes. © 2017, The International Biometric Society.

Implementing an exercise-training programme to prevent lower-limb injuries: considerations for the development of a randomised controlled trial intervention delivery plan.

PubMed

Finch, Caroline F; White, Peta; Twomey, Dara; Ullah, Shahid

2011-08-01

To identify important considerations for the delivery of an exercise training intervention in a randomised controlled trial to maximise subsequent participation in that randomised controlled trial and intervention uptake. A cross-sectional survey, with a theoretical basis derived from the Health Belief Model (HBM) and the Reach, Efficacy/Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework. 374 male senior Australian Football players, aged 17-38 years. Beliefs about lower-limb injury causation/prevention, and the relative value of exercise training for performance and injury prevention. The data are interpreted within HBM constructs and implications for subsequent intervention implementation considered within the RE-AIM framework. Ordinal logistic regression compared belief scores across player characteristics. 74.4% of players agreed that doing specific exercises during training would reduce their risk of lower-limb injury and would be willing to undertake them. However, 64.1% agreed that training should focus more on improving game performance than injury prevention. Younger players (both in terms of age and playing experience) generally had more positive views. Players were most supportive of kicking (98.9%) and ball-handling (97.0%) skills for performance and warm-up runs and cool-downs (both 91.5%) for injury prevention. Fewer than three-quarters of all players believed that balance (69.2%), landing (71.3%) or cutting/stepping (72.8) training had injury-prevention benefits. Delivery of future exercise training programmes for injury prevention aimed at these players should be implemented as part of routine football activities and integrated with those as standard practice, as a means of associating them with training benefits for this sport.

Modeling SF-6D Hong Kong standard gamble health state preference data using a nonparametric Bayesian method.

PubMed

Kharroubi, Samer A; Brazier, John E; McGhee, Sarah

2013-01-01

This article reports on the findings from applying a recently described approach to modeling health state valuation data and the impact of the respondent characteristics on health state valuations. The approach applies a nonparametric model to estimate a Bayesian six-dimensional health state short form (derived from short-form 36 health survey) health state valuation algorithm. A sample of 197 states defined by the six-dimensional health state short form (derived from short-form 36 health survey)has been valued by a representative sample of the Hong Kong general population by using standard gamble. The article reports the application of the nonparametric model and compares it to the original model estimated by using a conventional parametric random effects model. The two models are compared theoretically and in terms of empirical performance. Advantages of the nonparametric model are that it can be used to predict scores in populations with different distributions of characteristics than observed in the survey sample and that it allows for the impact of respondent characteristics to vary by health state (while ensuring that full health passes through unity). The results suggest an important age effect with sex, having some effect, but the remaining covariates having no discernible effect. The nonparametric Bayesian model is argued to be more theoretically appropriate than previously used parametric models. Furthermore, it is more flexible to take into account the impact of covariates. Copyright © 2013, International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc.

A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions.

PubMed

Peressutti, Devis; Penney, Graeme P; Housden, R James; Kolbitsch, Christoph; Gomez, Alberto; Rijkhorst, Erik-Jan; Barratt, Dean C; Rhode, Kawal S; King, Andrew P

2013-05-01

In image-guided cardiac interventions, respiratory motion causes misalignments between the pre-procedure roadmap of the heart used for guidance and the intra-procedure position of the heart, reducing the accuracy of the guidance information and leading to potentially dangerous consequences. We propose a novel technique for motion-correcting the pre-procedural information that combines a probabilistic MRI-derived affine motion model with intra-procedure real-time 3D echocardiography (echo) images in a Bayesian framework. The probabilistic model incorporates a measure of confidence in its motion estimates which enables resolution of the potentially conflicting information supplied by the model and the echo data. Unlike models proposed so far, our method allows the final motion estimate to deviate from the model-produced estimate according to the information provided by the echo images, so adapting to the complex variability of respiratory motion. The proposed method is evaluated using gold-standard MRI-derived motion fields and simulated 3D echo data for nine volunteers and real 3D live echo images for four volunteers. The Bayesian method is compared to 5 other motion estimation techniques and results show mean/max improvements in estimation accuracy of 10.6%/18.9% for simulated echo images and 20.8%/41.5% for real 3D live echo data, over the best comparative estimation method. Copyright © 2013 Elsevier B.V. All rights reserved.

Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2014-06-01

The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an uncertainty class of feature-label distributions to which the true, but unknown, feature-distribution belongs, can facilitate accurate error estimation (in the mean-square sense) in circumstances where accurate completely model-free error estimation is impossible. This paper provides analytic asymptotically exact finite-sample approximations for various performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE) error estimator in the case of linear discriminant analysis (LDA) in the multivariate Gaussian model. These performance metrics include the first, second, and cross moments of the Bayesian MMSE error estimator with the true error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator. We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a Bayesian setting, which enables us to derive asymptotic expressions of the desired performance metrics. From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples. Various examples illustrate the behavior of these approximations and their use in determining the necessary sample size to achieve a desired RMS. The Supplementary Material contains derivations for some equations and added figures.

Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

NASA Astrophysics Data System (ADS)

Krishnanathan, Kirubhakaran; Anderson, Sean R.; Billings, Stephen A.; Kadirkamanathan, Visakan

2016-11-01

In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (1) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (2) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (3) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation.

Bayesian approach for counting experiment statistics applied to a neutrino point source analysis

NASA Astrophysics Data System (ADS)

Bose, D.; Brayeur, L.; Casier, M.; de Vries, K. D.; Golup, G.; van Eijndhoven, N.

2013-12-01

In this paper we present a model independent analysis method following Bayesian statistics to analyse data from a generic counting experiment and apply it to the search for neutrinos from point sources. We discuss a test statistic defined following a Bayesian framework that will be used in the search for a signal. In case no signal is found, we derive an upper limit without the introduction of approximations. The Bayesian approach allows us to obtain the full probability density function for both the background and the signal rate. As such, we have direct access to any signal upper limit. The upper limit derivation directly compares with a frequentist approach and is robust in the case of low-counting observations. Furthermore, it allows also to account for previous upper limits obtained by other analyses via the concept of prior information without the need of the ad hoc application of trial factors. To investigate the validity of the presented Bayesian approach, we have applied this method to the public IceCube 40-string configuration data for 10 nearby blazars and we have obtained a flux upper limit, which is in agreement with the upper limits determined via a frequentist approach. Furthermore, the upper limit obtained compares well with the previously published result of IceCube, using the same data set.

2D Bayesian automated tilted-ring fitting of disc galaxies in large H I galaxy surveys: 2DBAT

NASA Astrophysics Data System (ADS)

Oh, Se-Heon; Staveley-Smith, Lister; Spekkens, Kristine; Kamphuis, Peter; Koribalski, Bärbel S.

2018-01-01

We present a novel algorithm based on a Bayesian method for 2D tilted-ring analysis of disc galaxy velocity fields. Compared to the conventional algorithms based on a chi-squared minimization procedure, this new Bayesian-based algorithm suffers less from local minima of the model parameters even with highly multimodal posterior distributions. Moreover, the Bayesian analysis, implemented via Markov Chain Monte Carlo sampling, only requires broad ranges of posterior distributions of the parameters, which makes the fitting procedure fully automated. This feature will be essential when performing kinematic analysis on the large number of resolved galaxies expected to be detected in neutral hydrogen (H I) surveys with the Square Kilometre Array and its pathfinders. The so-called 2D Bayesian Automated Tilted-ring fitter (2DBAT) implements Bayesian fits of 2D tilted-ring models in order to derive rotation curves of galaxies. We explore 2DBAT performance on (a) artificial H I data cubes built based on representative rotation curves of intermediate-mass and massive spiral galaxies, and (b) Australia Telescope Compact Array H I data from the Local Volume H I Survey. We find that 2DBAT works best for well-resolved galaxies with intermediate inclinations (20° < i < 70°), complementing 3D techniques better suited to modelling inclined galaxies.

Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor under Thermal Overstress Conditions

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.; Celaya, Jose R.; Goebel, Kai; Biswas, Gautam

2012-01-01

Electrolytic capacitors are used in several applications ranging from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuator. Past experiences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress conditions during operations. This makes them good candidates for prognostics and health management. Model-based prognostics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of heal th and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation models for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters.

The use of biomarkers to describe plasma-, red cell-, and blood volume from a simple blood test.

PubMed

Lobigs, Louisa Margit; Sottas, Pierre-Edouard; Bourdon, Pitre Collier; Nikolovski, Zoran; El-Gingo, Mohamed; Varamenti, Evdokia; Peeling, Peter; Dawson, Brian; Schumacher, Yorck Olaf

2017-01-01

Plasma volume and red cell mass are key health markers used to monitor numerous disease states, such as heart failure, kidney disease, or sepsis. Nevertheless, there is currently no practically applicable method to easily measure absolute plasma or red cell volumes in a clinical setting. Here, a novel marker for plasma volume and red cell mass was developed through analysis of the observed variability caused by plasma volume shifts in common biochemical measures, selected based on their propensity to present with low variations over time. Once a month for 6 months, serum and whole blood samples were collected from 33 active males. Concurrently, the CO-rebreathing method was applied to determine target levels of hemoglobin mass (HbM) and blood volumes. The variability of 18 common chemistry markers and 27 Full Blood Count variables was investigated and matched to the observed plasma volume variation. After the removal of between-subject variations using a Bayesian model, multivariate analysis identified two sets of 8 and 15 biomarkers explaining 68% and 69% of plasma volume variance, respectively. The final multiparametric model contains a weighting function to allow for isolated abnormalities in single biomarkers. This proof-of-concept investigation describes a novel approach to estimate absolute vascular volumes, with a simple blood test. Despite the physiological instability of critically ill patients, it is hypothesized the model, with its multiparametric approach and weighting function, maintains the capacity to describe vascular volumes. This model has potential to transform volume management in clinical settings. Am. J. Hematol. 92:62-67, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A Bayes linear Bayes method for estimation of correlated event rates.

PubMed

Quigley, John; Wilson, Kevin J; Walls, Lesley; Bedford, Tim

2013-12-01

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well-known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates. © 2013 Society for Risk Analysis.

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

PubMed

Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

2010-12-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies conservation planning. Journal compilation © 2010 Society for Conservation Biology. No claim to original US government works.

Correlates of Cigarette Smoking among Male Chinese College Students in China--A Preliminary Study

ERIC Educational Resources Information Center

Li, Kaigang; Kay, Noy S.

2009-01-01

The main purpose of this preliminary study was to examine the association between four constructs of the Health Belief Model (HBM) (i.e. perceived severity of smoking-related health problems, perceived susceptibility to smoking-health related problems, perceived barriers to non-smoking and perceived benefits of non-smoking) and cigarette smoking …

Participation in Prevention Programs for Dating Violence: Beliefs about Relationship Violence and Intention to Participate

ERIC Educational Resources Information Center

Cornelius, Tara L.; Sullivan, Kieran T.; Wyngarden, Nicole; Milliken, Jennifer C.

2009-01-01

This study utilizes the Health Belief Model (HBM) to examine the factors related to the intention to participate in prevention programming for dating violence. Perceptions of susceptibility to future violence and the benefits of prevention programming appear to be the strongest predictors of participation in prevention programs. Perceptions of the…

An overview of human biomonitoring of environmental chemicals in the Canadian Health Measures Survey: 2007-2019.

PubMed

Haines, Douglas A; Saravanabhavan, Gurusankar; Werry, Kate; Khoury, Cheryl

2017-03-01

Human biomonitoring (HBM) is used to indicate and quantify exposure by measuring environmental chemicals, their metabolites or reaction products in biological specimens. The biomonitoring component of the Canadian Health Measures Survey (CHMS) is the most comprehensive initiative providing general population HBM data in Canada. The CHMS is an ongoing cross-sectional direct measures survey implemented in 2-year cycles. It provides nationally-representative data on health, nutritional status, environmental exposures, and related risks and protective characteristics. The survey follows a robust planning, design and sampling protocol as well as a comprehensive quality assurance and quality control regime implemented for all aspect of the survey to ensure the validity of the HBM results. HBM blood and urine data are available for CHMS cycles 1 (2007-2009), 2 (2009-2011) and 3 (2012-2013). Field collection has been completed for cycle 4 (2014-2015), with cycle 5 (2016-2017) in progress and cycle 6 planning (2018-2019) being finalized. Biomonitoring results for 279 chemicals are expected over the six cycles of the CHMS (220 in individual blood, urine or hair samples, and 59 in pooled serum samples). The chemicals include metals and trace elements, polychlorinated biphenyls (PCBs), organochlorines, flame retardants, perfluoroalkyl substances, volatile organic compounds (VOCs) and metabolites, environmental phenols, triclocarban, acrylamide, pesticides (e.g., triazines, carbamates, organophosphates, phenoxy, pyrethroids) and/or their metabolites, chlorophenols, polycyclic aromatic hydrocarbon (PAH) metabolites, phthalates and alternate plasticizer metabolites, and tobacco biomarkers. Approximately one half of the chemicals measured in individual blood and urine samples over the first three cycles were detected in more than 60% of samples. CHMS biomonitoring data have been used to establish baseline HBM concentrations in Canadians; inform public health, regulatory risk assessment and management decisions; and fulfil national and international reporting requirements. Concurrent efforts are underway in Canada to develop statistically- and risk-based concepts and tools to interpret biomonitoring data. Crown Copyright © 2016. Published by Elsevier GmbH. All rights reserved.

An Anticipatory Model of Cavitation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allgood, G.O.; Dress, W.B., Jr.; Hylton, J.O.

1999-04-05

The Anticipatory System (AS) formalism developed by Robert Rosen provides some insight into the problem of embedding intelligent behavior in machines. AS emulates the anticipatory behavior of biological systems. AS bases its behavior on its expectations about the near future and those expectations are modified as the system gains experience. The expectation is based on an internal model that is drawn from an appeal to physical reality. To be adaptive, the model must be able to update itself. To be practical, the model must run faster than real-time. The need for a physical model and the requirement that the modelmore » execute at extreme speeds, has held back the application of AS to practical problems. Two recent advances make it possible to consider the use of AS for practical intelligent sensors. First, advances in transducer technology make it possible to obtain previously unavailable data from which a model can be derived. For example, acoustic emissions (AE) can be fed into a Bayesian system identifier that enables the separation of a weak characterizing signal, such as the signature of pump cavitation precursors, from a strong masking signal, such as a pump vibration feature. The second advance is the development of extremely fast, but inexpensive, digital signal processing hardware on which it is possible to run an adaptive Bayesian-derived model faster than real-time. This paper reports the investigation of an AS using a model of cavitation based on hydrodynamic principles and Bayesian analysis of data from high-performance AE sensors.« less

Bayesian Inference and Application of Robust Growth Curve Models Using Student's "t" Distribution

ERIC Educational Resources Information Center

Zhang, Zhiyong; Lai, Keke; Lu, Zhenqiu; Tong, Xin

2013-01-01

Despite the widespread popularity of growth curve analysis, few studies have investigated robust growth curve models. In this article, the "t" distribution is applied to model heavy-tailed data and contaminated normal data with outliers for growth curve analysis. The derived robust growth curve models are estimated through Bayesian…

Joint three-dimensional inversion of coupled groundwater flow and heat transfer based on automatic differentiation: sensitivity calculation, verification, and synthetic examples

NASA Astrophysics Data System (ADS)

Rath, V.; Wolf, A.; Bücker, H. M.

2006-10-01

Inverse methods are useful tools not only for deriving estimates of unknown parameters of the subsurface, but also for appraisal of the thus obtained models. While not being neither the most general nor the most efficient methods, Bayesian inversion based on the calculation of the Jacobian of a given forward model can be used to evaluate many quantities useful in this process. The calculation of the Jacobian, however, is computationally expensive and, if done by divided differences, prone to truncation error. Here, automatic differentiation can be used to produce derivative code by source transformation of an existing forward model. We describe this process for a coupled fluid flow and heat transport finite difference code, which is used in a Bayesian inverse scheme to estimate thermal and hydraulic properties and boundary conditions form measured hydraulic potentials and temperatures. The resulting derivative code was validated by comparison to simple analytical solutions and divided differences. Synthetic examples from different flow regimes demonstrate the use of the inverse scheme, and its behaviour in different configurations.

Heuristics as Bayesian inference under extreme priors.

PubMed

Parpart, Paula; Jones, Matt; Love, Bradley C

2018-05-01

Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

A Prior for Neural Networks utilizing Enclosing Spheres for Normalization

NASA Astrophysics Data System (ADS)

v. Toussaint, U.; Gori, S.; Dose, V.

2004-11-01

Neural Networks are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand this flexibility can cause over-fitting and can hamper the generalization properties of neural networks. Many approaches to regularize NN have been suggested but most of them based on ad-hoc arguments. Employing the principle of transformation invariance we derive a general prior in accordance with the Bayesian probability theory for a class of feedforward networks. Optimal networks are determined by Bayesian model comparison verifying the applicability of this approach.

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian; Punjabi, Naresh M.

2013-01-01

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis. PMID:22241689

The influence of spirituality and religiosity on breast cancer screening delay in African American women: application of the Theory of Reasoned Action and Planned Behavior (TRA/TPB).

PubMed

Gullate, Mary

2006-01-01

African American women (AAW) are 25% more likely to present with late stage breast cancer and 20% more likely to die from their disease than Caucasian women. Researchers report that a treatment delay of 3 months is a significant factor in breast cancer mortality. Socioeconomic factors, lack of access and knowledge, spiritual and religious beliefs, fear and fatalism are reported as contributing factors to screening delays. Studies have primarily applied the Health Belief Model (HBM) and modified versions like the Champion HBM to preventive health practices. Neither have significant inclusion of spirituality or religiosity. The TRA/TPB focus on beliefs, intent and attitude as individual determinants of the likelihood of performing a specific behavior; but have not had wide utility in studies related to screening delays among AAW. This paper explores the utility of applying the TRA/TPB as the theoretical framework for determining cultural relevance of spirituality and religiosity to screening delays among AAW.

Contribution to harmonic balance calculations of self-sustained periodic oscillations with focus on single-reed instruments.

PubMed

Farner, Snorre; Vergez, Christophe; Kergomard, Jean; Lizée, Aude

2006-03-01

The harmonic balance method (HBM) was originally developed for finding periodic solutions of electronical and mechanical systems under a periodic force, but has been adapted to self-sustained musical instruments. Unlike time-domain methods, this frequency-domain method does not capture transients and so is not adapted for sound synthesis. However, its independence of time makes it very useful for studying any periodic solution, whether stable or unstable, without care of particular initial conditions in time. A computer program for solving general problems involving nonlinearly coupled exciter and resonator, HARMBAL, has been developed based on the HBM. The method as well as convergence improvements and continuation facilities are thoroughly presented and discussed in the present paper. Applications of the method are demonstrated, especially on problems with severe difficulties of convergence: the Helmholtz motion (square signals) of single-reed instruments when no losses are taken into account, the reed being modeled as a simple spring.

Enhancing health knowledge, health beliefs, and health behavior in Poland through a health promoting television program series.

PubMed

Chew, Fiona; Palmer, Sushma; Slonska, Zofia; Subbiah, Kalyani

2002-01-01

This study examined the impact of a health promoting television program series on health knowledge and the key factors of the health belief model (HBM) that have led people to engage in healthy behavior (exercising, losing weight, changing eating habits, and not smoking/quitting smoking). Using data from a posttest comparison field study with 15) viewers and 146 nonviewers in Poland, we found that hierarchical regression analysis showed stronger support for the HBM factors of efficacy, susceptibility, seriousness, and salience in their contribution toward health behavior among television viewers compared with nonviewers. Cues to action variables (including television viewing) and health knowledge boosted efficacy among viewers. Without the advantage of receiving health information from the television series, nonviewers relied on their basic disease fears on one hand, and interest in good health on the other to take steps toward becoming healthier. A health promoting television series can increase health knowledge and enhance health beliefs, which in turn contribute to healthy behaviors.

Analytical approximations for the oscillators with anti-symmetric quadratic nonlinearity

NASA Astrophysics Data System (ADS)

Alal Hosen, Md.; Chowdhury, M. S. H.; Yeakub Ali, Mohammad; Faris Ismail, Ahmad

2017-12-01

A second-order ordinary differential equation involving anti-symmetric quadratic nonlinearity changes sign. The behaviour of the oscillators with an anti-symmetric quadratic nonlinearity is assumed to oscillate different in the positive and negative directions. In this reason, Harmonic Balance Method (HBM) cannot be directly applied. The main purpose of the present paper is to propose an analytical approximation technique based on the HBM for obtaining approximate angular frequencies and the corresponding periodic solutions of the oscillators with anti-symmetric quadratic nonlinearity. After applying HBM, a set of complicated nonlinear algebraic equations is found. Analytical approach is not always fruitful for solving such kinds of nonlinear algebraic equations. In this article, two small parameters are found, for which the power series solution produces desired results. Moreover, the amplitude-frequency relationship has also been determined in a novel analytical way. The presented technique gives excellent results as compared with the corresponding numerical results and is better than the existing ones.

Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks

NASA Astrophysics Data System (ADS)

Zhu, Shijia; Wang, Yadong

2015-12-01

Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is ‘stationarity’, and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.

Foundational workplace safety and health competencies for the emerging workforce☆

PubMed Central

Okun, Andrea H.; Guerin, Rebecca J.; Schulte, Paul A.

2016-01-01

Introduction Young workers (aged 15–24) suffer disproportionately from workplace injuries, with a nonfatal injury rate estimated to be two times higher than among workers age 25 or over. These workers make up approximately 9% of the U.S. workforce and studies have shown that nearly 80% of high school students work at some point during high school. Although young worker injuries are a pressing public health problem, the critical knowledge and skills needed to prepare youth for safe and healthy work are missing from most frameworks used to prepare the emerging U.S. workforce. Methods A framework of foundational workplace safety and health knowledge and skills (the NIOSH 8 Core Competencies)was developed based on the Health Belief Model (HBM). Results The proposed NIOSH Core Competencies utilize the HBM to provide a framework for foundational workplace safety and health knowledge and skills. An examination of how these competencies and the HBM apply to actions that workers take to protect themselves is provided. The social and physical environments that influence these actions are also discussed. Conclusions The NIOSH 8 Core Competencies, grounded in one of the most widely used health behavior theories, fill a critical gap in preparing the emerging U.S. workforce to be cognizant of workplace risks. Practical applications Integration of the NIOSH 8 Core Competencies into school curricula is one way to ensure that every young person has the foundational workplace safety and health knowledge and skills to participate in, and benefit from, safe and healthy work. National Safety Council and Elsevier Ltd. All rights reserved. PMID:27846998

A functional model for characterizing long-distance movement behaviour

USGS Publications Warehouse

Buderman, Frances E.; Hooten, Mevin B.; Ivan, Jacob S.; Shenk, Tanya M.

2016-01-01

Advancements in wildlife telemetry techniques have made it possible to collect large data sets of highly accurate animal locations at a fine temporal resolution. These data sets have prompted the development of a number of statistical methodologies for modelling animal movement.Telemetry data sets are often collected for purposes other than fine-scale movement analysis. These data sets may differ substantially from those that are collected with technologies suitable for fine-scale movement modelling and may consist of locations that are irregular in time, are temporally coarse or have large measurement error. These data sets are time-consuming and costly to collect but may still provide valuable information about movement behaviour.We developed a Bayesian movement model that accounts for error from multiple data sources as well as movement behaviour at different temporal scales. The Bayesian framework allows us to calculate derived quantities that describe temporally varying movement behaviour, such as residence time, speed and persistence in direction. The model is flexible, easy to implement and computationally efficient.We apply this model to data from Colorado Canada lynx (Lynx canadensis) and use derived quantities to identify changes in movement behaviour.

Information content of incubation experiments for inverse estimation of pools in the Rothamsted carbon model: a Bayesian approach

NASA Astrophysics Data System (ADS)

Scharnagl, Benedikt; Vrugt, Jasper A.; Vereecken, Harry; Herbst, Michael

2010-05-01

Turnover of soil organic matter is usually described with multi-compartment models. However, a major drawback of these models is that the conceptually defined compartments (or pools) do not necessarily correspond to measurable soil organic carbon (SOC) fractions in real practice. This not only impairs our ability to rigorously evaluate SOC models but also makes it difficult to derive accurate initial states. In this study, we tested the usefulness and applicability of inverse modeling to derive the various carbon pool sizes in the Rothamsted carbon model (ROTHC) using a synthetic time series of mineralization rates from laboratory incubation. To appropriately account for data and model uncertainty we considered a Bayesian approach using the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. This Markov chain Monte Carlo scheme derives the posterior probability density distribution of the initial pool sizes at the start of incubation from observed mineralization rates. We used the Kullback-Leibler divergence to quantify the information contained in the data and to illustrate the effect of increasing incubation times on the reliability of the pool size estimates. Our results show that measured mineralization rates generally provide sufficient information to reliably estimate the sizes of all active pools in the ROTHC model. However, with about 900 days of incubation, these experiments are excessively long. The use of prior information on microbial biomass provided a way forward to significantly reduce uncertainty and required duration of incubation to about 600 days. Explicit consideration of model parameter uncertainty in the estimation process further impaired the identifiability of initial pools, especially for the more slowly decomposing pools. Our illustrative case studies show how Bayesian inverse modeling can be used to provide important insights into the information content of incubation experiments. Moreover, the outcome of this virtual experiment helps to explain the results of related real-world studies on SOC dynamics.

Health belief model perceptions, knowledge of heart disease, and its risk factors in educated African-American women: an exploration of the relationships of socioeconomic status and age.

PubMed

Jones, Deborah E; Weaver, Michael T; Grimley, Diane; Appel, Susan J; Ard, Jamy

2006-12-01

Heart disease is the leading cause of death for African-American women in the United States. Although African-American women experience higher rates of heart disease with earlier onset and more severe consequences than White women do, they are not aware of their risk for the disease. The Health Belief Model (HBM) has been commonly used to guide preventive interventions in cardiovascular health. However, the HBM has not been evaluated for African-American women regarding its effectiveness. This study explored the perceptions of susceptibility and seriousness of heart disease, and the relationships between socioeconomic status (SES), age, and knowledge of heart disease and its risk factors among 194 educated African-American women from the southern United States. Participants did not perceive themselves to be at high risk for developing heart disease while perceiving heart disease as serious. African-American women who were older perceived heart disease to be more serious than their younger counterparts did. Older women and those with higher SES knew more about heart disease and risk factors. Neither SES nor age moderated the relationship between knowledge and perceived susceptibility or seriousness.

A Bayesian-frequentist two-stage single-arm phase II clinical trial design.

PubMed

Dong, Gaohong; Shih, Weichung Joe; Moore, Dirk; Quan, Hui; Marcella, Stephen

2012-08-30

It is well-known that both frequentist and Bayesian clinical trial designs have their own advantages and disadvantages. To have better properties inherited from these two types of designs, we developed a Bayesian-frequentist two-stage single-arm phase II clinical trial design. This design allows both early acceptance and rejection of the null hypothesis ( H(0) ). The measures (for example probability of trial early termination, expected sample size, etc.) of the design properties under both frequentist and Bayesian settings are derived. Moreover, under the Bayesian setting, the upper and lower boundaries are determined with predictive probability of trial success outcome. Given a beta prior and a sample size for stage I, based on the marginal distribution of the responses at stage I, we derived Bayesian Type I and Type II error rates. By controlling both frequentist and Bayesian error rates, the Bayesian-frequentist two-stage design has special features compared with other two-stage designs. Copyright © 2012 John Wiley & Sons, Ltd.

A method to model anticipatory postural control in driver braking events.

PubMed

Östh, Jonas; Eliasson, Erik; Happee, Riender; Brolin, Karin

2014-09-01

Human body models (HBMs) for vehicle occupant simulations have recently been extended with active muscles and postural control strategies. Feedback control has been used to model occupant responses to autonomous braking interventions. However, driver postural responses during driver initiated braking differ greatly from autonomous braking. In the present study, an anticipatory postural response was hypothesized, modelled in a whole-body HBM with feedback controlled muscles, and validated using existing volunteer data. The anticipatory response was modelled as a time dependent change in the reference value for the feedback controllers, which generates correcting moments to counteract the braking deceleration. The results showed that, in 11 m/s(2) driver braking simulations, including the anticipatory postural response reduced the peak forward displacement of the head by 100mm, of the shoulder by 30 mm, while the peak head flexion rotation was reduced by 18°. The HBM kinematic response was within a one standard deviation corridor of corresponding test data from volunteers performing maximum braking. It was concluded that the hypothesized anticipatory responses can be modelled by changing the reference positions of the individual joint feedback controllers that regulate muscle activation levels. The addition of anticipatory postural control muscle activations appears to explain the difference in occupant kinematics between driver and autonomous braking. This method of modelling postural reactions can be applied to the simulation of other driver voluntary actions, such as emergency avoidance by steering. Copyright © 2014. Published by Elsevier B.V.

Technical Note: Approximate Bayesian parameterization of a complex tropical forest model

NASA Astrophysics Data System (ADS)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2013-08-01

Inverse parameter estimation of process-based models is a long-standing problem in ecology and evolution. A key problem of inverse parameter estimation is to define a metric that quantifies how well model predictions fit to the data. Such a metric can be expressed by general cost or objective functions, but statistical inversion approaches are based on a particular metric, the probability of observing the data given the model, known as the likelihood. Deriving likelihoods for dynamic models requires making assumptions about the probability for observations to deviate from mean model predictions. For technical reasons, these assumptions are usually derived without explicit consideration of the processes in the simulation. Only in recent years have new methods become available that allow generating likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional MCMC, performs well in retrieving known parameter values from virtual field data generated by the forest model. We analyze the results of the parameter estimation, examine the sensitivity towards the choice and aggregation of model outputs and observed data (summary statistics), and show results from using this method to fit the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss differences of this approach to Approximate Bayesian Computing (ABC), another commonly used method to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can successfully be applied to process-based models of high complexity. The methodology is particularly suited to heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models in ecology and evolution.

Unmasking the masked Universe: the 2M++ catalogue through Bayesian eyes

NASA Astrophysics Data System (ADS)

Lavaux, Guilhem; Jasche, Jens

2016-01-01

This work describes a full Bayesian analysis of the Nearby Universe as traced by galaxies of the 2M++ survey. The analysis is run in two sequential steps. The first step self-consistently derives the luminosity-dependent galaxy biases, the power spectrum of matter fluctuations and matter density fields within a Gaussian statistic approximation. The second step makes a detailed analysis of the three-dimensional large-scale structures, assuming a fixed bias model and a fixed cosmology. This second step allows for the reconstruction of both the final density field and the initial conditions at z = 1000 assuming a fixed bias model. From these, we derive fields that self-consistently extrapolate the observed large-scale structures. We give two examples of these extrapolation and their utility for the detection of structures: the visibility of the Sloan Great Wall, and the detection and characterization of the Local Void using DIVA, a Lagrangian based technique to classify structures.

Comparacion de Modelos de Educacion Sexual en El Conocimiento y Cambio de Actitudes en Practicas Sexuales por Alumnos de Nivel Superior en La Region De Caguas, Puerto Rico

ERIC Educational Resources Information Center

Juan, Vallejo Ramos L.

2012-01-01

In opposition to the Sexual Education Traditional Model (SETM) that is used in the state schools of Puerto Rico, the Health Beliefs Model (HBM) appears. It facilitates a curricular design that improves the ability of the students to respond to the group pressure by means of attitudes that stimulate sexual conducts of smaller risk of propagation of…

Applying the Health Belief Model and an Integrated Behavioral Model to Promote Breast Tissue Donation Among Asian Americans.

PubMed

Shafer, Autumn; Kaufhold, Kelly; Luo, Yunjuan

2018-07-01

An important part in the effort to prevent, treat, and cure breast cancer is research done with healthy breast tissue. The Susan G. Komen for the Cure Tissue Bank at Indiana University Simon Cancer Center (KTB) encourages women to donate a small amount of healthy breast tissue and then provides that tissue to researchers studying breast cancer. Although KTB has a large donor base, the volume of tissue samples from Asian women is low despite prior marketing efforts to encourage donation among this population. This study builds on prior work promoting breast cancer screenings among Asian women by applying constructs from the Health Belief Model (HBM) and the Integrated Behavioral Model (IBM) to investigate why Asian-American women are less inclined to donate their healthy breast tissue than non-Asian women and how this population may be motivated to donate in the future. A national online survey (N = 1,317) found Asian women had significantly lower perceived severity, some lower perceived benefits, and higher perceived barriers to tissue donation than non-Asian women under HBM and significantly lower injunctive norms supporting breast tissue donation, lower perceived behavioral control, and lower intentions to donate under IBM. This study also compares and discusses similarities and differences among East, Southeast, and South Asian women on these same constructs.

Approximate Bayesian computation for forward modeling in cosmology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akeret, Joël; Refregier, Alexandre; Amara, Adam

Bayesian inference is often used in cosmology and astrophysics to derive constraints on model parameters from observations. This approach relies on the ability to compute the likelihood of the data given a choice of model parameters. In many practical situations, the likelihood function may however be unavailable or intractable due to non-gaussian errors, non-linear measurements processes, or complex data formats such as catalogs and maps. In these cases, the simulation of mock data sets can often be made through forward modeling. We discuss how Approximate Bayesian Computation (ABC) can be used in these cases to derive an approximation to themore » posterior constraints using simulated data sets. This technique relies on the sampling of the parameter set, a distance metric to quantify the difference between the observation and the simulations and summary statistics to compress the information in the data. We first review the principles of ABC and discuss its implementation using a Population Monte-Carlo (PMC) algorithm and the Mahalanobis distance metric. We test the performance of the implementation using a Gaussian toy model. We then apply the ABC technique to the practical case of the calibration of image simulations for wide field cosmological surveys. We find that the ABC analysis is able to provide reliable parameter constraints for this problem and is therefore a promising technique for other applications in cosmology and astrophysics. Our implementation of the ABC PMC method is made available via a public code release.« less

A Bayesian hierarchical model for mortality data from cluster-sampling household surveys in humanitarian crises.

PubMed

Heudtlass, Peter; Guha-Sapir, Debarati; Speybroeck, Niko

2018-05-31

The crude death rate (CDR) is one of the defining indicators of humanitarian emergencies. When data from vital registration systems are not available, it is common practice to estimate the CDR from household surveys with cluster-sampling design. However, sample sizes are often too small to compare mortality estimates to emergency thresholds, at least in a frequentist framework. Several authors have proposed Bayesian methods for health surveys in humanitarian crises. Here, we develop an approach specifically for mortality data and cluster-sampling surveys. We describe a Bayesian hierarchical Poisson-Gamma mixture model with generic (weakly informative) priors that could be used as default in absence of any specific prior knowledge, and compare Bayesian and frequentist CDR estimates using five different mortality datasets. We provide an interpretation of the Bayesian estimates in the context of an emergency threshold and demonstrate how to interpret parameters at the cluster level and ways in which informative priors can be introduced. With the same set of weakly informative priors, Bayesian CDR estimates are equivalent to frequentist estimates, for all practical purposes. The probability that the CDR surpasses the emergency threshold can be derived directly from the posterior of the mean of the mixing distribution. All observation in the datasets contribute to the estimation of cluster-level estimates, through the hierarchical structure of the model. In a context of sparse data, Bayesian mortality assessments have advantages over frequentist ones already when using only weakly informative priors. More informative priors offer a formal and transparent way of combining new data with existing data and expert knowledge and can help to improve decision-making in humanitarian crises by complementing frequentist estimates.

Robust human body model injury prediction in simulated side impact crashes.

PubMed

Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D

2016-01-01

This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.

Evaluation of a neutron spectrum from Bonner spheres measurements using a Bayesian parameter estimation combined with the traditional unfolding methods

NASA Astrophysics Data System (ADS)

Mazrou, H.; Bezoubiri, F.

2018-07-01

In this work, a new program developed under MATLAB environment and supported by the Bayesian software WinBUGS has been combined to the traditional unfolding codes namely MAXED and GRAVEL, to evaluate a neutron spectrum from the Bonner spheres measured counts obtained around a shielded 241AmBe based-neutron irradiator located at a Secondary Standards Dosimetry Laboratory (SSDL) at CRNA. In the first step, the results obtained by the standalone Bayesian program, using a parametric neutron spectrum model based on a linear superposition of three components namely: a thermal-Maxwellian distribution, an epithermal (1/E behavior) and a kind of a Watt fission and Evaporation models to represent the fast component, were compared to those issued from MAXED and GRAVEL assuming a Monte Carlo default spectrum. Through the selection of new upper limits for some free parameters, taking into account the physical characteristics of the irradiation source, of both considered models, good agreement was obtained for investigated integral quantities i.e. fluence rate and ambient dose equivalent rate compared to MAXED and GRAVEL results. The difference was generally below 4% for investigated parameters suggesting, thereby, the reliability of the proposed models. In the second step, the Bayesian results obtained from the previous calculations were used, as initial guess spectra, for the traditional unfolding codes, MAXED and GRAVEL to derive the solution spectra. Here again the results were in very good agreement, confirming the stability of the Bayesian solution.

Recursive Bayesian recurrent neural networks for time-series modeling.

PubMed

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

Inference of epidemiological parameters from household stratified data

PubMed Central

Walker, James N.; Ross, Joshua V.

2017-01-01

We consider a continuous-time Markov chain model of SIR disease dynamics with two levels of mixing. For this so-called stochastic households model, we provide two methods for inferring the model parameters—governing within-household transmission, recovery, and between-household transmission—from data of the day upon which each individual became infectious and the household in which each infection occurred, as might be available from First Few Hundred studies. Each method is a form of Bayesian Markov Chain Monte Carlo that allows us to calculate a joint posterior distribution for all parameters and hence the household reproduction number and the early growth rate of the epidemic. The first method performs exact Bayesian inference using a standard data-augmentation approach; the second performs approximate Bayesian inference based on a likelihood approximation derived from branching processes. These methods are compared for computational efficiency and posteriors from each are compared. The branching process is shown to be a good approximation and remains computationally efficient as the amount of data is increased. PMID:29045456

Inter- and intra-individual variation in urinary biomarker concentrations over a 6-day sampling period. Part 1: metals.

PubMed

Smolders, Roel; Koch, Holger M; Moos, Rebecca K; Cocker, John; Jones, Kate; Warren, Nick; Levy, Len; Bevan, Ruth; Hays, Sean M; Aylward, Lesa L

2014-12-01

The aim of the current HBM-study is to further the understanding of the impact of inter- and intra-individual variability in HBM surveys as it may have implications for the design and interpretation of the study outcomes. As spot samples only provide a snapshot in time of the concentrations of chemicals in an individual, it remains unclear to what extent intra-individual variability plays a role in the overall variability of population-wide HBM surveys. The current paper describes the results of an intensive biomonitoring study, in which all individual urine samples of 8 individuals were collected over a 6-day sampling period (a total of 352 unique samples). By analyzing different metals (As, Cd, Mn, Ni) in each individual sample, inter- and intra-individual variability for these four metals could be determined, and the relationships between exposure, internal dose, and sampling protocol assessed. Although the range of biomarker values for different metals was well within the normal range reported in large-scale population surveys, large intra-individual differences over a 6-day period could also be observed. Typically, measured biomarker values span at least an order of magnitude within an individual, and more if specific exposure episodes could be identified. Fish consumption for example caused a twenty- to thirty-fold increase in urinary As-levels over a period of 2-6h. Intra-class correlation coefficients (ICC) were typically low for uncorrected biomarker values (between 0.104 and 0.460 for the 4 metals), but improved when corrected for creatinine or specific gravity (SG). The results show that even though urine is a preferred matrix for HBM studies, there are certain methodological issues that need to be taken into account in the interpretation of urinary biomarker data, related to the intrinsic variability of the urination process itself, the relationship between exposure events and biomarker quantification, and the timing of sampling. When setting up HBM-projects, this expected relationship between individual exposure episode and urinary biomarker concentration needs to be taken into account. Copyright © 2014. Published by Elsevier Ireland Ltd.

What Health-Related Information Flows through You Every Day? A Content Analysis of Microblog Messages on Air Pollution

ERIC Educational Resources Information Center

Yang, Qinghua; Yang, Fan; Zhou, Chun

2015-01-01

Purpose: The purpose of this paper is to investigate how the information about haze, a term used in China to describe the air pollution problem, is portrayed on Chinese social media by different types of organizations using the theoretical framework of the health belief model (HBM). Design/methodology/approach: A content analysis was conducted…

Exploring Perceptions about and Behaviors Related to Mental Illness and Mental Health Service Utilization among College Students Using the Health Belief Model (HBM)

ERIC Educational Resources Information Center

Nobiling, Brandye D.; Maykrantz, Sherry Azadi

2017-01-01

Background: Mental health service is underutilized in the United States. Adolescent and young adults, including college students, are especially unlikely to seek professional help for mental illness. This issue presents a concern, because signs and symptoms commonly appear during this part of growth and development. Purpose: The Health Belief…

Analysis of multiple bone responses to graded strains above functional levels, and to disuse, in mice in vivo show that the human Lrp5 G171V High Bone Mass mutation increases the osteogenic response to loading but that lack of Lrp5 activity reduces it

PubMed Central

Saxon, Leanne K.; Jackson, Brendan F.; Sugiyama, Toshihiro; Lanyon, Lance E.; Price, Joanna S.

2011-01-01

Introduction To investigate the role of the low-density lipoprotein receptor-related protein 5 (Lrp5) in bones' responses to loading, we analysed changes in multiple measures of bone architecture in tibias subjected to loading or disuse in male and female mice with the Lrp5 loss of function mutation (Lrp5−/−) or heterozygous for the Lrp5 G171V High Bone Mass (HBM) mutation (Lrp5HBM+). Materials and methods The right tibias of these 17 week old male and female mice and their Wild Type (WT) littermates were subjected to short periods of loading three days a week for two weeks. Each tibia was loaded for 40 cycles, to produce peak strains at the midshaft within the low, medium or high physiological range (~ 1500, 2400 and 3000 microstrain, respectively). In similar groups of mice the right sciatic nerve was severed causing disuse of the right tibia for 3 weeks. Data from microCT of loaded, neurectomised and contra-lateral control tibias were analysed to quantify changes in the cortical and cancellous regions of the bone in the absence of functional strains and in response to graded strains in addition to those derived from function. Results and conclusion Male WT+/+ controls showed significant strain:response curves for cortical area and trabecular thickness, but Lrp5−/− mice showed no detectable strain:response in those same outcomes. Female mice of either WT+/+ or Lrp5−/− genotype did not show significant strain:response curves for cortical or trabecular parameters, the one exception being Tb.Th in Lrp5−/− mice. Since female WT+/+ mice did not respond to loading in a significant dose:responsive manner, the similar lack of responsiveness of the Lrp5−/− females could not be ascribed to their Lrp5 status. Cortical bone loss associated with disuse showed no differences between Lrp5−/− mice and WT+/+ controls, but in cancellous bone of both male and females of these mice, there was a greater loss than in WT+/+ controls. In contrast, the tibias of male and female mice heterozygous for the Lrp5 G171V HBM mutation showed greater osteogenic responsiveness to loading and less bone loss associated with disuse than their WTHBM− controls. These data indicate that the presence of the Lrp5 G171V HBM mutation is associated with an increased osteogenic response to loading but support only a marginal gender-related role for normal Lrp5 function in this loading-related response. PMID:21419885

Evaluating scaling models in biology using hierarchical Bayesian approaches

PubMed Central

Price, Charles A; Ogle, Kiona; White, Ethan P; Weitz, Joshua S

2009-01-01

Theoretical models for allometric relationships between organismal form and function are typically tested by comparing a single predicted relationship with empirical data. Several prominent models, however, predict more than one allometric relationship, and comparisons among alternative models have not taken this into account. Here we evaluate several different scaling models of plant morphology within a hierarchical Bayesian framework that simultaneously fits multiple scaling relationships to three large allometric datasets. The scaling models include: inflexible universal models derived from biophysical assumptions (e.g. elastic similarity or fractal networks), a flexible variation of a fractal network model, and a highly flexible model constrained only by basic algebraic relationships. We demonstrate that variation in intraspecific allometric scaling exponents is inconsistent with the universal models, and that more flexible approaches that allow for biological variability at the species level outperform universal models, even when accounting for relative increases in model complexity. PMID:19453621

Sensitive LC-MS/MS Method for the Simultaneous Determination of Bendamustine and its Active Metabolite, γ-Hydroxybendamustine in Small Volume Mice and Dog Plasma and its Application to a Pharmacokinetic Study in Mice and Dogs.

PubMed

Chandrashekar, Devaraj V; Suresh, Ponnayyan S; Kumar, Rajnish; Bhamidipati, Ravi Kanth; Mullangi, Ramesh; Richter, Wolfgang; Srinivas, Nuggehally R

2017-09-01

A highly sensitive, specific and rapid LC-ESI-MS/MS method has been developed and validated for the simultaneous quantification of bendamustine (BM) and γ-hydroxybendamustine (HBM) in small volume (20 µL) mice and dog plasma using phenacetin as an internal standard (IS) as per regulatory guidelines. Both the analytes and IS were extracted from mice and dog plasma using a liquid-liquid extraction method. Chromatography was achieved on Atlantis dC 18 column using an isocratic mobile phase (0.2% formic acid:acetonitrile, 25:75) at a flow rate of 0.40 mL/min. The total chromatographic run time was 3.0 min and the elution of BM, HBM and IS occurred at ~1.2, 1.2 and 2.0 min, respectively. A linear response function was established 0.11-518 ng/mL for both the analytes in mice and dog plasma. The intra- and inter-day accuracy and precisions were in the range of 3.46-12.9 and 3.63-8.23%; 1.15-9.00 and 7.86-9.49% for BM and HBM, respectively in mice plasma and 2.15-6.49 and 1.73-13.1%; 4.35-13.9 and 4.33-10.5% for BM and HBM, respectively in dog plasma. This novel method has been applied to a pharmacokinetic study in mice and dogs. © Georg Thieme Verlag KG Stuttgart · New York.

On a full Bayesian inference for force reconstruction problems

NASA Astrophysics Data System (ADS)

Aucejo, M.; De Smet, O.

2018-05-01

In a previous paper, the authors introduced a flexible methodology for reconstructing mechanical sources in the frequency domain from prior local information on both their nature and location over a linear and time invariant structure. The proposed approach was derived from Bayesian statistics, because of its ability in mathematically accounting for experimenter's prior knowledge. However, since only the Maximum a Posteriori estimate was computed, the posterior uncertainty about the regularized solution given the measured vibration field, the mechanical model and the regularization parameter was not assessed. To answer this legitimate question, this paper fully exploits the Bayesian framework to provide, from a Markov Chain Monte Carlo algorithm, credible intervals and other statistical measures (mean, median, mode) for all the parameters of the force reconstruction problem.

Effectiveness of breastfeeding education on the weight of child and self-efficacy of mothers – 2011

PubMed Central

Kamran, Aziz; Shrifirad, Gholamreza; Mirkarimi, Seyed Kamal; Farahani, Abbas

2012-01-01

Background: Breastfeeding is the most natural and essential way for feeding newborn babies. This is an ideal approach for physical and emotional development of babies, as well as for the recovery of mothers. This study was aimed to determine the effect of breastfeeding education based on the health belief model (HBM) toward primiparous women. Materials and Methods: In a case–control group, quasi-experimental study, 88 subjects were allocated in control and experimental groups. Subjects who were assigned to the experimental group were provided a program consisting of group education based on HBM during their prenatal period. Instrument for data gathering was made by the researchers and standard questionnaire from Dennis and Faux for Breastfeeding Self-efficacy Scale (BSES). Baseline interviews were conducted before delivery and follow-up visits were conducted after 30 days and at the fourth month after delivery. Data were analyzed using SPSS (version 16) with c2, independent sample t-tests, and paired t-test. Results: Mean age of pregnant women who participated in the study was 22 ± 3.29 years. After the program, the experimental group had significantly better scores in terms of self-efficacy, knowledge, and attitude scores statistically. In the fourth month, the mean of child weight in the experimental group was significantly higher than that of the control group (P=0.001) and exclusive breastfeeding was significantly higher than in the control group (P=0.007). Conclusion: Prenatal education in this study based on HBM was successful, and knowledge, attitude, self-efficacy, and related indicators improved. The necessity of producing standard education package and education of pregnant mothers, especially in their first pregnancy, by health professionals is perceived. PMID:23555114

The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.

PubMed

Kasi, Patrick; Wright, James; Khamis, Heba; Birznieks, Ingvars; van Schaik, André

2016-01-01

It is well known that signals encoded by mechanoreceptors facilitate precise object manipulation in humans. It is therefore of interest to study signals encoded by the mechanoreceptors because this will contribute further towards the understanding of fundamental sensory mechanisms that are responsible for coordinating force components during object manipulation. From a practical point of view, this may suggest strategies for designing sensory-controlled biomedical devices and robotic manipulators. We use a two-stage nonlinear decoding paradigm to reconstruct the force stimulus given signals from slowly adapting type one (SA-I) tactile afferents. First, we describe a nonhomogeneous Poisson encoding model which is a function of the force stimulus and the force's rate of change. In the decoding phase, we use a recursive nonlinear Bayesian filter to reconstruct the force profile, given the SA-I spike patterns and parameters described by the encoding model. Under the current encoding model, the mode ratio of force to its derivative is: 1.26 to 1.02. This indicates that the force derivative contributes significantly to the rate of change to the SA-I afferent spike modulation. Furthermore, using recursive Bayesian decoding algorithms is advantageous because it can incorporate past and current information in order to make predictions--consistent with neural systems--with little computational resources. This makes it suitable for interfacing with prostheses.

The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans

PubMed Central

Wright, James; Khamis, Heba; Birznieks, Ingvars; van Schaik, André

2016-01-01

It is well known that signals encoded by mechanoreceptors facilitate precise object manipulation in humans. It is therefore of interest to study signals encoded by the mechanoreceptors because this will contribute further towards the understanding of fundamental sensory mechanisms that are responsible for coordinating force components during object manipulation. From a practical point of view, this may suggest strategies for designing sensory-controlled biomedical devices and robotic manipulators. We use a two-stage nonlinear decoding paradigm to reconstruct the force stimulus given signals from slowly adapting type one (SA-I) tactile afferents. First, we describe a nonhomogeneous Poisson encoding model which is a function of the force stimulus and the force’s rate of change. In the decoding phase, we use a recursive nonlinear Bayesian filter to reconstruct the force profile, given the SA-I spike patterns and parameters described by the encoding model. Under the current encoding model, the mode ratio of force to its derivative is: 1.26 to 1.02. This indicates that the force derivative contributes significantly to the rate of change to the SA-I afferent spike modulation. Furthermore, using recursive Bayesian decoding algorithms is advantageous because it can incorporate past and current information in order to make predictions—consistent with neural systems—with little computational resources. This makes it suitable for interfacing with prostheses. PMID:27077750

SIG-VISA: Signal-based Vertically Integrated Seismic Monitoring

NASA Astrophysics Data System (ADS)

Moore, D.; Mayeda, K. M.; Myers, S. C.; Russell, S.

2013-12-01

Traditional seismic monitoring systems rely on discrete detections produced by station processing software; however, while such detections may constitute a useful summary of station activity, they discard large amounts of information present in the original recorded signal. We present SIG-VISA (Signal-based Vertically Integrated Seismic Analysis), a system for seismic monitoring through Bayesian inference on seismic signals. By directly modeling the recorded signal, our approach incorporates additional information unavailable to detection-based methods, enabling higher sensitivity and more accurate localization using techniques such as waveform matching. SIG-VISA's Bayesian forward model of seismic signal envelopes includes physically-derived models of travel times and source characteristics as well as Gaussian process (kriging) statistical models of signal properties that combine interpolation of historical data with extrapolation of learned physical trends. Applying Bayesian inference, we evaluate the model on earthquakes as well as the 2009 DPRK test event, demonstrating a waveform matching effect as part of the probabilistic inference, along with results on event localization and sensitivity. In particular, we demonstrate increased sensitivity from signal-based modeling, in which the SIGVISA signal model finds statistical evidence for arrivals even at stations for which the IMS station processing failed to register any detection.

Bayesian operational modal analysis with asynchronous data, Part II: Posterior uncertainty

NASA Astrophysics Data System (ADS)

Zhu, Yi-Chen; Au, Siu-Kui

2018-01-01

A Bayesian modal identification method has been proposed in the companion paper that allows the most probable values of modal parameters to be determined using asynchronous ambient vibration data. This paper investigates the identification uncertainty of modal parameters in terms of their posterior covariance matrix. Computational issues are addressed. Analytical expressions are derived to allow the posterior covariance matrix to be evaluated accurately and efficiently. Synthetic, laboratory and field data examples are presented to verify the consistency, investigate potential modelling error and demonstrate practical applications.

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.

PubMed

Li, Xuewen; Xie, Yunfeng; Li, Lianfa; Yang, Xunfeng; Wang, Ning; Wang, Jinfeng

2015-11-01

Prediction of antibiotic pollution and its consequences is difficult, due to the uncertainties and complexities associated with multiple related factors. This article employed domain knowledge and spatial data to construct a Bayesian network (BN) model to assess fluoroquinolone antibiotic (FQs) pollution in the soil of an intensive vegetable cultivation area. The results show: (1) The relationships between FQs pollution and contributory factors: Three factors (cultivation methods, crop rotations, and chicken manure types) were consistently identified as predictors in the topological structures of three FQs, indicating their importance in FQs pollution; deduced with domain knowledge, the cultivation methods are determined by the crop rotations, which require different nutrients (derived from the manure) according to different plant biomass. (2) The performance of BN model: The integrative robust Bayesian network model achieved the highest detection probability (pd) of high-risk and receiver operating characteristic (ROC) area, since it incorporates domain knowledge and model uncertainty. Our encouraging findings have implications for the use of BN as a robust approach to assessment of FQs pollution and for informing decisions on appropriate remedial measures.

Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.

PubMed

Chatzis, Sotirios P; Andreou, Andreas S

2015-11-01

Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.

Estimation of post-test probabilities by residents: Bayesian reasoning versus heuristics?

PubMed

Hall, Stacey; Phang, Sen Han; Schaefer, Jeffrey P; Ghali, William; Wright, Bruce; McLaughlin, Kevin

2014-08-01

Although the process of diagnosing invariably begins with a heuristic, we encourage our learners to support their diagnoses by analytical cognitive processes, such as Bayesian reasoning, in an attempt to mitigate the effects of heuristics on diagnosing. There are, however, limited data on the use ± impact of Bayesian reasoning on the accuracy of disease probability estimates. In this study our objective was to explore whether Internal Medicine residents use a Bayesian process to estimate disease probabilities by comparing their disease probability estimates to literature-derived Bayesian post-test probabilities. We gave 35 Internal Medicine residents four clinical vignettes in the form of a referral letter and asked them to estimate the post-test probability of the target condition in each case. We then compared these to literature-derived probabilities. For each vignette the estimated probability was significantly different from the literature-derived probability. For the two cases with low literature-derived probability our participants significantly overestimated the probability of these target conditions being the correct diagnosis, whereas for the two cases with high literature-derived probability the estimated probability was significantly lower than the calculated value. Our results suggest that residents generate inaccurate post-test probability estimates. Possible explanations for this include ineffective application of Bayesian reasoning, attribute substitution whereby a complex cognitive task is replaced by an easier one (e.g., a heuristic), or systematic rater bias, such as central tendency bias. Further studies are needed to identify the reasons for inaccuracy of disease probability estimates and to explore ways of improving accuracy.

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

Treesearch

Chad Babcock; Andrew O. Finley; John B. Bradford; Randy Kolka; Richard Birdsey; Michael G. Ryan

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both...

Impact behavior of a high viscosity magnetorheological fluid-based energy absorber with a radial flow mode

NASA Astrophysics Data System (ADS)

Fu, Benyuan; Liao, Changrong; Li, Zhuqiang; Xie, Lei; Zhang, Peng; Jian, Xiaochun

2017-02-01

High viscosity linear polysiloxane magnetorheological fluid (HVLP MRF) was demonstrated with excellent suspension stability. Such material is suitable for application in the magnetorheological energy absorbers (MREAs) under axial impact loading conditions. On this basis, a new energy absorber incorporating a radial valve with high magnetic field utilization and a corrugated tube is proposed. In energy absorption applications where the MREA is rarely if ever used, our MREA takes the ultra-stable HVLP MRF as controlled medium in order for a long-term stability. For MREA performing at very high shear rates where the minor losses are important contributing factors to damping, a nonlinear analytical model, based on the Herschel-Bulkley flow model (HB model), is developed taking into account the effects of minor losses (called HBM model). The HB model parameters are determined by rheological experiments with a commercial shear rheometer. Then, continuity equation and governing differential equation of the HVLP MRF in radial flow are established. Based on the HB model, the expressions of radial velocity distribution are deduced. The influences of minor losses on pressure drop are analyzed with mean fluid velocities. Further, mechanical behavior of the corrugated tube is investigated via drop test. In order to verify the theoretical methodology, a MREA is fabricated and tested using a high-speed drop tower facility with a 600 kg mass at different drop heights and in various magnetic fields. The experiment results show that the HBM model is capable of well predicting the impact behavior of the proposed MREA.

On the uncertainty in single molecule fluorescent lifetime and energy emission measurements

NASA Technical Reports Server (NTRS)

Brown, Emery N.; Zhang, Zhenhua; Mccollom, Alex D.

1995-01-01

Time-correlated single photon counting has recently been combined with mode-locked picosecond pulsed excitation to measure the fluorescent lifetimes and energy emissions of single molecules in a flow stream. Maximum likelihood (ML) and least square methods agree and are optimal when the number of detected photons is large however, in single molecule fluorescence experiments the number of detected photons can be less than 20, 67% of those can be noise and the detection time is restricted to 10 nanoseconds. Under the assumption that the photon signal and background noise are two independent inhomogeneous poisson processes, we derive the exact joint arrival time probably density of the photons collected in a single counting experiment performed in the presence of background noise. The model obviates the need to bin experimental data for analysis, and makes it possible to analyze formally the effect of background noise on the photon detection experiment using both ML or Bayesian methods. For both methods we derive the joint and marginal probability densities of the fluorescent lifetime and fluorescent emission. the ML and Bayesian methods are compared in an analysis of simulated single molecule fluorescence experiments of Rhodamine 110 using different combinations of expected background nose and expected fluorescence emission. While both the ML or Bayesian procedures perform well for analyzing fluorescence emissions, the Bayesian methods provide more realistic measures of uncertainty in the fluorescent lifetimes. The Bayesian methods would be especially useful for measuring uncertainty in fluorescent lifetime estimates in current single molecule flow stream experiments where the expected fluorescence emission is low. Both the ML and Bayesian algorithms can be automated for applications in molecular biology.

On the Uncertainty in Single Molecule Fluorescent Lifetime and Energy Emission Measurements

NASA Technical Reports Server (NTRS)

Brown, Emery N.; Zhang, Zhenhua; McCollom, Alex D.

1996-01-01

Time-correlated single photon counting has recently been combined with mode-locked picosecond pulsed excitation to measure the fluorescent lifetimes and energy emissions of single molecules in a flow stream. Maximum likelihood (ML) and least squares methods agree and are optimal when the number of detected photons is large, however, in single molecule fluorescence experiments the number of detected photons can be less than 20, 67 percent of those can be noise, and the detection time is restricted to 10 nanoseconds. Under the assumption that the photon signal and background noise are two independent inhomogeneous Poisson processes, we derive the exact joint arrival time probability density of the photons collected in a single counting experiment performed in the presence of background noise. The model obviates the need to bin experimental data for analysis, and makes it possible to analyze formally the effect of background noise on the photon detection experiment using both ML or Bayesian methods. For both methods we derive the joint and marginal probability densities of the fluorescent lifetime and fluorescent emission. The ML and Bayesian methods are compared in an analysis of simulated single molecule fluorescence experiments of Rhodamine 110 using different combinations of expected background noise and expected fluorescence emission. While both the ML or Bayesian procedures perform well for analyzing fluorescence emissions, the Bayesian methods provide more realistic measures of uncertainty in the fluorescent lifetimes. The Bayesian methods would be especially useful for measuring uncertainty in fluorescent lifetime estimates in current single molecule flow stream experiments where the expected fluorescence emission is low. Both the ML and Bayesian algorithms can be automated for applications in molecular biology.

Improve Knowledge, Beliefs and Behavior of Undergraduate Female Nursing Students in Al-Alzhar University toward Breast Self-Examination Practice

ERIC Educational Resources Information Center

El-Mohsen, Afaf S. Abd; El-Maksoud, Mona M. Abd

2015-01-01

Breast cancer is a public health problem that is most common form of cancer among females in both developed and developing world, The Health Belief Model (HBM) has been used as a theoretical framework to study Breast Self-Examination and other breast cancer detection behaviors. The aim of this study: Was to improve knowledge, beliefs and behavior…

Effectiveness of self-management promotion educational program among diabetic patients based on health belief model

PubMed Central

Jalilian, Farzad; Motlagh, Fazel Zinat; Solhi, Mahnaz; Gharibnavaz, Hasan

2014-01-01

Introduction: Diabetes is a chronic disease; it can cause serious complications. Diabetes self-management is essential for prevention of disease complications. This study was conducted to evaluate self-management promotion educational program intervention efficiency among diabetic patients in Iran and health belief model (HBM) was applied as a theoretical framework. Materials and Methods: Overall, 120 Type 2 diabetic patients referred to rural health centers in Gachsaran, Iran participated in this study as randomly divided into intervention and control group. This was a longitudinal randomized pre- and post-test series control group design panel study to implement a behavior modification based intervention to promotion self-management among diabetic patients. Cross-tabulation and t-test by using SPSS statistical package, version 16 was used for the statistical analysis. Results: Mean age was 55.07 years (SD = 9.94, range: 30-70). Our result shows significant improvements in average response for susceptibility, severity, benefit and self-management among intervention group. Additionally, after intervention, average response of the barrier to self-management was decreased among intervention group. Conclusion: Our result showed education program based on HBM was improve of self-management and seems implementing these programs can be effective in the and prevention of diabetes complications. PMID:24741654

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data

PubMed Central

Ferragina, A.; de los Campos, G.; Vazquez, A. I.; Cecchinato, A.; Bittante, G.

2017-01-01

The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict “difficult-to-predict” dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm−1 were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R2 value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R2 (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R2 of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations. PMID:26387015

A Development of Nonstationary Regional Frequency Analysis Model with Large-scale Climate Information: Its Application to Korean Watershed

NASA Astrophysics Data System (ADS)

Kim, Jin-Young; Kwon, Hyun-Han; Kim, Hung-Soo

2015-04-01

The existing regional frequency analysis has disadvantages in that it is difficult to consider geographical characteristics in estimating areal rainfall. In this regard, this study aims to develop a hierarchical Bayesian model based nonstationary regional frequency analysis in that spatial patterns of the design rainfall with geographical information (e.g. latitude, longitude and altitude) are explicitly incorporated. This study assumes that the parameters of Gumbel (or GEV distribution) are a function of geographical characteristics within a general linear regression framework. Posterior distribution of the regression parameters are estimated by Bayesian Markov Chain Monte Carlo (MCMC) method, and the identified functional relationship is used to spatially interpolate the parameters of the distributions by using digital elevation models (DEM) as inputs. The proposed model is applied to derive design rainfalls over the entire Han-river watershed. It was found that the proposed Bayesian regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis. In addition, the model showed an advantage in terms of quantifying uncertainty of the design rainfall and estimating the area rainfall considering geographical information. Finally, comprehensive discussion on design rainfall in the context of nonstationary will be presented. KEYWORDS: Regional frequency analysis, Nonstationary, Spatial information, Bayesian Acknowledgement This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Bayesian uncertainty quantification in linear models for diffusion MRI.

PubMed

Sjölund, Jens; Eklund, Anders; Özarslan, Evren; Herberthson, Magnus; Bånkestad, Maria; Knutsson, Hans

2018-03-29

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification. Copyright © 2018 Elsevier Inc. All rights reserved.

The Application of the Health Belief Model in Oral Health Education

PubMed Central

Solhi, M; Zadeh, D Shojaei; Seraj, B; Zadeh, S Faghih

2010-01-01

Background: The goal of this study was to determine the application of health belief model in oral health education for 12-year-old children and its effect on oral health behaviors and indexes. Methods: A quasi-experimental study was carried out on twelve-year-old girl students (n-291) in the first grade of secondary school, in the central district of Tehran, Iran. Research sample was selected by a multistage cluster sampling. The data was obtained by using a valid reliable questionnaire for measuring the perceptions, a checklist for observing the quality of brushing and dental flossing and health files and clinical observation. First, a descriptive study was applied to individual perceptions, oral behaviors, Oral Hygiene Index (OHI) and Decayed, Missing and Filled Teeth Index (DMFTI). Then an educational planning based on the results and Health Belief Model (HBM) was applied. The procedure was repeated after six months. Results: After education, based on HBM, all the oral health perceptions increased (P<.05). Correct brushing and flossing are influenced by increased perceptions. A low correlation between the reduction of DMFTI and increased perceived severity and increased perceived barriers are found (r= −0.28, r = 0.43 respectively). In addition, there was a limited correlation between OHI and increased perceived benefits (r = −0.26). Conclusion: Using health belief model in oral health education for increasing the likelihood of taking preventive oral health behaviors is applicable. PMID:23113044

DMI's Baltic Sea Coastal operational forecasting system

NASA Astrophysics Data System (ADS)

Murawski, Jens; Berg, Per; Weismann Poulsen, Jacob

2017-04-01

Operational forecasting is challenged with bridging the gap between the large scales of the driving weather systems and the local, human scales of the model applications. The limit of what can be represented by local model has been continuously shifted to higher and higher spatial resolution, with the aim to better resolve the local dynamic and to make it possible to describe processes that could only be parameterised in older versions, with the ultimate goal to improve the quality of the forecast. Current hardware trends demand a str onger focus on the development of efficient, highly parallelised software and require a refactoring of the code with a solid focus on portable performance. The gained performance can be used for running high resolution model with a larger coverage. Together with the development of efficient two-way nesting routines, this has made it possible to approach the near-coastal zone with model applications that can run in a time effective way. Denmarks Meteorological Institute uses the HBM(1) ocean circulation model for applications that covers the entire Baltic Sea and North Sea with an integrated model set-up that spans the range of horizontal resolution from 1nm for the entire Baltic Sea to approx. 200m resolution in local fjords (Limfjord). For the next model generation, the high resolution set-ups are going to be extended and new high resolution domains in coastal zones are either implemented or tested for operational use. For the first time it will be possible to cover large stretches of the Baltic coastal zone with sufficiently high resolution to model the local hydrodynamic adequately. (1) HBM stands for HIROMB-BOOS-Model, whereas HIROMB stands for "High Resolution Model for the Baltic Sea" and BOOS stands for "Baltic Operational Oceanography System".

Robust cue integration: a Bayesian model and evidence from cue-conflict studies with stereoscopic and figure cues to slant.

PubMed

Knill, David C

2007-05-23

Most research on depth cue integration has focused on stimulus regimes in which stimuli contain the small cue conflicts that one might expect to normally arise from sensory noise. In these regimes, linear models for cue integration provide a good approximation to system performance. This article focuses on situations in which large cue conflicts can naturally occur in stimuli. We describe a Bayesian model for nonlinear cue integration that makes rational inferences about scenes across the entire range of possible cue conflicts. The model derives from the simple intuition that multiple properties of scenes or causal factors give rise to the image information associated with most cues. To make perceptual inferences about one property of a scene, an ideal observer must necessarily take into account the possible contribution of these other factors to the information provided by a cue. In the context of classical depth cues, large cue conflicts most commonly arise when one or another cue is generated by an object or scene that violates the strongest form of constraint that makes the cue informative. For example, when binocularly viewing a slanted trapezoid, the slant interpretation of the figure derived by assuming that the figure is rectangular may conflict greatly with the slant suggested by stereoscopic disparities. An optimal Bayesian estimator incorporates the possibility that different constraints might apply to objects in the world and robustly integrates cues with large conflicts by effectively switching between different internal models of the prior constraints underlying one or both cues. We performed two experiments to test the predictions of the model when applied to estimating surface slant from binocular disparities and the compression cue (the aspect ratio of figures in an image). The apparent weight that subjects gave to the compression cue decreased smoothly as a function of the conflict between the cues but did not shrink to zero; that is, subjects did not fully veto the compression cue at large cue conflicts. A Bayesian model that assumes a mixed prior distribution of figure shapes in the world, with a large proportion being very regular and a smaller proportion having random shapes, provides a good quantitative fit for subjects' performance. The best fitting model parameters are consistent with the sensory noise to be expected in measurements of figure shape, further supporting the Bayesian model as an account of robust cue integration.

Bayesian structural inference for hidden processes.

PubMed

Strelioff, Christopher C; Crutchfield, James P

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Bayesian structural inference for hidden processes

NASA Astrophysics Data System (ADS)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Annealed Importance Sampling for Neural Mass Models

PubMed Central

Penny, Will; Sengupta, Biswa

2016-01-01

Neural Mass Models provide a compact description of the dynamical activity of cell populations in neocortical regions. Moreover, models of regional activity can be connected together into networks, and inferences made about the strength of connections, using M/EEG data and Bayesian inference. To date, however, Bayesian methods have been largely restricted to the Variational Laplace (VL) algorithm which assumes that the posterior distribution is Gaussian and finds model parameters that are only locally optimal. This paper explores the use of Annealed Importance Sampling (AIS) to address these restrictions. We implement AIS using proposals derived from Langevin Monte Carlo (LMC) which uses local gradient and curvature information for efficient exploration of parameter space. In terms of the estimation of Bayes factors, VL and AIS agree about which model is best but report different degrees of belief. Additionally, AIS finds better model parameters and we find evidence of non-Gaussianity in their posterior distribution. PMID:26942606

Evaluating Variability and Uncertainty of Geological Strength Index at a Specific Site

NASA Astrophysics Data System (ADS)

Wang, Yu; Aladejare, Adeyemi Emman

2016-09-01

Geological Strength Index (GSI) is an important parameter for estimating rock mass properties. GSI can be estimated from quantitative GSI chart, as an alternative to the direct observational method which requires vast geological experience of rock. GSI chart was developed from past observations and engineering experience, with either empiricism or some theoretical simplifications. The GSI chart thereby contains model uncertainty which arises from its development. The presence of such model uncertainty affects the GSI estimated from GSI chart at a specific site; it is, therefore, imperative to quantify and incorporate the model uncertainty during GSI estimation from the GSI chart. A major challenge for quantifying the GSI chart model uncertainty is a lack of the original datasets that have been used to develop the GSI chart, since the GSI chart was developed from past experience without referring to specific datasets. This paper intends to tackle this problem by developing a Bayesian approach for quantifying the model uncertainty in GSI chart when using it to estimate GSI at a specific site. The model uncertainty in the GSI chart and the inherent spatial variability in GSI are modeled explicitly in the Bayesian approach. The Bayesian approach generates equivalent samples of GSI from the integrated knowledge of GSI chart, prior knowledge and observation data available from site investigation. Equations are derived for the Bayesian approach, and the proposed approach is illustrated using data from a drill and blast tunnel project. The proposed approach effectively tackles the problem of how to quantify the model uncertainty that arises from using GSI chart for characterization of site-specific GSI in a transparent manner.

A Bayesian model averaging method for the derivation of reservoir operating rules

NASA Astrophysics Data System (ADS)

Zhang, Jingwen; Liu, Pan; Wang, Hao; Lei, Xiaohui; Zhou, Yanlai

2015-09-01

Because the intrinsic dynamics among optimal decision making, inflow processes and reservoir characteristics are complex, functional forms of reservoir operating rules are always determined subjectively. As a result, the uncertainty of selecting form and/or model involved in reservoir operating rules must be analyzed and evaluated. In this study, we analyze the uncertainty of reservoir operating rules using the Bayesian model averaging (BMA) model. Three popular operating rules, namely piecewise linear regression, surface fitting and a least-squares support vector machine, are established based on the optimal deterministic reservoir operation. These individual models provide three-member decisions for the BMA combination, enabling the 90% release interval to be estimated by the Markov Chain Monte Carlo simulation. A case study of China's the Baise reservoir shows that: (1) the optimal deterministic reservoir operation, superior to any reservoir operating rules, is used as the samples to derive the rules; (2) the least-squares support vector machine model is more effective than both piecewise linear regression and surface fitting; (3) BMA outperforms any individual model of operating rules based on the optimal trajectories. It is revealed that the proposed model can reduce the uncertainty of operating rules, which is of great potential benefit in evaluating the confidence interval of decisions.

Stochastic Model of Seasonal Runoff Forecasts

NASA Astrophysics Data System (ADS)

Krzysztofowicz, Roman; Watada, Leslie M.

1986-03-01

Each year the National Weather Service and the Soil Conservation Service issue a monthly sequence of five (or six) categorical forecasts of the seasonal snowmelt runoff volume. To describe uncertainties in these forecasts for the purposes of optimal decision making, a stochastic model is formulated. It is a discrete-time, finite, continuous-space, nonstationary Markov process. Posterior densities of the actual runoff conditional upon a forecast, and transition densities of forecasts are obtained from a Bayesian information processor. Parametric densities are derived for the process with a normal prior density of the runoff and a linear model of the forecast error. The structure of the model and the estimation procedure are motivated by analyses of forecast records from five stations in the Snake River basin, from the period 1971-1983. The advantages of supplementing the current forecasting scheme with a Bayesian analysis are discussed.

Bayesian nonparametric dictionary learning for compressed sensing MRI.

PubMed

Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

2014-12-01

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

Mercury in Children: Current State on Exposure through Human Biomonitoring Studies

PubMed Central

Ruggieri, Flavia; Majorani, Costanza; Domanico, Francesco; Alimonti, Alessandro

2017-01-01

Mercury (Hg) in children has multiple exposure sources and the toxicity of Hg compounds depends on exposure routes, dose, timing of exposure, and developmental stage (be it prenatal or postnatal). Over the last decades, Hg was widely recognized as a threat to the children’s health and there have been acknowledgements at the international level of the need of a global policy intervention—like the Minamata treaty—aimed at reducing or preventing Hg exposure and protecting the child health. National human biomonitoring (HBM) data has demonstrated that low levels of exposure of Hg are still an important health concern for children, which no one country can solve alone. Although independent HBM surveys have provided the basis for the achievements of exposure mitigation in specific contexts, a new paradigm for a coordinated global monitoring of children’s exposure, aimed at a reliable decision-making tool at global level is yet a great challenge for the next future. The objective of the present review is to describe current HBM studies on Hg exposure in children, taking into account the potential pathways of Hg exposure and the actual Hg exposure levels assessed by different biomarkers. PMID:28498344

A Hierarchical Bayesian Procedure for Two-Mode Cluster Analysis

ERIC Educational Resources Information Center

DeSarbo, Wayne S.; Fong, Duncan K. H.; Liechty, John; Saxton, M. Kim

2004-01-01

This manuscript introduces a new Bayesian finite mixture methodology for the joint clustering of row and column stimuli/objects associated with two-mode asymmetric proximity, dominance, or profile data. That is, common clusters are derived which partition both the row and column stimuli/objects simultaneously into the same derived set of clusters.…

Experimentally Derived δ13C and δ15N Discrimination Factors for Gray Wolves and the Impact of Prior Information in Bayesian Mixing Models

PubMed Central

Bucci, Melanie E.; Callahan, Peggy; Koprowski, John L.; Polfus, Jean L.; Krausman, Paul R.

2015-01-01

Stable isotope analysis of diet has become a common tool in conservation research. However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable. PMID:25803664

Experimentally derived δ¹³C and δ¹⁵N discrimination factors for gray wolves and the impact of prior information in Bayesian mixing models.

PubMed

Derbridge, Jonathan J; Merkle, Jerod A; Bucci, Melanie E; Callahan, Peggy; Koprowski, John L; Polfus, Jean L; Krausman, Paul R

2015-01-01

Stable isotope analysis of diet has become a common tool in conservation research. However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable.

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

NASA Astrophysics Data System (ADS)

Han, Feng; Zheng, Yi

2018-06-01

Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.

Bayesian model evidence as a model evaluation metric

NASA Astrophysics Data System (ADS)

Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang

2017-04-01

When building environmental systems models, we are typically confronted with the questions of how to choose an appropriate model (i.e., which processes to include or neglect) and how to measure its quality. Various metrics have been proposed that shall guide the modeller towards a most robust and realistic representation of the system under study. Criteria for evaluation often address aspects of accuracy (absence of bias) or of precision (absence of unnecessary variance) and need to be combined in a meaningful way in order to address the inherent bias-variance dilemma. We suggest using Bayesian model evidence (BME) as a model evaluation metric that implicitly performs a tradeoff between bias and variance. BME is typically associated with model weights in the context of Bayesian model averaging (BMA). However, it can also be seen as a model evaluation metric in a single-model context or in model comparison. It combines a measure for goodness of fit with a penalty for unjustifiable complexity. Unjustifiable refers to the fact that the appropriate level of model complexity is limited by the amount of information available for calibration. Derived in a Bayesian context, BME naturally accounts for measurement errors in the calibration data as well as for input and parameter uncertainty. BME is therefore perfectly suitable to assess model quality under uncertainty. We will explain in detail and with schematic illustrations what BME measures, i.e. how complexity is defined in the Bayesian setting and how this complexity is balanced with goodness of fit. We will further discuss how BME compares to other model evaluation metrics that address accuracy and precision such as the predictive logscore or other model selection criteria such as the AIC, BIC or KIC. Although computationally more expensive than other metrics or criteria, BME represents an appealing alternative because it provides a global measure of model quality. Even if not applicable to each and every case, we aim at stimulating discussion about how to judge the quality of hydrological models in the presence of uncertainty in general by dissecting the mechanism behind BME.

Using the Health Belief Model to Explain Mothers' and Fathers' Intention to Participate in Universal Parenting Programs.

PubMed

Salari, Raziye; Filus, Ania

2017-01-01

Using the Health Belief Model (HBM) as a theoretical framework, we studied factors related to parental intention to participate in parenting programs and examined the moderating effects of parent gender on these factors. Participants were a community sample of 290 mothers and 290 fathers of 5- to 10-year-old children. Parents completed a set of questionnaires assessing child emotional and behavioral difficulties and the HBM constructs concerning perceived program benefits and barriers, perceived child problem susceptibility and severity, and perceived self-efficacy. The hypothesized model was evaluated using structural equation modeling. The results showed that, for both mothers and fathers, perceived program benefits were associated with higher intention to participate in parenting programs. In addition, higher intention to participate was associated with lower perceived barriers only in the sample of mothers and with higher perceived self-efficacy only in the sample of fathers. No significant relations were found between intention to participate and perceived child problem susceptibility and severity. Mediation analyses indicated that, for both mothers and fathers, child emotional and behavioral problems had an indirect effect on parents' intention to participate by increasing the level of perceived benefits of the program. As a whole, the proposed model explained about 45 % of the variance in parental intention to participate. The current study suggests that mothers and fathers may be motivated by different factors when making their decision to participate in a parenting program. This finding can inform future parent engagement strategies intended to increase both mothers' and fathers' participation rates in parenting programs.

A Bayesian kriging approach for blending satellite and ground precipitation observations

USGS Publications Warehouse

Verdin, Andrew P.; Rajagopalan, Balaji; Kleiber, William; Funk, Christopher C.

2015-01-01

Drought and flood management practices require accurate estimates of precipitation. Gauge observations, however, are often sparse in regions with complicated terrain, clustered in valleys, and of poor quality. Consequently, the spatial extent of wet events is poorly represented. Satellite-derived precipitation data are an attractive alternative, though they tend to underestimate the magnitude of wet events due to their dependency on retrieval algorithms and the indirect relationship between satellite infrared observations and precipitation intensities. Here we offer a Bayesian kriging approach for blending precipitation gauge data and the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates for Central America, Colombia, and Venezuela. First, the gauge observations are modeled as a linear function of satellite-derived estimates and any number of other variables—for this research we include elevation. Prior distributions are defined for all model parameters and the posterior distributions are obtained simultaneously via Markov chain Monte Carlo sampling. The posterior distributions of these parameters are required for spatial estimation, and thus are obtained prior to implementing the spatial kriging model. This functional framework is applied to model parameters obtained by sampling from the posterior distributions, and the residuals of the linear model are subject to a spatial kriging model. Consequently, the posterior distributions and uncertainties of the blended precipitation estimates are obtained. We demonstrate this method by applying it to pentadal and monthly total precipitation fields during 2009. The model's performance and its inherent ability to capture wet events are investigated. We show that this blending method significantly improves upon the satellite-derived estimates and is also competitive in its ability to represent wet events. This procedure also provides a means to estimate a full conditional distribution of the “true” observed precipitation value at each grid cell.

Bayesian statistics applied to the location of the source of explosions at Stromboli Volcano, Italy

USGS Publications Warehouse

Saccorotti, G.; Chouet, B.; Martini, M.; Scarpa, R.

1998-01-01

We present a method for determining the location and spatial extent of the source of explosions at Stromboli Volcano, Italy, based on a Bayesian inversion of the slowness vector derived from frequency-slowness analyses of array data. The method searches for source locations that minimize the error between the expected and observed slowness vectors. For a given set of model parameters, the conditional probability density function of slowness vectors is approximated by a Gaussian distribution of expected errors. The method is tested with synthetics using a five-layer velocity model derived for the north flank of Stromboli and a smoothed velocity model derived from a power-law approximation of the layered structure. Application to data from Stromboli allows for a detailed examination of uncertainties in source location due to experimental errors and incomplete knowledge of the Earth model. Although the solutions are not constrained in the radial direction, excellent resolution is achieved in both transverse and depth directions. Under the assumption that the horizontal extent of the source does not exceed the crater dimension, the 90% confidence region in the estimate of the explosive source location corresponds to a small volume extending from a depth of about 100 m to a maximum depth of about 300 m beneath the active vents, with a maximum likelihood source region located in the 120- to 180-m-depth interval.

Parameter estimation for an immortal model of colonic stem cell division using approximate Bayesian computation.

PubMed

Walters, Kevin

2012-08-07

In this paper we use approximate Bayesian computation to estimate the parameters in an immortal model of colonic stem cell division. We base the inferences on the observed DNA methylation patterns of cells sampled from the human colon. Utilising DNA methylation patterns as a form of molecular clock is an emerging area of research and has been used in several studies investigating colonic stem cell turnover. There is much debate concerning the two competing models of stem cell turnover: the symmetric (immortal) and asymmetric models. Early simulation studies concluded that the observed methylation data were not consistent with the immortal model. A later modified version of the immortal model that included preferential strand segregation was subsequently shown to be consistent with the same methylation data. Most of this earlier work assumes site independent methylation models that do not take account of the known processivity of methyltransferases whilst other work does not take into account the methylation errors that occur in differentiated cells. This paper addresses both of these issues for the immortal model and demonstrates that approximate Bayesian computation provides accurate estimates of the parameters in this neighbour-dependent model of methylation error rates. The results indicate that if colonic stem cells divide asymmetrically then colon stem cell niches are maintained by more than 8 stem cells. Results also indicate the possibility of preferential strand segregation and provide clear evidence against a site-independent model for methylation errors. In addition, algebraic expressions for some of the summary statistics used in the approximate Bayesian computation (that allow for the additional variation arising from cell division in differentiated cells) are derived and their utility discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Underestimation of Variance of Predicted Health Utilities Derived from Multiattribute Utility Instruments.

PubMed

Chan, Kelvin K W; Xie, Feng; Willan, Andrew R; Pullenayegum, Eleanor M

2017-04-01

Parameter uncertainty in value sets of multiattribute utility-based instruments (MAUIs) has received little attention previously. This false precision leads to underestimation of the uncertainty of the results of cost-effectiveness analyses. The aim of this study is to examine the use of multiple imputation as a method to account for this uncertainty of MAUI scoring algorithms. We fitted a Bayesian model with random effects for respondents and health states to the data from the original US EQ-5D-3L valuation study, thereby estimating the uncertainty in the EQ-5D-3L scoring algorithm. We applied these results to EQ-5D-3L data from the Commonwealth Fund (CWF) Survey for Sick Adults ( n = 3958), comparing the standard error of the estimated mean utility in the CWF population using the predictive distribution from the Bayesian mixed-effect model (i.e., incorporating parameter uncertainty in the value set) with the standard error of the estimated mean utilities based on multiple imputation and the standard error using the conventional approach of using MAUI (i.e., ignoring uncertainty in the value set). The mean utility in the CWF population based on the predictive distribution of the Bayesian model was 0.827 with a standard error (SE) of 0.011. When utilities were derived using the conventional approach, the estimated mean utility was 0.827 with an SE of 0.003, which is only 25% of the SE based on the full predictive distribution of the mixed-effect model. Using multiple imputation with 20 imputed sets, the mean utility was 0.828 with an SE of 0.011, which is similar to the SE based on the full predictive distribution. Ignoring uncertainty of the predicted health utilities derived from MAUIs could lead to substantial underestimation of the variance of mean utilities. Multiple imputation corrects for this underestimation so that the results of cost-effectiveness analyses using MAUIs can report the correct degree of uncertainty.

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.

PubMed

Ferragina, A; de los Campos, G; Vazquez, A I; Cecchinato, A; Bittante, G

2015-11-01

The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict "difficult-to-predict" dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm(-1) were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R(2) value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R(2) (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R(2) of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Simultaneous Optimization of Decisions Using a Linear Utility Function.

ERIC Educational Resources Information Center

Vos, Hans J.

1990-01-01

An approach is presented to simultaneously optimize decision rules for combinations of elementary decisions through a framework derived from Bayesian decision theory. The developed linear utility model for selection-mastery decisions was applied to a sample of 43 first year medical students to illustrate the procedure. (SLD)

Value of Weather Information in Cranberry Marketing Decisions.

NASA Astrophysics Data System (ADS)

Morzuch, Bernard J.; Willis, Cleve E.

1982-04-01

Econometric techniques are used to establish a functional relationship between cranberry yields and important precipitation, temperature, and sunshine variables. Crop forecasts are derived from the model and are used to establish posterior probabilities to be used in a Bayesian decision context pertaining to leasing space for the storage of the berries.

IZI: INFERRING THE GAS PHASE METALLICITY (Z) AND IONIZATION PARAMETER (q) OF IONIZED NEBULAE USING BAYESIAN STATISTICS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blanc, Guillermo A.; Kewley, Lisa; Vogt, Frédéric P. A.

2015-01-10

We present a new method for inferring the metallicity (Z) and ionization parameter (q) of H II regions and star-forming galaxies using strong nebular emission lines (SELs). We use Bayesian inference to derive the joint and marginalized posterior probability density functions for Z and q given a set of observed line fluxes and an input photoionization model. Our approach allows the use of arbitrary sets of SELs and the inclusion of flux upper limits. The method provides a self-consistent way of determining the physical conditions of ionized nebulae that is not tied to the arbitrary choice of a particular SELmore » diagnostic and uses all the available information. Unlike theoretically calibrated SEL diagnostics, the method is flexible and not tied to a particular photoionization model. We describe our algorithm, validate it against other methods, and present a tool that implements it called IZI. Using a sample of nearby extragalactic H II regions, we assess the performance of commonly used SEL abundance diagnostics. We also use a sample of 22 local H II regions having both direct and recombination line (RL) oxygen abundance measurements in the literature to study discrepancies in the abundance scale between different methods. We find that oxygen abundances derived through Bayesian inference using currently available photoionization models in the literature can be in good (∼30%) agreement with RL abundances, although some models perform significantly better than others. We also confirm that abundances measured using the direct method are typically ∼0.2 dex lower than both RL and photoionization-model-based abundances.« less

Skin Cancer Knowledge, Beliefs, Self-Efficacy, and Preventative Behaviors among North Mississippi Landscapers

PubMed Central

Ford, M. Allison; Hallam, Jeffrey S.; Bass, Martha A.; Vice, Michael A.

2013-01-01

There are slightly over one million workers in the landscape service industry in the US. These workers have potential for high levels of solar ultraviolet radiation exposure, increasing their risk of skin cancer. A cross-sectional sample of 109 landscapers completed a self-administered questionnaire based on Health Belief Model (HBM). The participants correctly answered 67.1% of the knowledge questions, 69.7% believed they were more likely than the average person to get skin cancer, and 87.2% perceived skin cancer as a severe disease. Participants believed that the use of wide-brimmed hats, long sleeved shirts/long pants, and sunscreen was beneficial but reported low usage of these and other sun protective strategies. The primary barriers to using sun protection were “I forget to wear it” and “it is too hot to wear.” Of the HBM variables, perceived benefits outweighing perceived barrier (r = .285, P = .003) and self-efficacy (r = .538, P = .001) were correlated with sun protection behaviors. The reasons for absence of the relationship between perceived skin cancer threat and sun protection behaviors could be lack of skin cancer knowledge and low rate of personal skin cancer history. PMID:24223037

Cost and culture: Factors influencing worksite physical activity across three universities.

PubMed

Rinaldi-Miles, Anna I; Das, Bhibha M

2016-11-22

Physical inactivity is a leading cause of morbidity and mortality. Worksites provide an ideal environment for physical activity (PA) interventions. Colleges and universities are a unique work venue, with institutions of higher education of varying scope within every state of the United States and worldwide. To explore the institutional influences on worksite PA across multiple universities. Employees from two large, universities (Midwestern and Southern) and a mid-size, university (Midwestern) participated in exploratory research in March/April 2010 and 2013. The Nominal Group Technique (NGT) methodology and the Health Belief Model (HBM) were used to assess perceived influences on employees' engagement in worksite PA. The findings demonstrate that university employees experienced similar factors that influence PA as employees across the different institutions. Specifically, there was an interesting relationship between opportunities for PA and lack of a supportive work culture to promote it. Emphasis on immediate perceived threats to PA inactivity may improve the utility of the HBM for interventions within this context. Further, campus worksite interventions for employees should address barriers such as cost of campus recreation centers and administrative support for engaging in worksite PA as possible cues to action.

Analytical study to define a helicopter stability derivative extraction method, volume 1

NASA Technical Reports Server (NTRS)

Molusis, J. A.

1973-01-01

A method is developed for extracting six degree-of-freedom stability and control derivatives from helicopter flight data. Different combinations of filtering and derivative estimate are investigated and used with a Bayesian approach for derivative identification. The combination of filtering and estimate found to yield the most accurate time response match to flight test data is determined and applied to CH-53A and CH-54B flight data. The method found to be most accurate consists of (1) filtering flight test data with a digital filter, followed by an extended Kalman filter (2) identifying a derivative estimate with a least square estimator, and (3) obtaining derivatives with the Bayesian derivative extraction method.

Estimating the effect of lay knowledge and prior contact with pulmonary TB patients, on health-belief model in a high-risk pulmonary TB transmission population.

PubMed

Zein, Rizqy Amelia; Suhariadi, Fendy; Hendriani, Wiwin

2017-01-01

The research aimed to investigate the effect of lay knowledge of pulmonary tuberculosis (TB) and prior contact with pulmonary TB patients on a health-belief model (HBM) as well as to identify the social determinants that affect lay knowledge. Survey research design was conducted, where participants were required to fill in a questionnaire, which measured HBM and lay knowledge of pulmonary TB. Research participants were 500 residents of Semampir, Asemrowo, Bubutan, Pabean Cantian, and Simokerto districts, where the risk of pulmonary TB transmission is higher than other districts in Surabaya. Being a female, older in age, and having prior contact with pulmonary TB patients significantly increase the likelihood of having a higher level of lay knowledge. Lay knowledge is a substantial determinant to estimate belief in the effectiveness of health behavior and personal health threat. Prior contact with pulmonary TB patients is able to explain the belief in the effectiveness of a health behavior, yet fails to estimate participants' belief in the personal health threat. Health authorities should prioritize males and young people as their main target groups in a pulmonary TB awareness campaign. The campaign should be able to reconstruct people's misconception about pulmonary TB, thereby bringing around the health-risk perception so that it is not solely focused on improving lay knowledge.

Effect of Planned Follow-up on Married Women's Health Beliefs and Behaviors Concerning Breast and Cervical Cancer Screenings.

PubMed

Kolutek, Rahsan; Avci, Ilknur Aydin; Sevig, Umit

2018-04-01

The objective of this study was to identify the effect of planned follow-up visits on married women's health beliefs and behaviors concerning breast and cervical cancer screenings. The study was conducted using the single-group pre-test/post-test and quasi-experimental study designs. The sample of the study included 153 women. Data were collected using a Personal Information Form, the Health Belief Model (HBM) Scale for Breast Cancer Screening, the HBM Scale for Cervical Cancer Screening, and a Pap smear test. Data were collected using the aforementioned tools from September 2012 to March 2013. Four follow-up visits were conducted, nurses were educated, and telephone reminders were utilized. Friedman's test, McNemar's test, and descriptive statistics were used for data analyzing. The frequency of performing breast self-examination (BSE) at the last visit increased to 84.3 % compared to the pre-training. A statistically significant difference was observed between the pre- and post-training median values in four subscales except for the subscale of perceived seriousness of cervical cancer under "the Health Belief Model Scale for Cervical Cancer and the Pap Smear Test" (p < 0.001). The rate of performing BSE significantly increased after the training and follow-up visits. Also, the rate of having a Pap smear significantly increased after the follow-up visits.

Assessing the effect of an educational intervention program based on Health Belief Model on preventive behaviors of internet addiction

PubMed Central

Maheri, Aghbabak; Tol, Azar; Sadeghi, Roya

2017-01-01

INTRODUCTION: Internet addiction refers to the excessive use of the internet that causes mental, social, and physical problems. According to the high prevalence of internet addiction among university students, this study aimed to determine the effect of an educational intervention on preventive behaviors of internet addiction among Tehran University of Medical Sciences students. MATERIALS AND METHODS: This study was a quasi-experimental study conducted among female college students who live in the dormitories of Tehran University of Medical Sciences. Two-stage cluster sampling was used for selection of eighty participants in each study groups; data were collected using “Young's Internet Addiction” and unstructured questionnaire. Validity and reliability of unstructured questionnaire were evaluated by expert panel and were reported as Cronbach's alpha. Information of study groups before and 4 months after the intervention was compared using statistical methods by SPSS 16. RESULTS: After the intervention, the mean scores of internet addiction, perceived barriers construct, and the prevalence of internet addiction significantly decreased in the intervention group than that in the control group and the mean scores of knowledge and Health Belief Model (HBM) constructs (susceptibility, severity, benefits, self-efficacy) significantly increased. CONCLUSIONS: Education based on the HBM was effective on the reduction and prevention of internet addiction among female college students, and educational interventions in this field are highly recommended. PMID:28852654

Moving beyond qualitative evaluations of Bayesian models of cognition.

PubMed

Hemmer, Pernille; Tauber, Sean; Steyvers, Mark

2015-06-01

Bayesian models of cognition provide a powerful way to understand the behavior and goals of individuals from a computational point of view. Much of the focus in the Bayesian cognitive modeling approach has been on qualitative model evaluations, where predictions from the models are compared to data that is often averaged over individuals. In many cognitive tasks, however, there are pervasive individual differences. We introduce an approach to directly infer individual differences related to subjective mental representations within the framework of Bayesian models of cognition. In this approach, Bayesian data analysis methods are used to estimate cognitive parameters and motivate the inference process within a Bayesian cognitive model. We illustrate this integrative Bayesian approach on a model of memory. We apply the model to behavioral data from a memory experiment involving the recall of heights of people. A cross-validation analysis shows that the Bayesian memory model with inferred subjective priors predicts withheld data better than a Bayesian model where the priors are based on environmental statistics. In addition, the model with inferred priors at the individual subject level led to the best overall generalization performance, suggesting that individual differences are important to consider in Bayesian models of cognition.

Qualitative to quantitative: linked trajectory of method triangulation in a study on HIV/AIDS in Goa, India.

PubMed

Bailey, Ajay; Hutter, Inge

2008-10-01

With 3.1 million people estimated to be living with HIV/AIDS in India and 39.5 million people globally, the epidemic has posed academics the challenge of identifying behaviours and their underlying beliefs in the effort to reduce the risk of HIV transmission. The Health Belief Model (HBM) is frequently used to identify risk behaviours and adherence behaviour in the field of HIV/AIDS. Risk behaviour studies that apply HBM have been largely quantitative and use of qualitative methodology is rare. The marriage of qualitative and quantitative methods has never been easy. The challenge is in triangulating the methods. Method triangulation has been largely used to combine insights from the qualitative and quantitative methods but not to link both the methods. In this paper we suggest a linked trajectory of method triangulation (LTMT). The linked trajectory aims to first gather individual level information through in-depth interviews and then to present the information as vignettes in focus group discussions. We thus validate information obtained from in-depth interviews and gather emic concepts that arise from the interaction. We thus capture both the interpretation and the interaction angles of the qualitative method. Further, using the qualitative information gained, a survey is designed. In doing so, the survey questions are grounded and contextualized. We employed this linked trajectory of method triangulation in a study on the risk assessment of HIV/AIDS among migrant and mobile men. Fieldwork was carried out in Goa, India. Data come from two waves of studies, first an explorative qualitative study (2003), second a larger study (2004-2005), including in-depth interviews (25), focus group discussions (21) and a survey (n=1259). By employing the qualitative to quantitative LTMT we can not only contextualize the existing concepts of the HBM, but also validate new concepts and identify new risk groups.

A new treatment for predicting the self-excited vibrations of nonlinear systems with frictional interfaces: The Constrained Harmonic Balance Method, with application to disc brake squeal

NASA Astrophysics Data System (ADS)

Coudeyras, N.; Sinou, J.-J.; Nacivet, S.

2009-01-01

Brake squeal noise is still an issue since it generates high warranty costs for the automotive industry and irritation for customers. Key parameters must be known in order to reduce it. Stability analysis is a common method of studying nonlinear phenomena and has been widely used by the scientific and the engineering communities for solving disc brake squeal problems. This type of analysis provides areas of stability versus instability for driven parameters, thereby making it possible to define design criteria. Nevertheless, this technique does not permit obtaining the vibrating state of the brake system and nonlinear methods have to be employed. Temporal integration is a well-known method for computing the dynamic solution but as it is time consuming, nonlinear methods such as the Harmonic Balance Method (HBM) are preferred. This paper presents a novel nonlinear method called the Constrained Harmonic Balance Method (CHBM) that works for nonlinear systems subject to flutter instability. An additional constraint-based condition is proposed that omits the static equilibrium point (i.e. the trivial static solution of the nonlinear problem that would be obtained by applying the classical HBM) and therefore focuses on predicting both the Fourier coefficients and the fundamental frequency of the stationary nonlinear system. The effectiveness of the proposed nonlinear approach is illustrated by an analysis of disc brake squeal. The brake system under consideration is a reduced finite element model of a pad and a disc. Both stability and nonlinear analyses are performed and the results are compared with a classical variable order solver integration algorithm. Therefore, the objectives of the following paper are to present not only an extension of the HBM (CHBM) but also to demonstrate an application to the specific problem of disc brake squeal with extensively parametric studies that investigate the effects of the friction coefficient, piston pressure, nonlinear stiffness and structural damping.

Is the Jeffreys' scale a reliable tool for Bayesian model comparison in cosmology?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nesseris, Savvas; García-Bellido, Juan, E-mail: savvas.nesseris@uam.es, E-mail: juan.garciabellido@uam.es

2013-08-01

We are entering an era where progress in cosmology is driven by data, and alternative models will have to be compared and ruled out according to some consistent criterium. The most conservative and widely used approach is Bayesian model comparison. In this paper we explicitly calculate the Bayes factors for all models that are linear with respect to their parameters. We do this in order to test the so called Jeffreys' scale and determine analytically how accurate its predictions are in a simple case where we fully understand and can calculate everything analytically. We also discuss the case of nestedmore » models, e.g. one with M{sub 1} and another with M{sub 2} superset of M{sub 1} parameters and we derive analytic expressions for both the Bayes factor and the figure of Merit, defined as the inverse area of the model parameter's confidence contours. With all this machinery and the use of an explicit example we demonstrate that the threshold nature of Jeffreys' scale is not a ''one size fits all'' reliable tool for model comparison and that it may lead to biased conclusions. Furthermore, we discuss the importance of choosing the right basis in the context of models that are linear with respect to their parameters and how that basis affects the parameter estimation and the derived constraints.« less

Variational Bayesian Learning for Wavelet Independent Component Analysis

NASA Astrophysics Data System (ADS)

Roussos, E.; Roberts, S.; Daubechies, I.

2005-11-01

In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or "sources" via a generally unknown mapping. For the noisy overcomplete case, where we have more sources than observations, the problem becomes extremely ill-posed. Solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian methodology, allowing us to incorporate "soft" constraints in a natural manner. The work described in this paper is mainly driven by problems in functional magnetic resonance imaging of the brain, for the neuro-scientific goal of extracting relevant "maps" from the data. This can be stated as a `blind' source separation problem. Recent experiments in the field of neuroscience show that these maps are sparse, in some appropriate sense. The separation problem can be solved by independent component analysis (ICA), viewed as a technique for seeking sparse components, assuming appropriate distributions for the sources. We derive a hybrid wavelet-ICA model, transforming the signals into a domain where the modeling assumption of sparsity of the coefficients with respect to a dictionary is natural. We follow a graphical modeling formalism, viewing ICA as a probabilistic generative model. We use hierarchical source and mixing models and apply Bayesian inference to the problem. This allows us to perform model selection in order to infer the complexity of the representation, as well as automatic denoising. Since exact inference and learning in such a model is intractable, we follow a variational Bayesian mean-field approach in the conjugate-exponential family of distributions, for efficient unsupervised learning in multi-dimensional settings. The performance of the proposed algorithm is demonstrated on some representative experiments.

Bayesian reconstruction of projection reconstruction NMR (PR-NMR).

PubMed

Yoon, Ji Won

2014-11-01

Projection reconstruction nuclear magnetic resonance (PR-NMR) is a technique for generating multidimensional NMR spectra. A small number of projections from lower-dimensional NMR spectra are used to reconstruct the multidimensional NMR spectra. In our previous work, it was shown that multidimensional NMR spectra are efficiently reconstructed using peak-by-peak based reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. We propose an extended and generalized RJMCMC algorithm replacing a simple linear model with a linear mixed model to reconstruct close NMR spectra into true spectra. This statistical method generates samples in a Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of a protein HasA. Copyright © 2014 Elsevier Ltd. All rights reserved.

Associations between human breast milk hormones and adipocytokines and infant growth and body composition in the first 6 months of life.

PubMed

Fields, D A; George, B; Williams, M; Whitaker, K; Allison, D B; Teague, A; Demerath, E W

2017-08-01

Much is to be learnt about human breast milk (HBM). The purpose of this study is to extend our knowledge of HBM by investigating the role of maternal body mass index (BMI), sex and stage of lactation (month 1 vs. 6) on HBM insulin, glucose, leptin, IL-6 and TNF-α and their associations with infant body composition. Thirty-seven exclusively breastfeeding infants (n = 37; 16♀, 21♂), and their mothers (19-47 kg m -2 ) were studied at 1 and 6 months of lactation. Infants had body composition measured (using dual-energy X-ray absorptiometry) and HBM collected. A significant interaction between maternal BMI and infant sex on insulin levels (p = 0.0322) was observed such that insulin was 229% higher in obese mothers nursing female infants than in normal weight mothers nursing female infants and 179% higher than obese mothers nursing male infants. For leptin, a significant association with BMI category was observed (p < 0.0001) such that overweight and obese mothers had 96.5% and 315.1% higher leptin levels than normal weight mothers, respectively. Leptin was also found to have a significant (p = 0.0004) 33.7% decrease from months 1 to 6, controlling for BMI category and sex. A significant inverse relationship between month 1 leptin levels and infant length (p = 0.0257), percent fat (p = 0.0223), total fat mass (p = 0.0226) and trunk fat mass (p = 0.0111) at month 6 was also found. No associations or interactions were observed for glucose, TNF-α or IL-6. These data demonstrate that maternal BMI, infant sex and stage of lactation affect the compositional make-up of insulin and leptin. © 2017 World Obesity Federation.

Normal hematopoiesis and lack of β-catenin activation in osteoblasts of patients and mice harboring Lrp5 gain-of-function mutations.

PubMed

Galán-Díez, Marta; Isa, Adiba; Ponzetti, Marco; Nielsen, Morten Frost; Kassem, Moustapha; Kousteni, Stavroula

2016-03-01

Osteoblasts are emerging regulators of myeloid malignancies since genetic alterations in them, such as constitutive activation of β-catenin, instigate their appearance. The LDL receptor-related protein 5 (LRP5), initially proposed to be a co-receptor for Wnt proteins, in fact favors bone formation by suppressing gut-serotonin synthesis. This function of Lrp5 occurring in the gut is independent of β-catenin activation in osteoblasts. However, it is unknown whether Lrp5 can act directly in osteoblast to influence other functions that require β-catenin signaling, particularly, the deregulation of hematopoiesis and leukemogenic properties of β-catenin activation in osteoblasts, that lead to development of acute myeloid leukemia (AML). Using mice with gain-of-function (GOF) Lrp5 alleles (Lrp5(A214V)) that recapitulate the human high bone mass (HBM) phenotype, as well as patients with the T253I HBM Lrp5 mutation, we show here that Lrp5 GOF mutations in both humans and mice do not activate β-catenin signaling in osteoblasts. Consistent with a lack of β-catenin activation in their osteoblasts, Lrp5(A214V) mice have normal trilinear hematopoiesis. In contrast to leukemic mice with constitutive activation of β-catenin in osteoblasts (Ctnnb1(CAosb)), accumulation of early myeloid progenitors, a characteristic of AML, myeloid-blasts in blood, and segmented neutrophils or dysplastic megakaryocytes in the bone marrow, are not observed in Lrp5(A214V) mice. Likewise, peripheral blood count analysis in HBM patients showed normal hematopoiesis, normal percentage of myeloid cells, and lack of anemia. We conclude that Lrp5 GOF mutations do not activate β-catenin signaling in osteoblasts. As a result, myeloid lineage differentiation is normal in HBM patients and mice. This article is part of a Special Issue entitled: Tumor Microenvironment Regulation of Cancer Cell Survival, Metastasis, Inflammation, and Immune Surveillance edited by Peter Ruvolo and Gregg L. Semenza. Published by Elsevier B.V.

Quantum Graphical Models and Belief Propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leifer, M.S.; Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo Ont., N2L 2Y5; Poulin, D.

Belief Propagation algorithms acting on Graphical Models of classical probability distributions, such as Markov Networks, Factor Graphs and Bayesian Networks, are amongst the most powerful known methods for deriving probabilistic inferences amongst large numbers of random variables. This paper presents a generalization of these concepts and methods to the quantum case, based on the idea that quantum theory can be thought of as a noncommutative, operator-valued, generalization of classical probability theory. Some novel characterizations of quantum conditional independence are derived, and definitions of Quantum n-Bifactor Networks, Markov Networks, Factor Graphs and Bayesian Networks are proposed. The structure of Quantum Markovmore » Networks is investigated and some partial characterization results are obtained, along the lines of the Hammersley-Clifford theorem. A Quantum Belief Propagation algorithm is presented and is shown to converge on 1-Bifactor Networks and Markov Networks when the underlying graph is a tree. The use of Quantum Belief Propagation as a heuristic algorithm in cases where it is not known to converge is discussed. Applications to decoding quantum error correcting codes and to the simulation of many-body quantum systems are described.« less

Bayesian networks and information theory for audio-visual perception modeling.

PubMed

Besson, Patricia; Richiardi, Jonas; Bourdin, Christophe; Bringoux, Lionel; Mestre, Daniel R; Vercher, Jean-Louis

2010-09-01

Thanks to their different senses, human observers acquire multiple information coming from their environment. Complex cross-modal interactions occur during this perceptual process. This article proposes a framework to analyze and model these interactions through a rigorous and systematic data-driven process. This requires considering the general relationships between the physical events or factors involved in the process, not only in quantitative terms, but also in term of the influence of one factor on another. We use tools from information theory and probabilistic reasoning to derive relationships between the random variables of interest, where the central notion is that of conditional independence. Using mutual information analysis to guide the model elicitation process, a probabilistic causal model encoded as a Bayesian network is obtained. We exemplify the method by using data collected in an audio-visual localization task for human subjects, and we show that it yields a well-motivated model with good predictive ability. The model elicitation process offers new prospects for the investigation of the cognitive mechanisms of multisensory perception.

A Bayesian model for estimating population means using a link-tracing sampling design.

PubMed

St Clair, Katherine; O'Connell, Daniel

2012-03-01

Link-tracing sampling designs can be used to study human populations that contain "hidden" groups who tend to be linked together by a common social trait. These links can be used to increase the sampling intensity of a hidden domain by tracing links from individuals selected in an initial wave of sampling to additional domain members. Chow and Thompson (2003, Survey Methodology 29, 197-205) derived a Bayesian model to estimate the size or proportion of individuals in the hidden population for certain link-tracing designs. We propose an addition to their model that will allow for the modeling of a quantitative response. We assess properties of our model using a constructed population and a real population of at-risk individuals, both of which contain two domains of hidden and nonhidden individuals. Our results show that our model can produce good point and interval estimates of the population mean and domain means when our population assumptions are satisfied. © 2011, The International Biometric Society.

Multiscale Bayesian neural networks for soil water content estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.; Springer, Everett P.

2008-08-01

Artificial neural networks (ANN) have been used for some time now to estimate soil hydraulic parameters from other available or more easily measurable soil properties. However, most such uses of ANNs as pedotransfer functions (PTFs) have been at matching spatial scales (1:1) of inputs and outputs. This approach assumes that the outputs are only required at the same scale as the input data. Unfortunately, this is rarely true. Different hydrologic, hydroclimatic, and contaminant transport models require soil hydraulic parameter data at different spatial scales, depending upon their grid sizes. While conventional (deterministic) ANNs have been traditionally used in these studies, the use of Bayesian training of ANNs is a more recent development. In this paper, we develop a Bayesian framework to derive soil water retention function including its uncertainty at the point or local scale using PTFs trained with coarser-scale Soil Survey Geographic (SSURGO)-based soil data. The approach includes an ANN trained with Bayesian techniques as a PTF tool with training and validation data collected across spatial extents (scales) in two different regions in the United States. The two study areas include the Las Cruces Trench site in the Rio Grande basin of New Mexico, and the Southern Great Plains 1997 (SGP97) hydrology experimental region in Oklahoma. Each region-specific Bayesian ANN is trained using soil texture and bulk density data from the SSURGO database (scale 1:24,000), and predictions of the soil water contents at different pressure heads with point scale data (1:1) inputs are made. The resulting outputs are corrected for bias using both linear and nonlinear correction techniques. The results show good agreement between the soil water content values measured at the point scale and those predicted by the Bayesian ANN-based PTFs for both the study sites. Overall, Bayesian ANNs coupled with nonlinear bias correction are found to be very suitable tools for deriving soil hydraulic parameters at the local/fine scale from soil physical properties at coarser-scale and across different spatial extents. This approach could potentially be used for soil hydraulic properties estimation and downscaling.

Environmental interventions based on the Health Belief Model and the Ecological-social model in the continuation of consumption of rice, free from toxic metals

PubMed Central

Shafiei, Leili; Maleki, Afshin; Sayehmiri, Kourosh

2018-01-01

Background and aim Continuation of healthy nutritional behaviors is one of the important factors in effectiveness of educational intervention programs. The aim of this research is to compare the Health Belief Model and the Ecological-social model in reducing consumption of rice contaminated with toxic metals after completion of environmental intervention and continuation of consumption of healthy rice. Methods This research was the implementation of a six-month randomized controlled trial interventional program in two groups’ interventions along with a control group, with 80 people for each group totally, amounting to 240 women, between 18 and 50 years of age in Ilam, Iran in 2014. The questionnaires of the three groups consisted of demographic information, knowledge, the constructs of the models, performance of rice consumption. Friedman test and repeated measures used for data analysis with SPSS (version 20), and confidence interval of 95% were considered. Results The results of the Friedman test indicated a significant increase in the number of women consuming healthy rice over six months after intervention in both intervention groups (p<0.001). Women in the ECO group consumed healthy rice 27.5% more than the HBM group (p<0.001). The results of repeated measures analysis of variance suggested greater improvement in the consumption of healthy rice in the ECO group in comparison with the HBM group over six months after intervention (p<0.05). Conclusions Both educational environmental intervention methods caused the altered diet of people regarding consumption of healthy rice over six months after the intervention. Increased social support also probably had a more effective role in continuation of healthy diet among the people. PMID:29588814

Current education versus peer-education on walking in type 2 diabetic patients based on Health Belief Model: a randomized control trial study.

PubMed

Baghianimoghadam, M H; Hadavandkhani, M; Mohammadi, M; Fallahzade, H; Baghianimoghadam, B

2012-01-01

Diabetes is a disease with several metabolic and organic symptoms. Physical activity plays a key role in controlling type 2 diabetes. Several researches confirm that educational strategies can lead to healthy behaviors and its continuation is effective and can indicate what type of relationship with the client is better. The purpose of this study is comparing the Effect of Current Education and Peer-Education on Walking in Type 2 Diabetic Patients based on Health Belief Model (HBM). This was a clinical trial (RCT) study done on 80 people with type 2 diabetes. Patients were divided into two groups, Current education and Peer education groups. Data were collected using a questionnaire based on the health belief model, a checklist related to patients' practice and recording patients' HbA1c, 2HPP and FBS levels. Results were documented before and three months after intervention. The patients participated in 2 educational classes during three months of intervention, as the follow-up of the intervention. Mean scores for HBM Model variables, i.e. perceived susceptibility, perceived severity, perceived benefit and self-efficacy, were significantly increased in the peer education group compared to current education group after intervention. Also, behavioral walking, rates of HbA1c and FBS and 2HPP levels were improved significantly among the peer education group. Applying walking training program developed for diabetic patients and its implementation by the peers in order to control blood sugar using the health belief model is very useful and effective. During implementation of these control programs, monitoring and follow-up training is recommended.

Resolution analysis of marine seismic full waveform data by Bayesian inversion

NASA Astrophysics Data System (ADS)

Ray, A.; Sekar, A.; Hoversten, G. M.; Albertin, U.

2015-12-01

The Bayesian posterior density function (PDF) of earth models that fit full waveform seismic data convey information on the uncertainty with which the elastic model parameters are resolved. In this work, we apply the trans-dimensional reversible jump Markov Chain Monte Carlo method (RJ-MCMC) for the 1D inversion of noisy synthetic full-waveform seismic data in the frequency-wavenumber domain. While seismic full waveform inversion (FWI) is a powerful method for characterizing subsurface elastic parameters, the uncertainty in the inverted models has remained poorly known, if at all and is highly initial model dependent. The Bayesian method we use is trans-dimensional in that the number of model layers is not fixed, and flexible such that the layer boundaries are free to move around. The resulting parameterization does not require regularization to stabilize the inversion. Depth resolution is traded off with the number of layers, providing an estimate of uncertainty in elastic parameters (compressional and shear velocities Vp and Vs as well as density) with depth. We find that in the absence of additional constraints, Bayesian inversion can result in a wide range of posterior PDFs on Vp, Vs and density. These PDFs range from being clustered around the true model, to those that contain little resolution of any particular features other than those in the near surface, depending on the particular data and target geometry. We present results for a suite of different frequencies and offset ranges, examining the differences in the posterior model densities thus derived. Though these results are for a 1D earth, they are applicable to areas with simple, layered geology and provide valuable insight into the resolving capabilities of FWI, as well as highlight the challenges in solving a highly non-linear problem. The RJ-MCMC method also presents a tantalizing possibility for extension to 2D and 3D Bayesian inversion of full waveform seismic data in the future, as it objectively tackles the problem of model selection (i.e., the number of layers or cells for parameterization), which could ease the computational burden of evaluating forward models with many parameters.

Comparison between the effects of positive noncatastrophic HMB ESD stress in n-channel and p-channel power MOSFET's

NASA Astrophysics Data System (ADS)

Zupac, Dragan; Kosier, Steven L.; Schrimpf, Ronald D.; Galloway, Kenneth F.; Baum, Keith W.

1991-10-01

The effect of noncatastrophic positive human body model (HBM) electrostatic discharge (ESD) stress on n-channel power MOSFETs is radically different from that on p-channel MOSFETs. In n-channel transistors, the stress causes negative shifts of the current-voltage characteristics indicative of positive charge trapping in the gate oxide. In p-channel transistors, the stress increases the drain-to-source leakage current, probably due to localized avalanche electron injection from the p-doped drain.

Stochastic Modeling of the Environmental Impacts of the Mingtang Tunneling Project

NASA Astrophysics Data System (ADS)

Li, Xiaojun; Li, Yandong; Chang, Ching-Fu; Chen, Ziyang; Tan, Benjamin Zhi Wen; Sege, Jon; Wang, Changhong; Rubin, Yoram

2017-04-01

This paper investigates the environmental impacts of a major tunneling project in China. Of particular interest is the drawdown of the water table, due to its potential impacts on ecosystem health and on agricultural activity. Due to scarcity of data, the study pursues a Bayesian stochastic approach, which is built around a numerical model. We adopted the Bayesian approach with the goal of deriving the posterior distributions of the dependent variables conditional on local data. The choice of the Bayesian approach for this study is somewhat non-trivial because of the scarcity of in-situ measurements. The thought guiding this selection is that prior distributions for the model input variables are valuable tools even if that all inputs are available, the Bayesian approach could provide a good starting point for further updates as and if additional data becomes available. To construct effective priors, a systematic approach was developed and implemented for constructing informative priors based on other, well-documented sites which bear geological and hydrological similarity to the target site (the Mingtang tunneling project). The approach is built around two classes of similarity criteria: a physically-based set of criteria and an additional set covering epistemic criteria. The prior construction strategy was implemented for the hydraulic conductivity of various types of rocks at the site (Granite and Gneiss) and for modeling the geometry and conductivity of the fault zones. Additional elements of our strategy include (1) modeling the water table through bounding surfaces representing upper and lower limits, and (2) modeling the effective conductivity as a random variable (varying between realizations, not in space). The approach was tested successfully against its ability to predict the tunnel infiltration fluxes and against observations of drying soils.

A Bayesian Nonparametric Approach to Test Equating

ERIC Educational Resources Information Center

Karabatsos, George; Walker, Stephen G.

2009-01-01

A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…

Sampling-free Bayesian inversion with adaptive hierarchical tensor representations

NASA Astrophysics Data System (ADS)

Eigel, Martin; Marschall, Manuel; Schneider, Reinhold

2018-03-01

A sampling-free approach to Bayesian inversion with an explicit polynomial representation of the parameter densities is developed, based on an affine-parametric representation of a linear forward model. This becomes feasible due to the complete treatment in function spaces, which requires an efficient model reduction technique for numerical computations. The advocated perspective yields the crucial benefit that error bounds can be derived for all occuring approximations, leading to provable convergence subject to the discretization parameters. Moreover, it enables a fully adaptive a posteriori control with automatic problem-dependent adjustments of the employed discretizations. The method is discussed in the context of modern hierarchical tensor representations, which are used for the evaluation of a random PDE (the forward model) and the subsequent high-dimensional quadrature of the log-likelihood, alleviating the ‘curse of dimensionality’. Numerical experiments demonstrate the performance and confirm the theoretical results.

Model Diagnostics for Bayesian Networks

ERIC Educational Resources Information Center

Sinharay, Sandip

2006-01-01

Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…

Performance of two predictive uncertainty estimation approaches for conceptual Rainfall-Runoff Model: Bayesian Joint Inference and Hydrologic Uncertainty Post-processing

NASA Astrophysics Data System (ADS)

Hernández-López, Mario R.; Romero-Cuéllar, Jonathan; Camilo Múnera-Estrada, Juan; Coccia, Gabriele; Francés, Félix

2017-04-01

It is noticeably important to emphasize the role of uncertainty particularly when the model forecasts are used to support decision-making and water management. This research compares two approaches for the evaluation of the predictive uncertainty in hydrological modeling. First approach is the Bayesian Joint Inference of hydrological and error models. Second approach is carried out through the Model Conditional Processor using the Truncated Normal Distribution in the transformed space. This comparison is focused on the predictive distribution reliability. The case study is applied to two basins included in the Model Parameter Estimation Experiment (MOPEX). These two basins, which have different hydrological complexity, are the French Broad River (North Carolina) and the Guadalupe River (Texas). The results indicate that generally, both approaches are able to provide similar predictive performances. However, the differences between them can arise in basins with complex hydrology (e.g. ephemeral basins). This is because obtained results with Bayesian Joint Inference are strongly dependent on the suitability of the hypothesized error model. Similarly, the results in the case of the Model Conditional Processor are mainly influenced by the selected model of tails or even by the selected full probability distribution model of the data in the real space, and by the definition of the Truncated Normal Distribution in the transformed space. In summary, the different hypotheses that the modeler choose on each of the two approaches are the main cause of the different results. This research also explores a proper combination of both methodologies which could be useful to achieve less biased hydrological parameter estimation. For this approach, firstly the predictive distribution is obtained through the Model Conditional Processor. Secondly, this predictive distribution is used to derive the corresponding additive error model which is employed for the hydrological parameter estimation with the Bayesian Joint Inference methodology.

Order-Constrained Reference Priors with Implications for Bayesian Isotonic Regression, Analysis of Covariance and Spatial Models

NASA Astrophysics Data System (ADS)

Gong, Maozhen

Selecting an appropriate prior distribution is a fundamental issue in Bayesian Statistics. In this dissertation, under the framework provided by Berger and Bernardo, I derive the reference priors for several models which include: Analysis of Variance (ANOVA)/Analysis of Covariance (ANCOVA) models with a categorical variable under common ordering constraints, the conditionally autoregressive (CAR) models and the simultaneous autoregressive (SAR) models with a spatial autoregression parameter rho considered. The performances of reference priors for ANOVA/ANCOVA models are evaluated by simulation studies with comparisons to Jeffreys' prior and Least Squares Estimation (LSE). The priors are then illustrated in a Bayesian model of the "Risk of Type 2 Diabetes in New Mexico" data, where the relationship between the type 2 diabetes risk (through Hemoglobin A1c) and different smoking levels is investigated. In both simulation studies and real data set modeling, the reference priors that incorporate internal order information show good performances and can be used as default priors. The reference priors for the CAR and SAR models are also illustrated in the "1999 SAT State Average Verbal Scores" data with a comparison to a Uniform prior distribution. Due to the complexity of the reference priors for both CAR and SAR models, only a portion (12 states in the Midwest) of the original data set is considered. The reference priors can give a different marginal posterior distribution compared to a Uniform prior, which provides an alternative for prior specifications for areal data in Spatial statistics.

Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: An example from a vertigo phase III study with longitudinal count data as primary endpoint

PubMed Central

2012-01-01

Background A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. Methods We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). Results The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. Conclusions The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint. PMID:22962944

Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: an example from a vertigo phase III study with longitudinal count data as primary endpoint.

PubMed

Adrion, Christine; Mansmann, Ulrich

2012-09-10

A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint.

Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

PubMed Central

Lo, Benjamin W. Y.; Macdonald, R. Loch; Baker, Andrew; Levine, Mitchell A. H.

2013-01-01

Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication. PMID:23690884

Uncertainty Quantification of Hypothesis Testing for the Integrated Knowledge Engine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cuellar, Leticia

2012-05-31

The Integrated Knowledge Engine (IKE) is a tool of Bayesian analysis, based on Bayesian Belief Networks or Bayesian networks for short. A Bayesian network is a graphical model (directed acyclic graph) that allows representing the probabilistic structure of many variables assuming a localized type of dependency called the Markov property. The Markov property in this instance makes any node or random variable to be independent of any non-descendant node given information about its parent. A direct consequence of this property is that it is relatively easy to incorporate new evidence and derive the appropriate consequences, which in general is notmore » an easy or feasible task. Typically we use Bayesian networks as predictive models for a small subset of the variables, either the leave nodes or the root nodes. In IKE, since most applications deal with diagnostics, we are interested in predicting the likelihood of the root nodes given new observations on any of the children nodes. The root nodes represent the various possible outcomes of the analysis, and an important problem is to determine when we have gathered enough evidence to lean toward one of these particular outcomes. This document presents criteria to decide when the evidence gathered is sufficient to draw a particular conclusion or decide in favor of a particular outcome by quantifying the uncertainty in the conclusions that are drawn from the data. The material in this document is organized as follows: Section 2 presents briefly a forensics Bayesian network, and we explore evaluating the information provided by new evidence by looking first at the posterior distribution of the nodes of interest, and then at the corresponding posterior odds ratios. Section 3 presents a third alternative: Bayes Factors. In section 4 we finalize by showing the relation between the posterior odds ratios and Bayes factors and showing examples these cases, and in section 5 we conclude by providing clear guidelines of how to use these for the type of Bayesian networks used in IKE.« less

Development of an On-board Failure Diagnostics and Prognostics System for Solid Rocket Booster

NASA Technical Reports Server (NTRS)

Smelyanskiy, Vadim N.; Luchinsky, Dmitry G.; Osipov, Vyatcheslav V.; Timucin, Dogan A.; Uckun, Serdar

2009-01-01

We develop a case breach model for the on-board fault diagnostics and prognostics system for subscale solid-rocket boosters (SRBs). The model development was motivated by recent ground firing tests, in which a deviation of measured time-traces from the predicted time-series was observed. A modified model takes into account the nozzle ablation, including the effect of roughness of the nozzle surface, the geometry of the fault, and erosion and burning of the walls of the hole in the metal case. The derived low-dimensional performance model (LDPM) of the fault can reproduce the observed time-series data very well. To verify the performance of the LDPM we build a FLUENT model of the case breach fault and demonstrate a good agreement between theoretical predictions based on the analytical solution of the model equations and the results of the FLUENT simulations. We then incorporate the derived LDPM into an inferential Bayesian framework and verify performance of the Bayesian algorithm for the diagnostics and prognostics of the case breach fault. It is shown that the obtained LDPM allows one to track parameters of the SRB during the flight in real time, to diagnose case breach fault, and to predict its values in the future. The application of the method to fault diagnostics and prognostics (FD&P) of other SRB faults modes is discussed.

Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network

PubMed Central

Saleh, Lokman; Ajami, Hicham; Mili, Hafedh

2017-01-01

In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD) have been published. However, how to derive relevant attributes and early detection of COPD exacerbations remains a challenge. In this research work, we will use an efficient algorithm to select relevant attributes where there is no proper approach in this domain. Such algorithm predicts exacerbations with high accuracy by adding discretization process, and organizes the pertinent attributes in priority order based on their impact to facilitate the emergency medical treatment. In this paper, we propose an extension of our existing Helper Context-Aware Engine System (HCES) for COPD. This project uses Bayesian network algorithm to depict the dependency between the COPD symptoms (attributes) in order to overcome the insufficiency and the independency hypothesis of naïve Bayesian. In addition, the dependency in Bayesian network is realized using TAN algorithm rather than consulting pneumologists. All these combined algorithms (discretization, selection, dependency, and the ordering of the relevant attributes) constitute an effective prediction model, comparing to effective ones. Moreover, an investigation and comparison of different scenarios of these algorithms are also done to verify which sequence of steps of prediction model gives more accurate results. Finally, we designed and validated a computer-aided support application to integrate different steps of this model. The findings of our system HCES has shown promising results using Area Under Receiver Operating Characteristic (AUC = 81.5%). PMID:28644419

Additive effect of mesenchymal stem cells and defibrotide in an arterial rat thrombosis model.

PubMed

Dilli, Dilek; Kılıç, Emine; Yumuşak, Nihat; Beken, Serdar; Uçkan Çetinkaya, Duygu; Karabulut, Ramazan; Zenciroğlu, Ayşegu L

2017-06-01

In this study, we aimed to investigate the additive effect of mesenchymal stem cells (MSC) and defibrotide (DFT) in a rat model of femoral arterial thrombosis. Thirty Sprague Dawley rats were included. An arterial thrombosis model by ferric chloride (FeCl3) was developed in the left femoral artery. The rats were equally assigned to 5 groups: Group 1-Sham-operated (without arterial injury); Group 2-Phosphate buffered saline (PBS) injected; Group 3-MSC; Group 4-DFT; Group 5-MSC + DFT. All had two intraperitoneal injections of 0.5 ml: the 1st injection was 4 h after the procedure and the 2nd one 48 h after the 1st injection. The rats were sacrificed 7 days after the 2nd injection. Although the use of human bone marrow-derived (hBM) hBM-MSC or DFT alone enabled partial resolution of the thrombus, combining them resulted in near-complete resolution. Neovascularization was two-fold better in hBM-MSC + DFT treated rats (11.6 ± 2.4 channels) compared with the hBM-MSC (3.8 ± 2.7 channels) and DFT groups (5.5 ± 1.8 channels) (P < 0.0001 and P= 0.002, respectively). The combined use of hBM-MSC and DFT in a rat model of arterial thrombosis showed additive effect resulting in near-complete resolution of the thrombus.

Bayesian Model Averaging for Propensity Score Analysis

ERIC Educational Resources Information Center

Kaplan, David; Chen, Jianshen

2013-01-01

The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…

Accounting for imperfect detection in Hill numbers for biodiversity studies

USGS Publications Warehouse

Broms, Kristin M.; Hooten, Mevin B.; Fitzpatrick, Ryan M.

2015-01-01

The occupancy-based Hill number estimators are always at their asymptotic values (i.e. as if an infinite number of samples have been taken for the study region), therefore making it easy to compare biodiversity between different assemblages. In addition, the Hill numbers are computed as derived quantities within a Bayesian hierarchical model, allowing for straightforward inference.

Bayesian Inference for Functional Dynamics Exploring in fMRI Data.

PubMed

Guo, Xuan; Liu, Bing; Chen, Le; Chen, Guantao; Pan, Yi; Zhang, Jing

2016-01-01

This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

Bayesian Community Detection in the Space of Group-Level Functional Differences

PubMed Central

Venkataraman, Archana; Yang, Daniel Y.-J.; Pelphrey, Kevin A.; Duncan, James S.

2017-01-01

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder. PMID:26955022

Bayesian Community Detection in the Space of Group-Level Functional Differences.

PubMed

Venkataraman, Archana; Yang, Daniel Y-J; Pelphrey, Kevin A; Duncan, James S

2016-08-01

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.

Bayesian depth estimation from monocular natural images.

PubMed

Su, Che-Chun; Cormack, Lawrence K; Bovik, Alan C

2017-05-01

Estimating an accurate and naturalistic dense depth map from a single monocular photographic image is a difficult problem. Nevertheless, human observers have little difficulty understanding the depth structure implied by photographs. Two-dimensional (2D) images of the real-world environment contain significant statistical information regarding the three-dimensional (3D) structure of the world that the vision system likely exploits to compute perceived depth, monocularly as well as binocularly. Toward understanding how this might be accomplished, we propose a Bayesian model of monocular depth computation that recovers detailed 3D scene structures by extracting reliable, robust, depth-sensitive statistical features from single natural images. These features are derived using well-accepted univariate natural scene statistics (NSS) models and recent bivariate/correlation NSS models that describe the relationships between 2D photographic images and their associated depth maps. This is accomplished by building a dictionary of canonical local depth patterns from which NSS features are extracted as prior information. The dictionary is used to create a multivariate Gaussian mixture (MGM) likelihood model that associates local image features with depth patterns. A simple Bayesian predictor is then used to form spatial depth estimates. The depth results produced by the model, despite its simplicity, correlate well with ground-truth depths measured by a current-generation terrestrial light detection and ranging (LIDAR) scanner. Such a strong form of statistical depth information could be used by the visual system when creating overall estimated depth maps incorporating stereopsis, accommodation, and other conditions. Indeed, even in isolation, the Bayesian predictor delivers depth estimates that are competitive with state-of-the-art "computer vision" methods that utilize highly engineered image features and sophisticated machine learning algorithms.

New technologies - How to assess environmental effects

NASA Technical Reports Server (NTRS)

Sullivan, P. J.; Lavin, M. L.

1981-01-01

A method is provided for assessing the environmental effects of a room-and-pillar mining system (RP) and a new hydraulic borehole mining system (HBM). Before environmental assessment can begin, each technology is defined in terms of its engineering characteristics at both the conceptual and preliminary design stages. The mining sites are also described in order to identify the significant advantages and constraints for each system. This can be a basic physical and biological survey of the region at the conceptual stage, but a more specific representation of site characteristics is required at the preliminary stage. Assessment of potential environmental effects of each system at the conceptual design is critical to its hardware development and application. A checklist can be used to compare and identify the negative impacts of each method, outlining the resource affected, the type of impact involved, and the exact activity causing that impact. At the preliminary design stage, these impacts should be evaluated as a result of either utilization or alteration. Underground coal mining systems have three major utilization impacts - the total area disturbed, the total water resources withdrawn from other uses, and the overall energy efficiency of the process - and one major alteration impact - the degradation of water quality by sedimentation and acid contamination. A comparison of the RP and HBM systems shows the HBM to be an environmentally less desirable system for the Central Appalachia region.

Advancing Data assimilation for Baltic Monitoring and Forecasting Center: implementation and evaluation of HBP-PDAF system

NASA Astrophysics Data System (ADS)

Korabel, Vasily; She, Jun; Huess, Vibeke; Woge Nielsen, Jacob; Murawsky, Jens; Nerger, Lars

2017-04-01

The potential of an efficient data assimilation (DA) scheme to improve model forecast skill was successfully demonstrated by many operational centres around the world. The Baltic-North Sea region is one of the most heavily monitored seas. Ferryboxes, buoys, ADCP moorings, shallow water Argo floats, and research vessels are providing more and more near-real time observations. Coastal altimetry has now providing increasing amount of high resolution sea level observations, which will be significantly expanded by the launch of SWOT satellite in next years. This will turn operational DA into a valuable tool for improving forecast quality in the region. This motivated us to focus on advancing DA for the Baltic Monitoring and Forecasting Centre (BAL MFC) in order to create a common framework for operational data assimilation in the Baltic Sea. We have implemented HBM-PDAF system based on the Parallel Data Assimilation Framework (PDAF), a highly versatile and optimised parallel suit with a choice of sequential schemes originally developed at AWI, and a hydrodynamic HIROMB-BOOS Model (HBM). At initial phase, only the satellite Sea Surface Temperature (SST) Level 3 data has been assimilated. Several related aspects are discussed, including improvements of the forecast quality for both surface and subsurface fields, the estimation of ensemble-based forecast error covariance, as well as possibilities of assimilating new types of observations, such as in-situ salinity and temperature profiles, coastal altimetry, and ice concentration.

Factors associated with perception of risk of contracting HIV among secondary school female learners in Mbonge subdivision of rural Cameroon.

PubMed

Tarkang, Elvis Enowbeyang

2014-01-01

Since learners in secondary schools fall within the age group hardest hit by HIV/AIDS, it is obvious that these learners might be at high risk of contracting HIV/AIDS. However, little has been explored on the perception of risk of contracting HIV among secondary school learners in Cameroon. This study aimed at examining the perception of risk of contracting HIV among secondary school learners in Mbonge subdivision of rural Cameroon using the Health Belief Model (HBM) as framework. A quantitative, correlational design was adopted, using a self-administered questionnaire to collect data from 210 female learners selected through disproportional, stratified, simple random sampling technique, from three participating senior secondary schools. Statistics were calculated using SPSS version 20 software program. Only 39.4% of the respondents perceived themselves to be at high risk of contracting HIV, though the majority, 54.0% were sexually active. Multinomial logistic regression analyses show that sexual risk behaviours (p=0.000) and the Integrated Value Mapping (IVM) of the perception components of the HBM are the most significant factors associated with perception of risk of contracting HIV at the level p<0.05. The findings of this study can play an instrumental role in the development of effective preventive and interventional messages for adolescents in Cameroon.

Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Biros, George

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. Thesemore » include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.« less

A 3-Component Mixture of Rayleigh Distributions: Properties and Estimation in Bayesian Framework

PubMed Central

Aslam, Muhammad; Tahir, Muhammad; Hussain, Zawar; Al-Zahrani, Bander

2015-01-01

To study lifetimes of certain engineering processes, a lifetime model which can accommodate the nature of such processes is desired. The mixture models of underlying lifetime distributions are intuitively more appropriate and appealing to model the heterogeneous nature of process as compared to simple models. This paper is about studying a 3-component mixture of the Rayleigh distributionsin Bayesian perspective. The censored sampling environment is considered due to its popularity in reliability theory and survival analysis. The expressions for the Bayes estimators and their posterior risks are derived under different scenarios. In case the case that no or little prior information is available, elicitation of hyperparameters is given. To examine, numerically, the performance of the Bayes estimators using non-informative and informative priors under different loss functions, we have simulated their statistical properties for different sample sizes and test termination times. In addition, to highlight the practical significance, an illustrative example based on a real-life engineering data is also given. PMID:25993475

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

O'Malley, A. James; Zou, Kelly H.

2006-01-01

SUMMARY A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box–Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial. PMID:16217836

A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.

PubMed

Huertas, Ismael; Oldehinkel, Marianne; van Oort, Erik S B; Garcia-Solis, David; Mir, Pablo; Beckmann, Christian F; Marquand, Andre F

2017-11-01

The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data are characterised as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state. This model is estimated using a Bayesian framework which accurately quantifies uncertainty and automatically finds the most accurate and parsimonious combination of basis functions describing the data. We demonstrate the utility of this framework by predicting quantitative SPECT images of striatal dopamine function and we compare a variety of basis sets including generic isotropic functions, anatomical representations of the striatum derived from structural MRI, and two different soft functional parcellations of the striatum derived from resting-state fMRI (rfMRI). We found that a combination of ∼50 multiscale functional basis functions accurately represented the striatal dopamine activity, and that functional basis functions derived from an advanced parcellation technique known as Instantaneous Connectivity Parcellation (ICP) provided the most parsimonious models of dopamine function. Importantly, functional basis functions derived from resting fMRI were more accurate than both structural and generic basis sets in representing dopamine function in the striatum for a fixed model order. We demonstrate the translational validity of our framework by constructing classification models for discriminating parkinsonian disorders and their subtypes. Here, we show that ICP approach is the only basis set that performs well across all comparisons and performs better overall than the classical voxel-based approach. This spatial model constitutes an elegant alternative to voxel-based approaches in neuroimaging studies; not only are their atoms biologically informed, they are also adaptive to high resolutions, represent high dimensions efficiently, and capture long-range spatial dependencies, which are important and challenging objectives for neuroimaging data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Bayesian structural equation modeling in sport and exercise psychology.

PubMed

Stenling, Andreas; Ivarsson, Andreas; Johnson, Urban; Lindwall, Magnus

2015-08-01

Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.

Bayesian model reduction and empirical Bayes for group (DCM) studies

PubMed Central

Friston, Karl J.; Litvak, Vladimir; Oswal, Ashwini; Razi, Adeel; Stephan, Klaas E.; van Wijk, Bernadette C.M.; Ziegler, Gabriel; Zeidman, Peter

2016-01-01

This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level – e.g., dynamic causal models – and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction. PMID:26569570

An introduction to using Bayesian linear regression with clinical data.

PubMed

Baldwin, Scott A; Larson, Michael J

2017-11-01

Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Applications of Bayesian spectrum representation in acoustics

NASA Astrophysics Data System (ADS)

Botts, Jonathan M.

This dissertation utilizes a Bayesian inference framework to enhance the solution of inverse problems where the forward model maps to acoustic spectra. A Bayesian solution to filter design inverts a acoustic spectra to pole-zero locations of a discrete-time filter model. Spatial sound field analysis with a spherical microphone array is a data analysis problem that requires inversion of spatio-temporal spectra to directions of arrival. As with many inverse problems, a probabilistic analysis results in richer solutions than can be achieved with ad-hoc methods. In the filter design problem, the Bayesian inversion results in globally optimal coefficient estimates as well as an estimate the most concise filter capable of representing the given spectrum, within a single framework. This approach is demonstrated on synthetic spectra, head-related transfer function spectra, and measured acoustic reflection spectra. The Bayesian model-based analysis of spatial room impulse responses is presented as an analogous problem with equally rich solution. The model selection mechanism provides an estimate of the number of arrivals, which is necessary to properly infer the directions of simultaneous arrivals. Although, spectrum inversion problems are fairly ubiquitous, the scope of this dissertation has been limited to these two and derivative problems. The Bayesian approach to filter design is demonstrated on an artificial spectrum to illustrate the model comparison mechanism and then on measured head-related transfer functions to show the potential range of application. Coupled with sampling methods, the Bayesian approach is shown to outperform least-squares filter design methods commonly used in commercial software, confirming the need for a global search of the parameter space. The resulting designs are shown to be comparable to those that result from global optimization methods, but the Bayesian approach has the added advantage of a filter length estimate within the same unified framework. The application to reflection data is useful for representing frequency-dependent impedance boundaries in finite difference acoustic simulations. Furthermore, since the filter transfer function is a parametric model, it can be modified to incorporate arbitrary frequency weighting and account for the band-limited nature of measured reflection spectra. Finally, the model is modified to compensate for dispersive error in the finite difference simulation, from the filter design process. Stemming from the filter boundary problem, the implementation of pressure sources in finite difference simulation is addressed in order to assure that schemes properly converge. A class of parameterized source functions is proposed and shown to offer straightforward control of residual error in the simulation. Guided by the notion that the solution to be approximated affects the approximation error, sources are designed which reduce residual dispersive error to the size of round-off errors. The early part of a room impulse response can be characterized by a series of isolated plane waves. Measured with an array of microphones, plane waves map to a directional response of the array or spatial intensity map. Probabilistic inversion of this response results in estimates of the number and directions of image source arrivals. The model-based inversion is shown to avoid ambiguities associated with peak-finding or inspection of the spatial intensity map. For this problem, determining the number of arrivals in a given frame is critical for properly inferring the state of the sound field. This analysis is effectively compression of the spatial room response, which is useful for analysis or encoding of the spatial sound field. Parametric, model-based formulations of these problems enhance the solution in all cases, and a Bayesian interpretation provides a principled approach to model comparison and parameter estimation. v

Using GOMS and Bayesian plan recognition to develop recognition models of operator behavior

NASA Astrophysics Data System (ADS)

Zaientz, Jack D.; DeKoven, Elyon; Piegdon, Nicholas; Wood, Scott D.; Huber, Marcus J.

2006-05-01

Trends in combat technology research point to an increasing role for uninhabited vehicles in modern warfare tactics. To support increased span of control over these vehicles human responsibilities need to be transformed from tedious, error-prone and cognition intensive operations into tasks that are more supervisory and manageable, even under intensely stressful conditions. The goal is to move away from only supporting human command of low-level system functions to intention-level human-system dialogue about the operator's tasks and situation. A critical element of this process is developing the means to identify when human operators need automated assistance and to identify what assistance they need. Toward this goal, we are developing an unmanned vehicle operator task recognition system that combines work in human behavior modeling and Bayesian plan recognition. Traditionally, human behavior models have been considered generative, meaning they describe all possible valid behaviors. Basing behavior recognition on models designed for behavior generation can offers advantages in improved model fidelity and reuse. It is not clear, however, how to reconcile the structural differences between behavior recognition and behavior modeling approaches. Our current work demonstrates that by pairing a cognitive psychology derived human behavior modeling approach, GOMS, with a Bayesian plan recognition engine, ASPRN, we can translate a behavior generation model into a recognition model. We will discuss the implications for using human performance models in this manner as well as suggest how this kind of modeling may be used to support the real-time control of multiple, uninhabited battlefield vehicles and other semi-autonomous systems.

Hepatitis disease detection using Bayesian theory

NASA Astrophysics Data System (ADS)

Maseleno, Andino; Hidayati, Rohmah Zahroh

2017-02-01

This paper presents hepatitis disease diagnosis using a Bayesian theory for better understanding of the theory. In this research, we used a Bayesian theory for detecting hepatitis disease and displaying the result of diagnosis process. Bayesian algorithm theory is rediscovered and perfected by Laplace, the basic idea is using of the known prior probability and conditional probability density parameter, based on Bayes theorem to calculate the corresponding posterior probability, and then obtained the posterior probability to infer and make decisions. Bayesian methods combine existing knowledge, prior probabilities, with additional knowledge derived from new data, the likelihood function. The initial symptoms of hepatitis which include malaise, fever and headache. The probability of hepatitis given the presence of malaise, fever, and headache. The result revealed that a Bayesian theory has successfully identified the existence of hepatitis disease.

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

NASA Astrophysics Data System (ADS)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

Bayesian models: A statistical primer for ecologists

USGS Publications Warehouse

Hobbs, N. Thompson; Hooten, Mevin B.

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

A Bayesian framework to estimate diversification rates and their variation through time and space

PubMed Central

2011-01-01

Background Patterns of species diversity are the result of speciation and extinction processes, and molecular phylogenetic data can provide valuable information to derive their variability through time and across clades. Bayesian Markov chain Monte Carlo methods offer a promising framework to incorporate phylogenetic uncertainty when estimating rates of diversification. Results We introduce a new approach to estimate diversification rates in a Bayesian framework over a distribution of trees under various constant and variable rate birth-death and pure-birth models, and test it on simulated phylogenies. Furthermore, speciation and extinction rates and their posterior credibility intervals can be estimated while accounting for non-random taxon sampling. The framework is particularly suitable for hypothesis testing using Bayes factors, as we demonstrate analyzing dated phylogenies of Chondrostoma (Cyprinidae) and Lupinus (Fabaceae). In addition, we develop a model that extends the rate estimation to a meta-analysis framework in which different data sets are combined in a single analysis to detect general temporal and spatial trends in diversification. Conclusions Our approach provides a flexible framework for the estimation of diversification parameters and hypothesis testing while simultaneously accounting for uncertainties in the divergence times and incomplete taxon sampling. PMID:22013891

Predicting Rotator Cuff Tears Using Data Mining and Bayesian Likelihood Ratios

PubMed Central

Lu, Hsueh-Yi; Huang, Chen-Yuan; Su, Chwen-Tzeng; Lin, Chen-Chiang

2014-01-01

Objectives Rotator cuff tear is a common cause of shoulder diseases. Correct diagnosis of rotator cuff tears can save patients from further invasive, costly and painful tests. This study used predictive data mining and Bayesian theory to improve the accuracy of diagnosing rotator cuff tears by clinical examination alone. Methods In this retrospective study, 169 patients who had a preliminary diagnosis of rotator cuff tear on the basis of clinical evaluation followed by confirmatory MRI between 2007 and 2011 were identified. MRI was used as a reference standard to classify rotator cuff tears. The predictor variable was the clinical assessment results, which consisted of 16 attributes. This study employed 2 data mining methods (ANN and the decision tree) and a statistical method (logistic regression) to classify the rotator cuff diagnosis into “tear” and “no tear” groups. Likelihood ratio and Bayesian theory were applied to estimate the probability of rotator cuff tears based on the results of the prediction models. Results Our proposed data mining procedures outperformed the classic statistical method. The correction rate, sensitivity, specificity and area under the ROC curve of predicting a rotator cuff tear were statistical better in the ANN and decision tree models compared to logistic regression. Based on likelihood ratios derived from our prediction models, Fagan's nomogram could be constructed to assess the probability of a patient who has a rotator cuff tear using a pretest probability and a prediction result (tear or no tear). Conclusions Our predictive data mining models, combined with likelihood ratios and Bayesian theory, appear to be good tools to classify rotator cuff tears as well as determine the probability of the presence of the disease to enhance diagnostic decision making for rotator cuff tears. PMID:24733553

Research on ethics in two large Human Biomonitoring projects ECNIS and NewGeneris: a bottom up approach.

PubMed

Dumez, Birgit; Van Damme, Karel; Casteleyn, Ludwine

2008-06-05

Assessment of ethical aspects and authorization by ethics committees have become a major constraint for health research including human subjects. Ethical reference values often are extrapolated from clinical settings, where emphasis lies on decisional autonomy and protection of individual's privacy. The question rises if this set of values used in clinical research can be considered as relevant references for HBM research, which is at the basis of public health surveillance. Current and future research activities using human biomarkers are facing new challenges and expectancies on sensitive socio-ethical issues. Reflection is needed on the necessity to balance individual rights against public interest. In addition, many HBM research programs require international collaboration. Domestic legislation is not always easily applicable in international projects. Also, there seem to be considerable inconsistencies in ethical assessments of similar research activities between different countries and even within one country. All this is causing delay and putting the researcher in situations in which it is unclear how to act in accordance with necessary legal requirements. Therefore, analysis of ethical practices and their consequences for HBM research is needed.This analysis will be performed by a bottom-up approach, based on a methodology for comparative analysis of determinants in ethical reasoning, allowing taking into account different social, cultural, political and historical traditions, in view of safeguarding common EU values. Based on information collected in real life complexity, paradigm cases and virtual case scenarios will be developed and discussed with relevant stakeholders to openly discuss possible obstacles and to identify options for improvement in regulation. The material collected will allow developing an ethical framework which may constitute the basis for a more harmonized and consistent socio-ethical and legal approach. This will not only increase the possibilities for comparison between data generated but may also allow for more equality in the protection of the rights of European citizens and establish trustful relationships between science and society, based on firmly rooted ethical values within the EU legislative framework.These considerations outline part of the research on legal, socio-ethical and communication aspects of HBM within the scope of ECNIS (NoE) and NewGeneris (IP).

Research on ethics in two large Human Biomonitoring projects ECNIS and NewGeneris: a bottom up approach

PubMed Central

Dumez, Birgit; Van Damme, Karel; Casteleyn, Ludwine

2008-01-01

Assessment of ethical aspects and authorization by ethics committees have become a major constraint for health research including human subjects. Ethical reference values often are extrapolated from clinical settings, where emphasis lies on decisional autonomy and protection of individual's privacy. The question rises if this set of values used in clinical research can be considered as relevant references for HBM research, which is at the basis of public health surveillance. Current and future research activities using human biomarkers are facing new challenges and expectancies on sensitive socio-ethical issues. Reflection is needed on the necessity to balance individual rights against public interest. In addition, many HBM research programs require international collaboration. Domestic legislation is not always easily applicable in international projects. Also, there seem to be considerable inconsistencies in ethical assessments of similar research activities between different countries and even within one country. All this is causing delay and putting the researcher in situations in which it is unclear how to act in accordance with necessary legal requirements. Therefore, analysis of ethical practices and their consequences for HBM research is needed. This analysis will be performed by a bottom-up approach, based on a methodology for comparative analysis of determinants in ethical reasoning, allowing taking into account different social, cultural, political and historical traditions, in view of safeguarding common EU values. Based on information collected in real life complexity, paradigm cases and virtual case scenarios will be developed and discussed with relevant stakeholders to openly discuss possible obstacles and to identify options for improvement in regulation. The material collected will allow developing an ethical framework which may constitute the basis for a more harmonized and consistent socio-ethical and legal approach. This will not only increase the possibilities for comparison between data generated but may also allow for more equality in the protection of the rights of European citizens and establish trustful relationships between science and society, based on firmly rooted ethical values within the EU legislative framework. These considerations outline part of the research on legal, socio-ethical and communication aspects of HBM within the scope of ECNIS (NoE) and NewGeneris (IP). PMID:18541073

Bayesian approach to estimate AUC, partition coefficient and drug targeting index for studies with serial sacrifice design.

PubMed

Wang, Tianli; Baron, Kyle; Zhong, Wei; Brundage, Richard; Elmquist, William

2014-03-01

The current study presents a Bayesian approach to non-compartmental analysis (NCA), which provides the accurate and precise estimate of AUC 0 (∞) and any AUC 0 (∞) -based NCA parameter or derivation. In order to assess the performance of the proposed method, 1,000 simulated datasets were generated in different scenarios. A Bayesian method was used to estimate the tissue and plasma AUC 0 (∞) s and the tissue-to-plasma AUC 0 (∞) ratio. The posterior medians and the coverage of 95% credible intervals for the true parameter values were examined. The method was applied to laboratory data from a mice brain distribution study with serial sacrifice design for illustration. Bayesian NCA approach is accurate and precise in point estimation of the AUC 0 (∞) and the partition coefficient under a serial sacrifice design. It also provides a consistently good variance estimate, even considering the variability of the data and the physiological structure of the pharmacokinetic model. The application in the case study obtained a physiologically reasonable posterior distribution of AUC, with a posterior median close to the value estimated by classic Bailer-type methods. This Bayesian NCA approach for sparse data analysis provides statistical inference on the variability of AUC 0 (∞) -based parameters such as partition coefficient and drug targeting index, so that the comparison of these parameters following destructive sampling becomes statistically feasible.

An algebraic aspect of Pareto mixture parameter estimation using censored sample: A Bayesian approach.

PubMed

Saleem, Muhammad; Sharif, Kashif; Fahmi, Aliya

2018-04-27

Applications of Pareto distribution are common in reliability, survival and financial studies. In this paper, A Pareto mixture distribution is considered to model a heterogeneous population comprising of two subgroups. Each of two subgroups is characterized by the same functional form with unknown distinct shape and scale parameters. Bayes estimators have been derived using flat and conjugate priors using squared error loss function. Standard errors have also been derived for the Bayes estimators. An interesting feature of this study is the preparation of components of Fisher Information matrix.

Optimal modeling of 1D azimuth correlations in the context of Bayesian inference

NASA Astrophysics Data System (ADS)

De Kock, Michiel B.; Eggers, Hans C.; Trainor, Thomas A.

2015-09-01

Analysis and interpretation of spectrum and correlation data from high-energy nuclear collisions is currently controversial because two opposing physics narratives derive contradictory implications from the same data, one narrative claiming collision dynamics is dominated by dijet production and projectile-nucleon fragmentation, the other claiming collision dynamics is dominated by a dense, flowing QCD medium. Opposing interpretations seem to be supported by alternative data models, and current model-comparison schemes are unable to distinguish between them. There is clearly need for a convincing new methodology to break the deadlock. In this study we introduce Bayesian inference (BI) methods applied to angular correlation data as a basis to evaluate competing data models. For simplicity the data considered are projections of two-dimensional (2D) angular correlations onto a 1D azimuth from three centrality classes of 200-GeV Au-Au collisions. We consider several data models typical of current model choices, including Fourier series (FS) and a Gaussian plus various combinations of individual cosine components. We evaluate model performance with BI methods and with power-spectrum analysis. We find that FS-only models are rejected in all cases by Bayesian analysis, which always prefers a Gaussian. A cylindrical quadrupole cos(2 ϕ ) is required in some cases but rejected for 0%-5%-central Au-Au collisions. Given a Gaussian centered at the azimuth origin, "higher harmonics" cos(m ϕ ) for m >2 are rejected. A model consisting of Gaussian +dipole cos(ϕ )+quadrupole cos(2 ϕ ) provides good 1D data descriptions in all cases.

Bayesian model reduction and empirical Bayes for group (DCM) studies.

PubMed

Friston, Karl J; Litvak, Vladimir; Oswal, Ashwini; Razi, Adeel; Stephan, Klaas E; van Wijk, Bernadette C M; Ziegler, Gabriel; Zeidman, Peter

2016-03-01

This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level - e.g., dynamic causal models - and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

DOE PAGES

Wang, Liming; Huang, Jiaji; Yuan, Xin; ...

2015-09-17

The measurement matrix employed in compressive sensing typically cannot be known precisely a priori and must be estimated via calibration. One may take multiple compressive measurements, from which the measurement matrix and underlying signals may be estimated jointly. This is of interest as well when the measurement matrix may change as a function of the details of what is measured. This problem has been considered recently for Gaussian measurement noise, and here we develop this idea with application to Poisson systems. A collaborative maximum likelihood algorithm and alternating proximal gradient algorithm are proposed, and associated theoretical performance guarantees are establishedmore » based on newly derived concentration-of-measure results. A Bayesian model is then introduced, to improve flexibility and generality. Connections between the maximum likelihood methods and the Bayesian model are developed, and example results are presented for a real compressive X-ray imaging system.« less

A study of finite mixture model: Bayesian approach on financial time series data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-07-01

Recently, statistician have emphasized on the fitting finite mixture model by using Bayesian method. Finite mixture model is a mixture of distributions in modeling a statistical distribution meanwhile Bayesian method is a statistical method that use to fit the mixture model. Bayesian method is being used widely because it has asymptotic properties which provide remarkable result. In addition, Bayesian method also shows consistency characteristic which means the parameter estimates are close to the predictive distributions. In the present paper, the number of components for mixture model is studied by using Bayesian Information Criterion. Identify the number of component is important because it may lead to an invalid result. Later, the Bayesian method is utilized to fit the k-component mixture model in order to explore the relationship between rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia. Lastly, the results showed that there is a negative effect among rubber price and stock market price for all selected countries.

PIMS: Memristor-Based Processing-in-Memory-and-Storage.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Jeanine

Continued progress in computing has augmented the quest for higher performance with a new quest for higher energy efficiency. This has led to the re-emergence of Processing-In-Memory (PIM) ar- chitectures that offer higher density and performance with some boost in energy efficiency. Past PIM work either integrated a standard CPU with a conventional DRAM to improve the CPU- memory link, or used a bit-level processor with Single Instruction Multiple Data (SIMD) control, but neither matched the energy consumption of the memory to the computation. We originally proposed to develop a new architecture derived from PIM that more effectively addressed energymore » efficiency for high performance scientific, data analytics, and neuromorphic applications. We also originally planned to implement a von Neumann architecture with arithmetic/logic units (ALUs) that matched the power consumption of an advanced storage array to maximize energy efficiency. Implementing this architecture in storage was our original idea, since by augmenting storage (in- stead of memory), the system could address both in-memory computation and applications that accessed larger data sets directly from storage, hence Processing-in-Memory-and-Storage (PIMS). However, as our research matured, we discovered several things that changed our original direc- tion, the most important being that a PIM that implements a standard von Neumann-type archi- tecture results in significant energy efficiency improvement, but only about a O(10) performance improvement. In addition to this, the emergence of new memory technologies moved us to propos- ing a non-von Neumann architecture, called Superstrider, implemented not in storage, but in a new DRAM technology called High Bandwidth Memory (HBM). HBM is a stacked DRAM tech- nology that includes a logic layer where an architecture such as Superstrider could potentially be implemented.« less

Bayesian Data-Model Fit Assessment for Structural Equation Modeling

ERIC Educational Resources Information Center

Levy, Roy

2011-01-01

Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes…

Fire risk in San Diego County, California: A weighted Bayesian model approach

USGS Publications Warehouse

Kolden, Crystal A.; Weigel, Timothy J.

2007-01-01

Fire risk models are widely utilized to mitigate wildfire hazards, but models are often based on expert opinions of less understood fire-ignition and spread processes. In this study, we used an empirically derived weights-of-evidence model to assess what factors produce fire ignitions east of San Diego, California. We created and validated a dynamic model of fire-ignition risk based on land characteristics and existing fire-ignition history data, and predicted ignition risk for a future urbanization scenario. We then combined our empirical ignition-risk model with a fuzzy fire behavior-risk model developed by wildfire experts to create a hybrid model of overall fire risk. We found that roads influence fire ignitions and that future growth will increase risk in new rural development areas. We conclude that empirically derived risk models and hybrid models offer an alternative method to assess current and future fire risk based on management actions.

Toward improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom-up control hypothesis using high-resolution topographic data

NASA Astrophysics Data System (ADS)

Gomes, Guilherme J. C.; Vrugt, Jasper A.; Vargas, Eurípedes A.

2016-04-01

The depth to bedrock controls a myriad of processes by influencing subsurface flow paths, erosion rates, soil moisture, and water uptake by plant roots. As hillslope interiors are very difficult and costly to illuminate and access, the topography of the bedrock surface is largely unknown. This essay is concerned with the prediction of spatial patterns in the depth to bedrock (DTB) using high-resolution topographic data, numerical modeling, and Bayesian analysis. Our DTB model builds on the bottom-up control on fresh-bedrock topography hypothesis of Rempe and Dietrich (2014) and includes a mass movement and bedrock-valley morphology term to extent the usefulness and general applicability of the model. We reconcile the DTB model with field observations using Bayesian analysis with the DREAM algorithm. We investigate explicitly the benefits of using spatially distributed parameter values to account implicitly, and in a relatively simple way, for rock mass heterogeneities that are very difficult, if not impossible, to characterize adequately in the field. We illustrate our method using an artificial data set of bedrock depth observations and then evaluate our DTB model with real-world data collected at the Papagaio river basin in Rio de Janeiro, Brazil. Our results demonstrate that the DTB model predicts accurately the observed bedrock depth data. The posterior mean DTB simulation is shown to be in good agreement with the measured data. The posterior prediction uncertainty of the DTB model can be propagated forward through hydromechanical models to derive probabilistic estimates of factors of safety.

Estimating the Uncertain Mathematical Structure of Hydrological Model via Bayesian Data Assimilation

NASA Astrophysics Data System (ADS)

Bulygina, N.; Gupta, H.; O'Donell, G.; Wheater, H.

2008-12-01

The structure of hydrological model at macro scale (e.g. watershed) is inherently uncertain due to many factors, including the lack of a robust hydrological theory at the macro scale. In this work, we assume that a suitable conceptual model for the hydrologic system has already been determined - i.e., the system boundaries have been specified, the important state variables and input and output fluxes to be included have been selected, and the major hydrological processes and geometries of their interconnections have been identified. The structural identification problem then is to specify the mathematical form of the relationships between the inputs, state variables and outputs, so that a computational model can be constructed for making simulations and/or predictions of system input-state-output behaviour. We show how Bayesian data assimilation can be used to merge both prior beliefs in the form of pre-assumed model equations with information derived from the data to construct a posterior model. The approach, entitled Bayesian Estimation of Structure (BESt), is used to estimate a hydrological model for a small basin in England, at hourly time scales, conditioned on the assumption of 3-dimensional state - soil moisture storage, fast and slow flow stores - conceptual model structure. Inputs to the system are precipitation and potential evapotranspiration, and outputs are actual evapotranspiration and streamflow discharge. Results show the difference between prior and posterior mathematical structures, as well as provide prediction confidence intervals that reflect three types of uncertainty: due to initial conditions, due to input and due to mathematical structure.

Seasonal Influenza Vaccine Acceptance among Pregnant Women in Zhejiang Province, China: Evidence Based on Health Belief Model.

PubMed

Hu, Yu; Wang, Ying; Liang, Hui; Chen, Yaping

2017-12-11

Background: Reasons for acceptance of seasonal influenza vaccine (SIV) vaccination among pregnant women in China are poorly understood. We assessed the intention to accept SIV among pregnant women in Zhejiang province, by using a self-administrated structured questionnaire developed on the basis of health belief model (HBM). Methods: From 1 January to 31 March 2014, pregnant women with ≥12 gestational weeks who attended antenatal clinics (ANCs) at public hospitals in 6 out of 90 districts were surveyed using a self-administered questionnaire that covered knowledge, attitudes, and beliefs related to SIV vaccination and influenza infection. We examined the associations between the acceptance of SIV vaccination and the demographic factors and HBM constructs using the logistic regression model, calculating the adjusted odds ratio (AOR). Results: Of the 1252 participants, 76.28% were willing to receive the SIV vaccination during their current pregnancy. High levels of perceived susceptibility of influenza (AOR = 1.75 (95%CI: 1.36-2.08)), high levels of perceived severity of influenza (AOR = 1.62 (95%CI: 1.25-1.95)), high level of perceived benefits of vaccination (AOR = 1.97 (95%CI: 1.76-2.21)), and high levels of cues to action were positively associated with the acceptance of SIV vaccination among pregnant women (AOR = 2.03 (95%CI: 1.70-2.69)), while high level of perceived barriers of vaccination was a negative determinant (AOR = 0.76 (95%CI: 0.62-0.94)). Conclusions: Poor knowledge and negative attitude towards SIV were associated with the poor acceptance of SIV. Health providers' recommendations were important to pregnant women's acceptance of SIV. Health education and direct communication strategies on SIV vaccination and influenza infection are necessary to improve the acceptance of SIV vaccination among pregnant women.

Targeting the LRP5 pathway improves bone properties in a mouse model of Osteogenesis Imperfecta

PubMed Central

Jacobsen, Christina M.; Barber, Lauren A.; Ayturk, Ugur M.; Roberts, Heather J.; Deal, Lauren E.; Schwartz, Marissa A.; Weis, MaryAnn; Eyre, David; Zurakowski, David; Robling, Alexander G.; Warman, Matthew L.

2014-01-01

The cell surface receptor low-density lipoprotein receptor-related protein 5 (LRP5) is a key regulator of bone mass and bone strength. Heterozygous missense mutations in LRP5 cause autosomal dominant high bone mass (HBM) in humans by reducing binding to LRP5 by endogenous inhibitors, such as sclerostin (SOST). Mice heterozygous for a knockin allele (Lrp5p.A214V) that is orthologous to a human HBM-causing mutation have increased bone mass and strength. Osteogenesis Imperfecta (OI) is a skeletal fragility disorder predominantly caused by mutations that affect type I collagen. We tested whether the LRP5 pathway can be used to improve bone properties in animal models of OI. First, we mated Lrp5+/p.A214V mice to Col1a2+/p.G610C mice, which model human type IV OI. We found that Col1a2+/p.G610C;Lrp5+/p.A214V offspring had significantly increased bone mass and strength compared to Col1a2+/p.G610C;Lrp5+/+ littermates. The improved bone properties were not due to altered mRNA expression of type I collagen or its chaperones, nor were they due to changes in mutant type I collagen secretion. Second, we treated Col1a2+/p.G610C mice with a monoclonal antibody that inhibits sclerostin activity (Scl-Ab). We found that antibody treated mice had significantly increased bone mass and strength compared to vehicle treated littermates. These findings indicate increasing bone formation, even without altering bone collagen composition, may benefit patients with OI. PMID:24677211

Targeting the LRP5 pathway improves bone properties in a mouse model of osteogenesis imperfecta.

PubMed

Jacobsen, Christina M; Barber, Lauren A; Ayturk, Ugur M; Roberts, Heather J; Deal, Lauren E; Schwartz, Marissa A; Weis, MaryAnn; Eyre, David; Zurakowski, David; Robling, Alexander G; Warman, Matthew L

2014-10-01

The cell surface receptor low-density lipoprotein receptor-related protein 5 (LRP5) is a key regulator of bone mass and bone strength. Heterozygous missense mutations in LRP5 cause autosomal dominant high bone mass (HBM) in humans by reducing binding to LRP5 by endogenous inhibitors, such as sclerostin (SOST). Mice heterozygous for a knockin allele (Lrp5(p.A214V) ) that is orthologous to a human HBM-causing mutation have increased bone mass and strength. Osteogenesis imperfecta (OI) is a skeletal fragility disorder predominantly caused by mutations that affect type I collagen. We tested whether the LRP5 pathway can be used to improve bone properties in animal models of OI. First, we mated Lrp5(+/p.A214V) mice to Col1a2(+/p.G610C) mice, which model human type IV OI. We found that Col1a2(+/p.G610C) ;Lrp5(+/p.A214V) offspring had significantly increased bone mass and strength compared to Col1a2(+/p.G610C) ;Lrp5(+/+) littermates. The improved bone properties were not a result of altered mRNA expression of type I collagen or its chaperones, nor were they due to changes in mutant type I collagen secretion. Second, we treated Col1a2(+/p.G610C) mice with a monoclonal antibody that inhibits sclerostin activity (Scl-Ab). We found that antibody-treated mice had significantly increased bone mass and strength compared to vehicle-treated littermates. These findings indicate increasing bone formation, even without altering bone collagen composition, may benefit patients with OI. © 2014 American Society for Bone and Mineral Research.

A Bayesian rupture model of the 2007 Mw 8.1 Solomon Islands earthquake in Southwest Pacific with coral reef displacement measurements

NASA Astrophysics Data System (ADS)

Chen, Ting; Luo, Haipeng; Furlong, Kevin P.

2017-05-01

On 1st April 2007 a Mw 8.1 megathrust earthquake occurred in the western Solomon Islands of the Southwest Pacific and generated a regional tsunami with run-up heights of up to 12 m. A Bayesian inversion model is constructed to derive fault dip angle and cumulative co-seismic and early post-seismic slip using coral reef displacement measurements, in which both data misfit and moment magnitude are used as constraints. Results show three shallow, high-slip patches concentrated along the trench from west of Ranongga Island to Rendova Island on a fault plane dipping 20°, and a maximum dip slip of 11.6 m beneath Ranongga Island. Considerable subsidence on Simbo Island outboard of the trench on the subducting plate is not well explained with this model, but may be related to the effects of afterslip and/or Simbo Island's location near the triple junction among the Australia, Woodlark and Pacific plates.

Bayesian inversion of refraction seismic traveltime data

NASA Astrophysics Data System (ADS)

Ryberg, T.; Haberland, Ch

2018-03-01

We apply a Bayesian Markov chain Monte Carlo (McMC) formalism to the inversion of refraction seismic, traveltime data sets to derive 2-D velocity models below linear arrays (i.e. profiles) of sources and seismic receivers. Typical refraction data sets, especially when using the far-offset observations, are known as having experimental geometries which are very poor, highly ill-posed and far from being ideal. As a consequence, the structural resolution quickly degrades with depth. Conventional inversion techniques, based on regularization, potentially suffer from the choice of appropriate inversion parameters (i.e. number and distribution of cells, starting velocity models, damping and smoothing constraints, data noise level, etc.) and only local model space exploration. McMC techniques are used for exhaustive sampling of the model space without the need of prior knowledge (or assumptions) of inversion parameters, resulting in a large number of models fitting the observations. Statistical analysis of these models allows to derive an average (reference) solution and its standard deviation, thus providing uncertainty estimates of the inversion result. The highly non-linear character of the inversion problem, mainly caused by the experiment geometry, does not allow to derive a reference solution and error map by a simply averaging procedure. We present a modified averaging technique, which excludes parts of the prior distribution in the posterior values due to poor ray coverage, thus providing reliable estimates of inversion model properties even in those parts of the models. The model is discretized by a set of Voronoi polygons (with constant slowness cells) or a triangulated mesh (with interpolation within the triangles). Forward traveltime calculations are performed by a fast, finite-difference-based eikonal solver. The method is applied to a data set from a refraction seismic survey from Northern Namibia and compared to conventional tomography. An inversion test for a synthetic data set from a known model is also presented.

Detection of BRAF V600 Mutations in Melanoma: Evaluation of Concordance between the Cobas® 4800 BRAF V600 Mutation Test and the Methods Used in French National Cancer Institute (INCa) Platforms in a Real-Life Setting

PubMed Central

Mourah, Samia; Denis, Marc G.; Narducci, Fabienne Escande; Solassol, Jérôme; Merlin, Jean-Louis; Sabourin, Jean-Christophe; Scoazec, Jean-Yves; Ouafik, L’Houcine; Emile, Jean-François; Heller, Remy; Souvignet, Claude; Bergougnoux, Loïc; Merlio, Jean-Philippe

2015-01-01

Vemurafenib is approved for the treatment of metastatic melanoma in patients with BRAF V600 mutation. In pivotal clinical trials, BRAF testing has always been done with the approved cobas 4800 BRAF test. In routine practice, several methods are available and are used according to the laboratories usual procedures. A national, multicenter, non-interventional study was conducted with prospective and consecutive collection of tumor samples. A parallel evaluation was performed in routine practice between the cobas 4800 BRAF V600 mutation test and home brew methods (HBMs) of 12 national laboratories, labelled and funded by the French National Cancer Institute (INCa). For 420 melanoma samples tested, the cobas method versus HBM showed a high concordance (93.3%; kappa = 0.86) in BRAF V600 genotyping with similar mutation rates (34.0% versus 35.7%, respectively). Overall, 97.4% and 98.6% of samples gave valid results using the cobas and HBM, respectively. Of the 185 samples strictly fulfilling the cobas guidelines, the concordance rate was even higher (95.7%; kappa = 0.91; 95%CI [0.85; 0.97]). Out of the 420 samples tested, 28 (6.7%) showed discordance between HBM and cobas. This prospective study shows a high concordance rate between the cobas 4800 BRAF V600 test and home brew methods in the routine detection of BRAF V600E mutations. PMID:25789737

Quantum mechanics: The Bayesian theory generalized to the space of Hermitian matrices

NASA Astrophysics Data System (ADS)

Benavoli, Alessio; Facchini, Alessandro; Zaffalon, Marco

2016-10-01

We consider the problem of gambling on a quantum experiment and enforce rational behavior by a few rules. These rules yield, in the classical case, the Bayesian theory of probability via duality theorems. In our quantum setting, they yield the Bayesian theory generalized to the space of Hermitian matrices. This very theory is quantum mechanics: in fact, we derive all its four postulates from the generalized Bayesian theory. This implies that quantum mechanics is self-consistent. It also leads us to reinterpret the main operations in quantum mechanics as probability rules: Bayes' rule (measurement), marginalization (partial tracing), independence (tensor product). To say it with a slogan, we obtain that quantum mechanics is the Bayesian theory in the complex numbers.

Bayesian multimodel inference for dose-response studies

USGS Publications Warehouse

Link, W.A.; Albers, P.H.

2007-01-01

Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

Agricultural legacy, climate, and soil influence the restoration and carbon potential of woody regrowth in Australia.

PubMed

Dwyer, John M; Fensham, Rod J; Buckley, Yvonne M

2010-10-01

Opportunities for dual restoration and carbon benefits from naturally regenerating woody ecosystems in agricultural landscapes have been highlighted recently. The restoration capacity of woody ecosystems depends on the magnitude and duration of ecosystem modification, i.e., the "agricultural legacy." However, this legacy may not influence carbon sequestration in the same way as restoration because carbon potential depends primarily on biomass accumulation, with little consideration of other attributes and functions of the ecosystem. Our present study simultaneously assesses the restoration and carbon potential of Acacia harpophylla regrowth, an extensive regrowth ecosystem in northeastern Australia. We used a landscape-scale survey of A. harpophylla regrowth to test the following hypotheses: (1) management history, in combination with climatic and edaphic factors, has long-term effects on stem densities, and (2) higher-density stands have lower restoration and carbon potential, which is also influenced by climatic and edaphic factors. We focused on the restoration of forest structure, which was characterized using stem density, aboveground biomass, stem heights, and stem diameters. Data were analyzed using multilevel models within the hierarchical Bayesian model (HBM) framework. We found strong support for both hypotheses. Repeated attempts at clearing Brigalow (A. harpophylla ecosystem) regrowth increases stem densities, and these densities remain high over the long term, particularly in high-rainfall areas and on gilgaied, high-clay soils (hypothesis 1). In models testing hypothesis 2, interactions between stem density and stand age indicate that higher-density stands have slower biomass accumulation and structural development in the long term. After accounting for stem density and stand age, annual rainfall had a positive effect on biomass accumulation and structural development. Other climate and soil variables were retained in the various models but had weaker effects. Spatial extrapolations of the HBMs indicated that the central and eastern parts of the study region are most suitable for biomass accumulation; however, these may not correspond to the areas that historically supported the highest biomass Brigalow forests. We conclude that carbon and restoration goals are largely congruent within areas of similar climate. At the regional scale, however, spatial prioritization of restoration and carbon projects may only be aligned where carbon benefits will be high.

A guide to Bayesian model selection for ecologists

USGS Publications Warehouse

Hooten, Mevin B.; Hobbs, N.T.

2015-01-01

The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.

Interpretable Topic Features for Post-ICU Mortality Prediction.

PubMed

Luo, Yen-Fu; Rumshisky, Anna

2016-01-01

Electronic health records provide valuable resources for understanding the correlation between various diseases and mortality. The analysis of post-discharge mortality is critical for healthcare professionals to follow up potential causes of death after a patient is discharged from the hospital and give prompt treatment. Moreover, it may reduce the cost derived from readmissions and improve the quality of healthcare. Our work focused on post-discharge ICU mortality prediction. In addition to features derived from physiological measurements, we incorporated ICD-9-CM hierarchy into Bayesian topic model learning and extracted topic features from medical notes. We achieved highest AUCs of 0.835 and 0.829 for 30-day and 6-month post-discharge mortality prediction using baseline and topic proportions derived from Labeled-LDA. Moreover, our work emphasized the interpretability of topic features derived from topic model which may facilitates the understanding and investigation of the complexity between mortality and diseases.

On the Adequacy of Bayesian Evaluations of Categorization Models: Reply to Vanpaemel and Lee (2012)

ERIC Educational Resources Information Center

Wills, Andy J.; Pothos, Emmanuel M.

2012-01-01

Vanpaemel and Lee (2012) argued, and we agree, that the comparison of formal models can be facilitated by Bayesian methods. However, Bayesian methods neither precede nor supplant our proposals (Wills & Pothos, 2012), as Bayesian methods can be applied both to our proposals and to their polar opposites. Furthermore, the use of Bayesian methods to…

Bayesian Regression of Thermodynamic Models of Redox Active Materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnston, Katherine

Finding a suitable functional redox material is a critical challenge to achieving scalable, economically viable technologies for storing concentrated solar energy in the form of a defected oxide. Demonstrating e ectiveness for thermal storage or solar fuel is largely accomplished by using a thermodynamic model derived from experimental data. The purpose of this project is to test the accuracy of our regression model on representative data sets. Determining the accuracy of the model includes parameter tting the model to the data, comparing the model using di erent numbers of param- eters, and analyzing the entropy and enthalpy calculated from themore » model. Three data sets were considered in this project: two demonstrating materials for solar fuels by wa- ter splitting and the other of a material for thermal storage. Using Bayesian Inference and Markov Chain Monte Carlo (MCMC), parameter estimation was preformed on the three data sets. Good results were achieved, except some there was some deviations on the edges of the data input ranges. The evidence values were then calculated in a variety of ways and used to compare models with di erent number of parameters. It was believed that at least one of the parameters was unnecessary and comparing evidence values demonstrated that the parameter was need on one data set and not signi cantly helpful on another. The entropy was calculated by taking the derivative in one variable and integrating over another. and its uncertainty was also calculated by evaluating the entropy over multiple MCMC samples. Afterwards, all the parts were written up as a tutorial for the Uncertainty Quanti cation Toolkit (UQTk).« less

Fast Bayesian approach for modal identification using free vibration data, Part I - Most probable value

NASA Astrophysics Data System (ADS)

Zhang, Feng-Liang; Ni, Yan-Chun; Au, Siu-Kui; Lam, Heung-Fai

2016-03-01

The identification of modal properties from field testing of civil engineering structures is becoming economically viable, thanks to the advent of modern sensor and data acquisition technology. Its demand is driven by innovative structural designs and increased performance requirements of dynamic-prone structures that call for a close cross-checking or monitoring of their dynamic properties and responses. Existing instrumentation capabilities and modal identification techniques allow structures to be tested under free vibration, forced vibration (known input) or ambient vibration (unknown broadband loading). These tests can be considered complementary rather than competing as they are based on different modeling assumptions in the identification model and have different implications on costs and benefits. Uncertainty arises naturally in the dynamic testing of structures due to measurement noise, sensor alignment error, modeling error, etc. This is especially relevant in field vibration tests because the test condition in the field environment can hardly be controlled. In this work, a Bayesian statistical approach is developed for modal identification using the free vibration response of structures. A frequency domain formulation is proposed that makes statistical inference based on the Fast Fourier Transform (FFT) of the data in a selected frequency band. This significantly simplifies the identification model because only the modes dominating the frequency band need to be included. It also legitimately ignores the information in the excluded frequency bands that are either irrelevant or difficult to model, thereby significantly reducing modeling error risk. The posterior probability density function (PDF) of the modal parameters is derived rigorously from modeling assumptions and Bayesian probability logic. Computational difficulties associated with calculating the posterior statistics, including the most probable value (MPV) and the posterior covariance matrix, are addressed. Fast computational algorithms for determining the MPV are proposed so that the method can be practically implemented. In the companion paper (Part II), analytical formulae are derived for the posterior covariance matrix so that it can be evaluated without resorting to finite difference method. The proposed method is verified using synthetic data. It is also applied to modal identification of full-scale field structures.

Bayesian image reconstruction for improving detection performance of muon tomography.

PubMed

Wang, Guobao; Schultz, Larry J; Qi, Jinyi

2009-05-01

Muon tomography is a novel technology that is being developed for detecting high-Z materials in vehicles or cargo containers. Maximum likelihood methods have been developed for reconstructing the scattering density image from muon measurements. However, the instability of maximum likelihood estimation often results in noisy images and low detectability of high-Z targets. In this paper, we propose using regularization to improve the image quality of muon tomography. We formulate the muon reconstruction problem in a Bayesian framework by introducing a prior distribution on scattering density images. An iterative shrinkage algorithm is derived to maximize the log posterior distribution. At each iteration, the algorithm obtains the maximum a posteriori update by shrinking an unregularized maximum likelihood update. Inverse quadratic shrinkage functions are derived for generalized Laplacian priors and inverse cubic shrinkage functions are derived for generalized Gaussian priors. Receiver operating characteristic studies using simulated data demonstrate that the Bayesian reconstruction can greatly improve the detection performance of muon tomography.

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

Bayesian selection of misspecified models is overconfident and may cause spurious posterior probabilities for phylogenetic trees.

PubMed

Yang, Ziheng; Zhu, Tianqi

2018-02-20

The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this overconfidence are unknown. In general, the performance of Bayesian selection of misspecified models is poorly understood, even though this is of great scientific interest since models are never true in real data analysis. Here we characterize the asymptotic behavior of Bayesian model selection and show that when the competing models are equally wrong, Bayesian model selection exhibits surprising and polarized behaviors in large datasets, supporting one model with full force while rejecting the others. If one model is slightly less wrong than the other, the less wrong model will eventually win when the amount of data increases, but the method may become overconfident before it becomes reliable. We suggest that this extreme behavior may be a major factor for the spuriously high posterior probabilities for evolutionary trees. The philosophical implications of our results to the application of Bayesian model selection to evaluate opposing scientific hypotheses are yet to be explored, as are the behaviors of non-Bayesian methods in similar situations.

BONNSAI: correlated stellar observables in Bayesian methods

NASA Astrophysics Data System (ADS)

Schneider, F. R. N.; Castro, N.; Fossati, L.; Langer, N.; de Koter, A.

2017-02-01

In an era of large spectroscopic surveys of stars and big data, sophisticated statistical methods become more and more important in order to infer fundamental stellar parameters such as mass and age. Bayesian techniques are powerful methods because they can match all available observables simultaneously to stellar models while taking prior knowledge properly into account. However, in most cases it is assumed that observables are uncorrelated which is generally not the case. Here, we include correlations in the Bayesian code Bonnsai by incorporating the covariance matrix in the likelihood function. We derive a parametrisation of the covariance matrix that, in addition to classical uncertainties, only requires the specification of a correlation parameter that describes how observables co-vary. Our correlation parameter depends purely on the method with which observables have been determined and can be analytically derived in some cases. This approach therefore has the advantage that correlations can be accounted for even if information for them are not available in specific cases but are known in general. Because the new likelihood model is a better approximation of the data, the reliability and robustness of the inferred parameters are improved. We find that neglecting correlations biases the most likely values of inferred stellar parameters and affects the precision with which these parameters can be determined. The importance of these biases depends on the strength of the correlations and the uncertainties. For example, we apply our technique to massive OB stars, but emphasise that it is valid for any type of stars. For effective temperatures and surface gravities determined from atmosphere modelling, we find that masses can be underestimated on average by 0.5σ and mass uncertainties overestimated by a factor of about 2 when neglecting correlations. At the same time, the age precisions are underestimated over a wide range of stellar parameters. We conclude that accounting for correlations is essential in order to derive reliable stellar parameters including robust uncertainties and will be vital when entering an era of precision stellar astrophysics thanks to the Gaia satellite.

Population size and stopover duration estimation using mark–resight data and Bayesian analysis of a superpopulation model

USGS Publications Warehouse

Lyons, James E.; Kendall, William L.; Royle, J. Andrew; Converse, Sarah J.; Andres, Brad A.; Buchanan, Joseph B.

2016-01-01

We present a novel formulation of a mark–recapture–resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. Estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. We use a Bayesian analysis of a state–space formulation of the Jolly–Seber mark–recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. We also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state–space model. We conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. Simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. Our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. This provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). Application of the methods is demonstrated with data from a study of migratory sandpipers.

A pilot study: the development of a culturally tailored Malaysian Diabetes Education Module (MY-DEMO) based on the Health Belief Model.

PubMed

Ahmad, Badariah; Ramadas, Amutha; Kia Fatt, Quek; Md Zain, Anuar Zaini

2014-04-08

Diabetes education and self-care remains the cornerstone of diabetes management. There are many structured diabetes modules available in the United Kingdom, Europe and United States of America. Contrastingly, few structured and validated diabetes modules are available in Malaysia. This pilot study aims to develop and validate diabetes education material suitable and tailored for a multicultural society like Malaysia. The theoretical framework of this module was founded from the Health Belief Model (HBM). The participants were assessed using 6-item pre- and post-test questionnaires that measured some of the known HBM constructs namely cues to action, perceived severity and perceived benefit. Data was analysed using PASW Statistics 18.0. The pre- and post-test questionnaires were administered to 88 participants (31 males). In general, there was a significant increase in the total score in post-test (97.34 ± 6.13%) compared to pre-test (92.80 ± 12.83%) (p < 0.05) and a significant increase in excellent score (>85%) at post-test (84.1%) compared to pre-test (70.5%) (p < 0.05). There was an improvement in post-test score in 4 of 6 items tested. The remaining 2 items which measured the perceived severity and cues to action had poorer post-test score. The preliminary results from this pilot study suggest contextualised content material embedded within MY DEMO maybe suitable for integration with the existing diabetes education programmes. This was the first known validated diabetes education programme available in the Malay language.

How the Bayesians Got Their Beliefs (and What Those Beliefs Actually Are): Comment on Bowers and Davis (2012)

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Chater, Nick; Norris, Dennis; Pouget, Alexandre

2012-01-01

Bowers and Davis (2012) criticize Bayesian modelers for telling "just so" stories about cognition and neuroscience. Their criticisms are weakened by not giving an accurate characterization of the motivation behind Bayesian modeling or the ways in which Bayesian models are used and by not evaluating this theoretical framework against specific…

Modeling Diagnostic Assessments with Bayesian Networks

ERIC Educational Resources Information Center

Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego

2007-01-01

This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

Performance of VPIC on Trinity

NASA Astrophysics Data System (ADS)

Nystrom, W. D.; Bergen, B.; Bird, R. F.; Bowers, K. J.; Daughton, W. S.; Guo, F.; Li, H.; Nam, H. A.; Pang, X.; Rust, W. N., III; Wohlbier, J.; Yin, L.; Albright, B. J.

2016-10-01

Trinity is a new major DOE computing resource which is going through final acceptance testing at Los Alamos National Laboratory. Trinity has several new and unique architectural features including two compute partitions, one with dual socket Intel Haswell Xeon compute nodes and one with Intel Knights Landing (KNL) Xeon Phi compute nodes. Additional unique features include use of on package high bandwidth memory (HBM) for the KNL nodes, the ability to configure the KNL nodes with respect to HBM model and on die network topology in a variety of operational modes at run time, and use of solid state storage via burst buffer technology to reduce time required to perform I/O. An effort is in progress to port and optimize VPIC to Trinity and evaluate its performance. Because VPIC was recently released as Open Source, it is being used as part of acceptance testing for Trinity and is participating in the Trinity Open Science Program which has resulted in excellent collaboration activities with both Cray and Intel. Results of this work will be presented on performance of VPIC on both Haswell and KNL partitions for both single node runs and runs at scale. Work performed under the auspices of the U.S. Dept. of Energy by the Los Alamos National Security, LLC Los Alamos National Laboratory under contract DE-AC52-06NA25396 and supported by the LANL LDRD program.

Factors associated with perception of risk of contracting HIV among secondary school female learners in Mbonge subdivision of rural Cameroon

PubMed Central

Tarkang, Elvis Enowbeyang

2014-01-01

Introduction Since learners in secondary schools fall within the age group hardest hit by HIV/AIDS, it is obvious that these learners might be at high risk of contracting HIV/AIDS. However, little has been explored on the perception of risk of contracting HIV among secondary school learners in Cameroon. This study aimed at examining the perception of risk of contracting HIV among secondary school learners in Mbonge subdivision of rural Cameroon using the Health Belief Model (HBM) as framework. Methods A quantitative, correlational design was adopted, using a self-administered questionnaire to collect data from 210 female learners selected through disproportional, stratified, simple random sampling technique, from three participating senior secondary schools. Statistics were calculated using SPSS version 20 software program. Results Only 39.4% of the respondents perceived themselves to be at high risk of contracting HIV, though the majority, 54.0% were sexually active. Multinomial logistic regression analyses show that sexual risk behaviours (p=0.000) and the Integrated Value Mapping (IVM) of the perception components of the HBM are the most significant factors associated with perception of risk of contracting HIV at the level p<0.05. Conclusion The findings of this study can play an instrumental role in the development of effective preventive and interventional messages for adolescents in Cameroon. PMID:25309659

Preventing HIV transmission among Iranian prisoners: Initial support for providing education on the benefits of harm reduction practices

PubMed Central

Eshrati, Babak; Asl, Rahim Taghizadeh; Dell, Colleen Anne; Afshar, Parviz; Millson, Peggy Margaret E; Kamali, Mohammad; Weekes, John

2008-01-01

Background Harm reduction is a health-centred approach that seeks to reduce the health and social harms associated with high-risk behaviors, such as illicit drug use. The objective of this study is to determine the association between the beliefs of a group of adult, male prisoners in Iran about the transmission of HIV and their high-risk practices while in prison. Methods A cross-sectional study was conducted in 2004. The study population was a random selection of 100 men incarcerated at Rajaei-Shahr prison. The data were collected through a self-administered questionnaire. Focus group discussions were held at the prison to guide the design of the questionnaire. The relationship between components of the Health Belief Model (HBM) and prisoners' risky HIV-related behaviors was examined. Results Calculating Pearson's correlation coefficient, a significant, positive association was found between the benefit component of the HBM and prisoners not engaging in HIV high-risk behaviors. Conclusion Educational harm reduction initiatives that promote the effectiveness of strategies designed to reduce the risk of HIV transmission may decrease prisoners' high-risk behaviors. This finding provides initial support for the Iran prison system's current offering of HIV/AIDS harm reduction programming and suggests the need to offer increased education about the effectiveness of HIV prevention practices. PMID:18541032

Health Beliefs Describing Patients Enrolling in Community Pharmacy Disease Management Programs.

PubMed

Luder, Heidi; Frede, Stacey; Kirby, James; King, Keith; Heaton, Pamela

2016-08-01

The purpose of this study was to survey new enrollees in a community pharmacy, employer-based diabetes and hypertension coaching program to describe the characteristics, health beliefs, and cues to action of newly enrolled participants. A 70-question, 5-point Likert-type survey was developed using constructs from the Health Belief Model (HBM), Theory of Planned Behavior (TPB), and Theory of Reasoned Action (TRA). New enrollees in the coaching programs completed the survey. Survey responses between controlled and uncontrolled patients and patient demographics were compared. Between November 2011 and November 2012, 154 patients completed the survey. Patients were fairly well controlled with a mean hemoglobin A1C of 7.3% and a mean blood pressure of 134/82 mm Hg. The strongest cue to action for enrollment was the financial incentives offered by the employer (mean: 3.33, median: 4). White patients were significantly more motivated by financial incentives. More patients indicated they had not enrolled previously in the program because they were unaware it was available (mean: 2.89, median 3.0) and these patients were more likely to have an uncontrolled condition (P ≤ 0.050). A top factor motivating patients to enroll in a disease management coaching program was the receipt of financial incentives. Significant differences in HBM, TPB, and TRA responses were seen for patients with different demographics. © The Author(s) 2015.

Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

NASA Astrophysics Data System (ADS)

Wheeler, David C.; Waller, Lance A.

2009-03-01

In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.

Philosophy and the practice of Bayesian statistics

PubMed Central

Gelman, Andrew; Shalizi, Cosma Rohilla

2015-01-01

A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. PMID:22364575

Philosophy and the practice of Bayesian statistics.

PubMed

Gelman, Andrew; Shalizi, Cosma Rohilla

2013-02-01

A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. © 2012 The British Psychological Society.

Bayesian Analysis of Hot Jupiter Radius Anomalies Points to Ohmic Dissipation

NASA Astrophysics Data System (ADS)

Thorngren, Daniel; Fortney, Jonathan

2018-01-01

The cause of the unexpectedly large radii of hot Jupiters has been the subject of many hypotheses over the past 15 years and is one of the long-standing open issues in exoplanetary physics. In our work, we seek to examine the population of 300 hot Jupiters to identify a model that best explains their radii. Using a hierarchical Bayesian framework, we match structure evolution models to the observed giant planets’ masses, radii, and ages, with a prior for bulk composition based on the mass from Thorngren et al. (2016). We consider various models for the relationship between heating efficiency (the fraction of flux absorbed into the interior) and incident flux. For the first time, we are able to derive this heating efficiency as a function of planetary T_eq. Models in which the heating efficiency decreases at the higher temperatures (above ~1600 K) are strongly and statistically significantly preferred. Of the published models for the radius anomaly, only the Ohmic dissipation model predicts this feature, which it explains as being the result of magnetic drag reducing atmospheric wind speeds. We interpret our results as evidence in favor of the Ohmic dissipation model.

Estimating the Term Structure With a Semiparametric Bayesian Hierarchical Model: An Application to Corporate Bonds.

PubMed

Cruz-Marcelo, Alejandro; Ensor, Katherine B; Rosner, Gary L

2011-06-01

The term structure of interest rates is used to price defaultable bonds and credit derivatives, as well as to infer the quality of bonds for risk management purposes. We introduce a model that jointly estimates term structures by means of a Bayesian hierarchical model with a prior probability model based on Dirichlet process mixtures. The modeling methodology borrows strength across term structures for purposes of estimation. The main advantage of our framework is its ability to produce reliable estimators at the company level even when there are only a few bonds per company. After describing the proposed model, we discuss an empirical application in which the term structure of 197 individual companies is estimated. The sample of 197 consists of 143 companies with only one or two bonds. In-sample and out-of-sample tests are used to quantify the improvement in accuracy that results from approximating the term structure of corporate bonds with estimators by company rather than by credit rating, the latter being a popular choice in the financial literature. A complete description of a Markov chain Monte Carlo (MCMC) scheme for the proposed model is available as Supplementary Material.

Estimating the Term Structure With a Semiparametric Bayesian Hierarchical Model: An Application to Corporate Bonds1

PubMed Central

Cruz-Marcelo, Alejandro; Ensor, Katherine B.; Rosner, Gary L.

2011-01-01

The term structure of interest rates is used to price defaultable bonds and credit derivatives, as well as to infer the quality of bonds for risk management purposes. We introduce a model that jointly estimates term structures by means of a Bayesian hierarchical model with a prior probability model based on Dirichlet process mixtures. The modeling methodology borrows strength across term structures for purposes of estimation. The main advantage of our framework is its ability to produce reliable estimators at the company level even when there are only a few bonds per company. After describing the proposed model, we discuss an empirical application in which the term structure of 197 individual companies is estimated. The sample of 197 consists of 143 companies with only one or two bonds. In-sample and out-of-sample tests are used to quantify the improvement in accuracy that results from approximating the term structure of corporate bonds with estimators by company rather than by credit rating, the latter being a popular choice in the financial literature. A complete description of a Markov chain Monte Carlo (MCMC) scheme for the proposed model is available as Supplementary Material. PMID:21765566

Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science.

PubMed

Cavagnaro, Daniel R; Myung, Jay I; Pitt, Mark A; Kujala, Janne V

2010-04-01

Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experimental designs under which one can infer the underlying model in the fewest possible steps. When the models under consideration are nonlinear, as is often the case in cognitive science, this problem can be impossible to solve analytically without simplifying assumptions. However, as we show in this letter, a full solution can be found numerically with the help of a Bayesian computational trick derived from the statistics literature, which recasts the problem as a probability density simulation in which the optimal design is the mode of the density. We use a utility function based on mutual information and give three intuitive interpretations of the utility function in terms of Bayesian posterior estimates. As a proof of concept, we offer a simple example application to an experiment on memory retention.

Taking error into account when fitting models using Approximate Bayesian Computation.

PubMed

van der Vaart, Elske; Prangle, Dennis; Sibly, Richard M

2018-03-01

Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the coverage test with which accuracy is assessed. We apply this method, which we call error-calibrated ABC, to a toy example and a realistic 14-parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic coverage test show that our approach improves estimation of parameter values and their credible intervals for both models. © 2017 by the Ecological Society of America.

Bayesian inference based on dual generalized order statistics from the exponentiated Weibull model

NASA Astrophysics Data System (ADS)

Al Sobhi, Mashail M.

2015-02-01

Bayesian estimation for the two parameters and the reliability function of the exponentiated Weibull model are obtained based on dual generalized order statistics (DGOS). Also, Bayesian prediction bounds for future DGOS from exponentiated Weibull model are obtained. The symmetric and asymmetric loss functions are considered for Bayesian computations. The Markov chain Monte Carlo (MCMC) methods are used for computing the Bayes estimates and prediction bounds. The results have been specialized to the lower record values. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.

Uncertainty estimation of Intensity-Duration-Frequency relationships: A regional analysis

NASA Astrophysics Data System (ADS)

Mélèse, Victor; Blanchet, Juliette; Molinié, Gilles

2018-03-01

We propose in this article a regional study of uncertainties in IDF curves derived from point-rainfall maxima. We develop two generalized extreme value models based on the simple scaling assumption, first in the frequentist framework and second in the Bayesian framework. Within the frequentist framework, uncertainties are obtained i) from the Gaussian density stemming from the asymptotic normality theorem of the maximum likelihood and ii) with a bootstrap procedure. Within the Bayesian framework, uncertainties are obtained from the posterior densities. We confront these two frameworks on the same database covering a large region of 100, 000 km2 in southern France with contrasted rainfall regime, in order to be able to draw conclusion that are not specific to the data. The two frameworks are applied to 405 hourly stations with data back to the 1980's, accumulated in the range 3 h-120 h. We show that i) the Bayesian framework is more robust than the frequentist one to the starting point of the estimation procedure, ii) the posterior and the bootstrap densities are able to better adjust uncertainty estimation to the data than the Gaussian density, and iii) the bootstrap density give unreasonable confidence intervals, in particular for return levels associated to large return period. Therefore our recommendation goes towards the use of the Bayesian framework to compute uncertainty.

An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code, with Application to WASP-12b

NASA Astrophysics Data System (ADS)

Harrington, Joseph; Blecic, Jasmina; Cubillos, Patricio; Rojo, Patricio; Loredo, Thomas J.; Bowman, M. Oliver; Foster, Andrew S. D.; Stemm, Madison M.; Lust, Nate B.

2015-01-01

Atmospheric retrievals for solar-system planets typically fit, either with a minimizer or by eye, a synthetic spectrum to high-resolution (Δλ/λ ~ 1000-100,000) data with S/N > 100 per point. In contrast, exoplanet data often have S/N ~ 10 per point, and may have just a few points representing bandpasses larger than 1 um. To derive atmospheric constraints and robust parameter uncertainty estimates from such data requires a Bayesian approach. To date there are few investigators with the relevant codes, none of which are publicly available. We are therefore pleased to announce the open-source Bayesian Atmospheric Radiative Transfer (BART) code. BART uses a Bayesian phase-space explorer to drive a radiative-transfer model through the parameter phase space, producing the most robust estimates available for the thermal profile and chemical abundances in the atmosphere. We present an overview of the code and an initial application to Spitzer eclipse data for WASP-12b. We invite the community to use and improve BART via the open-source development site GitHub.com. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G. JB holds a NASA Earth and Space Science Fellowship.

An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code, and Application to WASP-12b

NASA Astrophysics Data System (ADS)

Harrington, Joseph; Blecic, Jasmina; Cubillos, Patricio; Rojo, Patricio M.; Loredo, Thomas J.; Bowman, Matthew O.; Foster, Andrew S.; Stemm, Madison M.; Lust, Nate B.

2014-11-01

Atmospheric retrievals for solar-system planets typically fit, either with a minimizer or by eye, a synthetic spectrum to high-resolution (Δλ/λ ~ 1000-100,000) data with S/N > 100 per point. In contrast, exoplanet data often have S/N ~ 10 per point, and may have just a few points representing bandpasses larger than 1 um. To derive atmospheric constraints and robust parameter uncertainty estimates from such data requires a Bayesian approach. To date there are few investigators with the relevant codes, none of which are publicly available. We are therefore pleased to announce the open-source Bayesian Atmospheric Radiative Transfer (BART) code. BART uses a Bayesian phase-space explorer to drive a radiative-transfer model through the parameter phase space, producing the most robust estimates available for the thermal profile and chemical abundances in the atmosphere. We present an overview of the code and an initial application to Spitzer eclipse data for WASP-12b. We invite the community to use and improve BART via the open-source development site GitHub.com. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G. JB holds a NASA Earth and Space Science Fellowship.

Spatially constrained Bayesian inversion of frequency- and time-domain electromagnetic data from the Tellus projects

NASA Astrophysics Data System (ADS)

Kiyan, Duygu; Rath, Volker; Delhaye, Robert

2017-04-01

The frequency- and time-domain airborne electromagnetic (AEM) data collected under the Tellus projects of the Geological Survey of Ireland (GSI) which represent a wealth of information on the multi-dimensional electrical structure of Ireland's near-surface. Our project, which was funded by GSI under the framework of their Short Call Research Programme, aims to develop and implement inverse techniques based on various Bayesian methods for these densely sampled data. We have developed a highly flexible toolbox using Python language for the one-dimensional inversion of AEM data along the flight lines. The computational core is based on an adapted frequency- and time-domain forward modelling core derived from the well-tested open-source code AirBeo, which was developed by the CSIRO (Australia) and the AMIRA consortium. Three different inversion methods have been implemented: (i) Tikhonov-type inversion including optimal regularisation methods (Aster el al., 2012; Zhdanov, 2015), (ii) Bayesian MAP inversion in parameter and data space (e.g. Tarantola, 2005), and (iii) Full Bayesian inversion with Markov Chain Monte Carlo (Sambridge and Mosegaard, 2002; Mosegaard and Sambridge, 2002), all including different forms of spatial constraints. The methods have been tested on synthetic and field data. This contribution will introduce the toolbox and present case studies on the AEM data from the Tellus projects.

Nonparametric Bayesian inference of the microcanonical stochastic block model

NASA Astrophysics Data System (ADS)

Peixoto, Tiago P.

2017-01-01

A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.

Fundamentals and Recent Developments in Approximate Bayesian Computation

PubMed Central

Lintusaari, Jarno; Gutmann, Michael U.; Dutta, Ritabrata; Kaski, Samuel; Corander, Jukka

2017-01-01

Abstract Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments. [ABC; approximate Bayesian computation; Bayesian inference; likelihood-free inference; phylogenetics; simulator-based models; stochastic simulation models; tree-based models.] PMID:28175922

A regularization approach to continuous learning with an application to financial derivatives pricing.

PubMed

Ormoneit, D

1999-12-01

We consider the training of neural networks in cases where the nonlinear relationship of interest gradually changes over time. One possibility to deal with this problem is by regularization where a variation penalty is added to the usual mean squared error criterion. To learn the regularized network weights we suggest the Iterative Extended Kalman Filter (IEKF) as a learning rule, which may be derived from a Bayesian perspective on the regularization problem. A primary application of our algorithm is in financial derivatives pricing, where neural networks may be used to model the dependency of the derivatives' price on one or several underlying assets. After giving a brief introduction to the problem of derivatives pricing we present experiments with German stock index options data showing that a regularized neural network trained with the IEKF outperforms several benchmark models and alternative learning procedures. In particular, the performance may be greatly improved using a newly designed neural network architecture that accounts for no-arbitrage pricing restrictions.

Multi-objective calibration and uncertainty analysis of hydrologic models; A comparative study between formal and informal methods

NASA Astrophysics Data System (ADS)

Shafii, M.; Tolson, B.; Matott, L. S.

2012-04-01

Hydrologic modeling has benefited from significant developments over the past two decades. This has resulted in building of higher levels of complexity into hydrologic models, which eventually makes the model evaluation process (parameter estimation via calibration and uncertainty analysis) more challenging. In order to avoid unreasonable parameter estimates, many researchers have suggested implementation of multi-criteria calibration schemes. Furthermore, for predictive hydrologic models to be useful, proper consideration of uncertainty is essential. Consequently, recent research has emphasized comprehensive model assessment procedures in which multi-criteria parameter estimation is combined with statistically-based uncertainty analysis routines such as Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. Such a procedure relies on the use of formal likelihood functions based on statistical assumptions, and moreover, the Bayesian inference structured on MCMC samplers requires a considerably large number of simulations. Due to these issues, especially in complex non-linear hydrological models, a variety of alternative informal approaches have been proposed for uncertainty analysis in the multi-criteria context. This study aims at exploring a number of such informal uncertainty analysis techniques in multi-criteria calibration of hydrological models. The informal methods addressed in this study are (i) Pareto optimality which quantifies the parameter uncertainty using the Pareto solutions, (ii) DDS-AU which uses the weighted sum of objective functions to derive the prediction limits, and (iii) GLUE which describes the total uncertainty through identification of behavioral solutions. The main objective is to compare such methods with MCMC-based Bayesian inference with respect to factors such as computational burden, and predictive capacity, which are evaluated based on multiple comparative measures. The measures for comparison are calculated both for calibration and evaluation periods. The uncertainty analysis methodologies are applied to a simple 5-parameter rainfall-runoff model, called HYMOD.

Accounting for model error in Bayesian solutions to hydrogeophysical inverse problems using a local basis approach

NASA Astrophysics Data System (ADS)

Köpke, Corinna; Irving, James; Elsheikh, Ahmed H.

2018-06-01

Bayesian solutions to geophysical and hydrological inverse problems are dependent upon a forward model linking subsurface physical properties to measured data, which is typically assumed to be perfectly known in the inversion procedure. However, to make the stochastic solution of the inverse problem computationally tractable using methods such as Markov-chain-Monte-Carlo (MCMC), fast approximations of the forward model are commonly employed. This gives rise to model error, which has the potential to significantly bias posterior statistics if not properly accounted for. Here, we present a new methodology for dealing with the model error arising from the use of approximate forward solvers in Bayesian solutions to hydrogeophysical inverse problems. Our approach is geared towards the common case where this error cannot be (i) effectively characterized through some parametric statistical distribution; or (ii) estimated by interpolating between a small number of computed model-error realizations. To this end, we focus on identification and removal of the model-error component of the residual during MCMC using a projection-based approach, whereby the orthogonal basis employed for the projection is derived in each iteration from the K-nearest-neighboring entries in a model-error dictionary. The latter is constructed during the inversion and grows at a specified rate as the iterations proceed. We demonstrate the performance of our technique on the inversion of synthetic crosshole ground-penetrating radar travel-time data considering three different subsurface parameterizations of varying complexity. Synthetic data are generated using the eikonal equation, whereas a straight-ray forward model is assumed for their inversion. In each case, our developed approach enables us to remove posterior bias and obtain a more realistic characterization of uncertainty.

Light radiation pressure upon an optically orthotropic surface

NASA Astrophysics Data System (ADS)

Nerovny, Nikolay A.; Lapina, Irina E.; Grigorjev, Anton S.

2017-11-01

In this paper, we discuss the problem of determination of light radiation pressure force upon an anisotropic surface. The optical parameters of such a surface are considered to have major and minor axes, so the model is called an orthotropic model. We derive the equations for force components from emission, absorption, and reflection, utilizing a modified Maxwell's specular-diffuse model. The proposed model can be used to model a flat solar sail with wrinkles. By performing Bayesian analysis for example of a wrinkled surface, we show that there are cases in which an orthotropic model of the optical parameters of a surface may be more accurate than an isotropic model.

Bayesian QTL mapping using genome-wide SSR markers and segregating population derived from a cross of two commercial F1 hybrids of tomato.

PubMed

Ohyama, Akio; Shirasawa, Kenta; Matsunaga, Hiroshi; Negoro, Satomi; Miyatake, Koji; Yamaguchi, Hirotaka; Nunome, Tsukasa; Iwata, Hiroyoshi; Fukuoka, Hiroyuki; Hayashi, Takeshi

2017-08-01

Using newly developed euchromatin-derived genomic SSR markers and a flexible Bayesian mapping method, 13 significant agricultural QTLs were identified in a segregating population derived from a four-way cross of tomato. So far, many QTL mapping studies in tomato have been performed for progeny obtained from crosses between two genetically distant parents, e.g., domesticated tomatoes and wild relatives. However, QTL information of quantitative traits related to yield (e.g., flower or fruit number, and total or average weight of fruits) in such intercross populations would be of limited use for breeding commercial tomato cultivars because individuals in the populations have specific genetic backgrounds underlying extremely different phenotypes between the parents such as large fruit in domesticated tomatoes and small fruit in wild relatives, which may not be reflective of the genetic variation in tomato breeding populations. In this study, we constructed F 2 population derived from a cross between two commercial F 1 cultivars in tomato to extract QTL information practical for tomato breeding. This cross corresponded to a four-way cross, because the four parental lines of the two F 1 cultivars were considered to be the founders. We developed 2510 new expressed sequence tag (EST)-based (euchromatin-derived) genomic SSR markers and selected 262 markers from these new SSR markers and publicly available SSR markers to construct a linkage map. QTL analysis for ten agricultural traits of tomato was performed based on the phenotypes and marker genotypes of F 2 plants using a flexible Bayesian method. As results, 13 QTL regions were detected for six traits by the Bayesian method developed in this study.

A Bayesian inverse modeling approach to estimate soil hydraulic properties of a toposequence in southeastern Amazonia.

NASA Astrophysics Data System (ADS)

Stucchi Boschi, Raquel; Qin, Mingming; Gimenez, Daniel; Cooper, Miguel

2016-04-01

Modeling is an important tool for better understanding and assessing land use impacts on landscape processes. A key point for environmental modeling is the knowledge of soil hydraulic properties. However, direct determination of soil hydraulic properties is difficult and costly, particularly in vast and remote regions such as one constituting the Amazon Biome. One way to overcome this problem is to extrapolate accurately estimated data to pedologically similar sites. The van Genuchten (VG) parametric equation is the most commonly used for modeling SWRC. The use of a Bayesian approach in combination with the Markov chain Monte Carlo to estimate the VG parameters has several advantages compared to the widely used global optimization techniques. The Bayesian approach provides posterior distributions of parameters that are independent from the initial values and allow for uncertainty analyses. The main objectives of this study were: i) to estimate hydraulic parameters from data of pasture and forest sites by the Bayesian inverse modeling approach; and ii) to investigate the extrapolation of the estimated VG parameters to a nearby toposequence with pedologically similar soils to those used for its estimate. The parameters were estimated from volumetric water content and tension observations obtained after rainfall events during a 207-day period from pasture and forest sites located in the southeastern Amazon region. These data were used to run HYDRUS-1D under a Differential Evolution Adaptive Metropolis (DREAM) scheme 10,000 times, and only the last 2,500 times were used to calculate the posterior distributions of each hydraulic parameter along with 95% confidence intervals (CI) of volumetric water content and tension time series. Then, the posterior distributions were used to generate hydraulic parameters for two nearby toposequences composed by six soil profiles, three are under forest and three are under pasture. The parameters of the nearby site were accepted when the predicted tension time series were within the 95% CI which is derived from the calibration site using DREAM scheme.

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey

USGS Publications Warehouse

Link, William; Sauer, John R.

2016-01-01

The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.

Bayesian networks for maritime traffic accident prevention: benefits and challenges.

PubMed

Hänninen, Maria

2014-12-01

Bayesian networks are quantitative modeling tools whose applications to the maritime traffic safety context are becoming more popular. This paper discusses the utilization of Bayesian networks in maritime safety modeling. Based on literature and the author's own experiences, the paper studies what Bayesian networks can offer to maritime accident prevention and safety modeling and discusses a few challenges in their application to this context. It is argued that the capability of representing rather complex, not necessarily causal but uncertain relationships makes Bayesian networks an attractive modeling tool for the maritime safety and accidents. Furthermore, as the maritime accident and safety data is still rather scarce and has some quality problems, the possibility to combine data with expert knowledge and the easy way of updating the model after acquiring more evidence further enhance their feasibility. However, eliciting the probabilities from the maritime experts might be challenging and the model validation can be tricky. It is concluded that with the utilization of several data sources, Bayesian updating, dynamic modeling, and hidden nodes for latent variables, Bayesian networks are rather well-suited tools for the maritime safety management and decision-making. Copyright © 2014 Elsevier Ltd. All rights reserved.

Nonparametric Bayesian predictive distributions for future order statistics

Treesearch

Richard A. Johnson; James W. Evans; David W. Green

1999-01-01

We derive the predictive distribution for a specified order statistic, determined from a future random sample, under a Dirichlet process prior. Two variants of the approach are treated and some limiting cases studied. A practical application to monitoring the strength of lumber is discussed including choices of prior expectation and comparisons made to a Bayesian...

Bayesian Framework for Water Quality Model Uncertainty Estimation and Risk Management

EPA Science Inventory

A formal Bayesian methodology is presented for integrated model calibration and risk-based water quality management using Bayesian Monte Carlo simulation and maximum likelihood estimation (BMCML). The primary focus is on lucid integration of model calibration with risk-based wat...

Bayesian methods to estimate urban growth potential

USGS Publications Warehouse

Smith, Jordan W.; Smart, Lindsey S.; Dorning, Monica; Dupéy, Lauren Nicole; Méley, Andréanne; Meentemeyer, Ross K.

2017-01-01

Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners’ perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners in one of the United States’ most rapidly urbanizing regions − the urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region’s development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners’ intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatially-explicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region’s socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region’s historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth.

Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments

NASA Astrophysics Data System (ADS)

Rings, Joerg; Vrugt, Jasper A.; Schoups, Gerrit; Huisman, Johan A.; Vereecken, Harry

2012-05-01

Bayesian model averaging (BMA) is a standard method for combining predictive distributions from different models. In recent years, this method has enjoyed widespread application and use in many fields of study to improve the spread-skill relationship of forecast ensembles. The BMA predictive probability density function (pdf) of any quantity of interest is a weighted average of pdfs centered around the individual (possibly bias-corrected) forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts, and reflect the individual models skill over a training (calibration) period. The original BMA approach presented by Raftery et al. (2005) assumes that the conditional pdf of each individual model is adequately described with a rather standard Gaussian or Gamma statistical distribution, possibly with a heteroscedastic variance. Here we analyze the advantages of using BMA with a flexible representation of the conditional pdf. A joint particle filtering and Gaussian mixture modeling framework is presented to derive analytically, as closely and consistently as possible, the evolving forecast density (conditional pdf) of each constituent ensemble member. The median forecasts and evolving conditional pdfs of the constituent models are subsequently combined using BMA to derive one overall predictive distribution. This paper introduces the theory and concepts of this new ensemble postprocessing method, and demonstrates its usefulness and applicability by numerical simulation of the rainfall-runoff transformation using discharge data from three different catchments in the contiguous United States. The revised BMA method receives significantly lower-prediction errors than the original default BMA method (due to filtering) with predictive uncertainty intervals that are substantially smaller but still statistically coherent (due to the use of a time-variant conditional pdf).

Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2016-12-01

In classification, prior knowledge is incorporated in a Bayesian framework by assuming that the feature-label distribution belongs to an uncertainty class of feature-label distributions governed by a prior distribution. A posterior distribution is then derived from the prior and the sample data. An optimal Bayesian classifier (OBC) minimizes the expected misclassification error relative to the posterior distribution. From an application perspective, prior construction is critical. The prior distribution is formed by mapping a set of mathematical relations among the features and labels, the prior knowledge, into a distribution governing the probability mass across the uncertainty class. In this paper, we consider prior knowledge in the form of stochastic differential equations (SDEs). We consider a vector SDE in integral form involving a drift vector and dispersion matrix. Having constructed the prior, we develop the optimal Bayesian classifier between two models and examine, via synthetic experiments, the effects of uncertainty in the drift vector and dispersion matrix. We apply the theory to a set of SDEs for the purpose of differentiating the evolutionary history between two species.

A Comparison of General Diagnostic Models (GDM) and Bayesian Networks Using a Middle School Mathematics Test

ERIC Educational Resources Information Center

Wu, Haiyan

2013-01-01

General diagnostic models (GDMs) and Bayesian networks are mathematical frameworks that cover a wide variety of psychometric models. Both extend latent class models, and while GDMs also extend item response theory (IRT) models, Bayesian networks can be parameterized using discretized IRT. The purpose of this study is to examine similarities and…

Construction of monitoring model and algorithm design on passenger security during shipping based on improved Bayesian network.

PubMed

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.

Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

PubMed Central

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping. PMID:25254227

Hierarchical Bayesian Modeling of Fluid-Induced Seismicity

NASA Astrophysics Data System (ADS)

Broccardo, M.; Mignan, A.; Wiemer, S.; Stojadinovic, B.; Giardini, D.

2017-11-01

In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a nonhomogeneous Poisson process with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.

Estimating virus occurrence using Bayesian modeling in multiple drinking water systems of the United States

USGS Publications Warehouse

Varughese, Eunice A.; Brinkman, Nichole E; Anneken, Emily M; Cashdollar, Jennifer S; Fout, G. Shay; Furlong, Edward T.; Kolpin, Dana W.; Glassmeyer, Susan T.; Keely, Scott P

2017-01-01

incorporated into a Bayesian model to more accurately determine viral load in both source and treated water. Results of the Bayesian model indicated that viruses are present in source water and treated water. By using a Bayesian framework that incorporates inhibition, as well as many other parameters that affect viral detection, this study offers an approach for more accurately estimating the occurrence of viral pathogens in environmental waters.

A local approach for focussed Bayesian fusion

NASA Astrophysics Data System (ADS)

Sander, Jennifer; Heizmann, Michael; Goussev, Igor; Beyerer, Jürgen

2009-04-01

Local Bayesian fusion approaches aim to reduce high storage and computational costs of Bayesian fusion which is separated from fixed modeling assumptions. Using the small world formalism, we argue why this proceeding is conform with Bayesian theory. Then, we concentrate on the realization of local Bayesian fusion by focussing the fusion process solely on local regions that are task relevant with a high probability. The resulting local models correspond then to restricted versions of the original one. In a previous publication, we used bounds for the probability of misleading evidence to show the validity of the pre-evaluation of task specific knowledge and prior information which we perform to build local models. In this paper, we prove the validity of this proceeding using information theoretic arguments. For additional efficiency, local Bayesian fusion can be realized in a distributed manner. Here, several local Bayesian fusion tasks are evaluated and unified after the actual fusion process. For the practical realization of distributed local Bayesian fusion, software agents are predestinated. There is a natural analogy between the resulting agent based architecture and criminal investigations in real life. We show how this analogy can be used to improve the efficiency of distributed local Bayesian fusion additionally. Using a landscape model, we present an experimental study of distributed local Bayesian fusion in the field of reconnaissance, which highlights its high potential.

Human biomonitoring pilot study DEMOCOPHES in Germany: Contribution to a harmonized European approach.

PubMed

Schwedler, Gerda; Seiwert, Margarete; Fiddicke, Ulrike; Ißleb, Sissy; Hölzer, Jürgen; Nendza, Julia; Wilhelm, Michael; Wittsiepe, Jürgen; Koch, Holger M; Schindler, Birgit K; Göen, Thomas; Hildebrand, Jörg; Joas, Reinhard; Joas, Anke; Casteleyn, Ludwine; Angerer, Jürgen; Castano, Argelia; Esteban, Marta; Schoeters, Greet; Den Hond, Elly; Sepai, Ovnair; Exley, Karen; Bloemen, Louis; Knudsen, Lisbeth E; Kolossa-Gehring, Marike

2017-06-01

Human biomonitoring (HBM) is an effective tool to assess human exposure to environmental pollutants, but comparable HBM data in Europe are lacking. In order to expedite harmonization of HBM studies on a European scale, the twin projects COPHES (Consortium to Perform Human Biomonitoring on a European Scale) and DEMOCOPHES (Demonstration of a study to Coordinate and Perform Human Biomonitoring on a European Scale) were formed, comprising 35 partners from 27 European countries. In COPHES a research scheme and guidelines were developed to exemplarily measure in a pilot study mercury in hair, cadmium, cotinine and several phthalate metabolites in urine of 6-11year old children and their mothers in an urban and a rural region. Seventeen European countries simultaneously conducted this cross-sectional DEMOCOPHES feasibility study. The German study population was taken in the city of Bochum and in the Higher Sauerland District, comprising 120 mother-child pairs. In the present paper features of the study implementation are presented. German exposure concentrations of the pollutants are reported and compared with European average concentrations from DEMOCOPHES and with those measured in the representative German Environmental Survey (GerES IV). German DEMOCOPHES concentrations for mercury and cotinine were lower than the European average. However, 47% of the children were still exposed to environmental tobacco smoke (ETS) outside their home, which gives further potential for enhancing protection of children from ETS. Compared with samples from the other European countries German participating children had lower concentrations of the phthalate metabolites MEP and of the sum of 3 DEHP-metabolites (MEHP, 5OH-MEHP and 5oxo-MEHP), about the same concentrations of the phthalate metabolites MBzP and MiBP and higher concentrations of the phthalate metabolite MnBP. 2.5% of the German children had concentrations of the sum of 4 DEHP-metabolites and 4.2% had concentrations of MnBP that exceeded health based guidance values, indicating reasons for concern. Continuous HBM is necessary to track changes of pollutant exposure over time. Therefore Germany will continue to cooperate on the harmonisation of European human biomonitoring to support the chemicals regulation with the best possible exposure data to protect Europe's people against environmental health risks. Copyright © 2017 The Authors. Published by Elsevier GmbH.. All rights reserved.

Theoretical and observational constraints on Tachyon Inflation

NASA Astrophysics Data System (ADS)

Barbosa-Cendejas, Nandinii; De-Santiago, Josue; German, Gabriel; Hidalgo, Juan Carlos; Rigel Mora-Luna, Refugio

2018-03-01

We constrain several models in Tachyonic Inflation derived from the large-N formalism by considering theoretical aspects as well as the latest observational data. On the theoretical side, we assess the field range of our models by means of the excursion of the equivalent canonical field. On the observational side, we employ BK14+PLANCK+BAO data to perform a parameter estimation analysis as well as a Bayesian model selection to distinguish the most favoured models among all four classes here presented. We observe that the original potential V propto sech(T) is strongly disfavoured by observations with respect to a reference model with flat priors on inflationary observables. This realisation of Tachyon inflation also presents a large field range which may demand further quantum corrections. We also provide examples of potentials derived from the polynomial and the perturbative classes which are both statistically favoured and theoretically acceptable.

Bayesian Hierarchical Modeling for Big Data Fusion in Soil Hydrology

NASA Astrophysics Data System (ADS)

Mohanty, B.; Kathuria, D.; Katzfuss, M.

2016-12-01

Soil moisture datasets from remote sensing (RS) platforms (such as SMOS and SMAP) and reanalysis products from land surface models are typically available on a coarse spatial granularity of several square km. Ground based sensors on the other hand provide observations on a finer spatial scale (meter scale or less) but are sparsely available. Soil moisture is affected by high variability due to complex interactions between geologic, topographic, vegetation and atmospheric variables. Hydrologic processes usually occur at a scale of 1 km or less and therefore spatially ubiquitous and temporally periodic soil moisture products at this scale are required to aid local decision makers in agriculture, weather prediction and reservoir operations. Past literature has largely focused on downscaling RS soil moisture for a small extent of a field or a watershed and hence the applicability of such products has been limited. The present study employs a spatial Bayesian Hierarchical Model (BHM) to derive soil moisture products at a spatial scale of 1 km for the state of Oklahoma by fusing point scale Mesonet data and coarse scale RS data for soil moisture and its auxiliary covariates such as precipitation, topography, soil texture and vegetation. It is seen that the BHM model handles change of support problems easily while performing accurate uncertainty quantification arising from measurement errors and imperfect retrieval algorithms. The computational challenge arising due to the large number of measurements is tackled by utilizing basis function approaches and likelihood approximations. The BHM model can be considered as a complex Bayesian extension of traditional geostatistical prediction methods (such as Kriging) for large datasets in the presence of uncertainties.

A phylogenetic framework for root lesion nematodes of the genus Pratylenchus (Nematoda): Evidence from 18S and D2-D3 expansion segments of 28S ribosomal RNA genes and morphological characters.

PubMed

Subbotin, Sergei A; Ragsdale, Erik J; Mullens, Teresa; Roberts, Philip A; Mundo-Ocampo, Manuel; Baldwin, James G

2008-08-01

The root lesion nematodes of the genus Pratylenchus Filipjev, 1936 are migratory endoparasites of plant roots, considered among the most widespread and important nematode parasites in a variety of crops. We obtained gene sequences from the D2 and D3 expansion segments of 28S rRNA partial and 18S rRNA from 31 populations belonging to 11 valid and two unidentified species of root lesion nematodes and five outgroup taxa. These datasets were analyzed using maximum parsimony and Bayesian inference. The alignments were generated using the secondary structure models for these molecules and analyzed with Bayesian inference under the standard models and the complex model, considering helices under the doublet model and loops and bulges under the general time reversible model. The phylogenetic informativeness of morphological characters is tested by reconstruction of their histories on rRNA based trees using parallel parsimony and Bayesian approaches. Phylogenetic and sequence analyses of the 28S D2-D3 dataset with 145 accessions for 28 species and 18S dataset with 68 accessions for 15 species confirmed among large numbers of geographical diverse isolates that most classical morphospecies are monophyletic. Phylogenetic analyses revealed at least six distinct major clades of examined Pratylenchus species and these clades are generally congruent with those defined by characters derived from lip patterns, numbers of lip annules, and spermatheca shape. Morphological results suggest the need for sophisticated character discovery and analysis for morphology based phylogenetics in nematodes.

SWARM : a scientific workflow for supporting Bayesian approaches to improve metabolic models.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shi, X.; Stevens, R.; Mathematics and Computer Science

2008-01-01

With the exponential growth of complete genome sequences, the analysis of these sequences is becoming a powerful approach to build genome-scale metabolic models. These models can be used to study individual molecular components and their relationships, and eventually study cells as systems. However, constructing genome-scale metabolic models manually is time-consuming and labor-intensive. This property of manual model-building process causes the fact that much fewer genome-scale metabolic models are available comparing to hundreds of genome sequences available. To tackle this problem, we design SWARM, a scientific workflow that can be utilized to improve genome-scale metabolic models in high-throughput fashion. SWARM dealsmore » with a range of issues including the integration of data across distributed resources, data format conversions, data update, and data provenance. Putting altogether, SWARM streamlines the whole modeling process that includes extracting data from various resources, deriving training datasets to train a set of predictors and applying Bayesian techniques to assemble the predictors, inferring on the ensemble of predictors to insert missing data, and eventually improving draft metabolic networks automatically. By the enhancement of metabolic model construction, SWARM enables scientists to generate many genome-scale metabolic models within a short period of time and with less effort.« less

Bayesian Models for Astrophysical Data Using R, JAGS, Python, and Stan

NASA Astrophysics Data System (ADS)

Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.

2017-05-01

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

Cyber-T web server: differential analysis of high-throughput data.

PubMed

Kayala, Matthew A; Baldi, Pierre

2012-07-01

The Bayesian regularization method for high-throughput differential analysis, described in Baldi and Long (A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001: 17: 509-519) and implemented in the Cyber-T web server, is one of the most widely validated. Cyber-T implements a t-test using a Bayesian framework to compute a regularized variance of the measurements associated with each probe under each condition. This regularized estimate is derived by flexibly combining the empirical measurements with a prior, or background, derived from pooling measurements associated with probes in the same neighborhood. This approach flexibly addresses problems associated with low replication levels and technology biases, not only for DNA microarrays, but also for other technologies, such as protein arrays, quantitative mass spectrometry and next-generation sequencing (RNA-seq). Here we present an update to the Cyber-T web server, incorporating several useful new additions and improvements. Several preprocessing data normalization options including logarithmic and (Variance Stabilizing Normalization) VSN transforms are included. To augment two-sample t-tests, a one-way analysis of variance is implemented. Several methods for multiple tests correction, including standard frequentist methods and a probabilistic mixture model treatment, are available. Diagnostic plots allow visual assessment of the results. The web server provides comprehensive documentation and example data sets. The Cyber-T web server, with R source code and data sets, is publicly available at http://cybert.ics.uci.edu/.

Information content of incubation experiments for inverse estimation of pools in the Rothamsted carbon model: a Bayesian perspective

NASA Astrophysics Data System (ADS)

Scharnagl, B.; Vrugt, J. A.; Vereecken, H.; Herbst, M.

2010-02-01

A major drawback of current soil organic carbon (SOC) models is that their conceptually defined pools do not necessarily correspond to measurable SOC fractions in real practice. This not only impairs our ability to rigorously evaluate SOC models but also makes it difficult to derive accurate initial states of the individual carbon pools. In this study, we tested the feasibility of inverse modelling for estimating pools in the Rothamsted carbon model (ROTHC) using mineralization rates observed during incubation experiments. This inverse approach may provide an alternative to existing SOC fractionation methods. To illustrate our approach, we used a time series of synthetically generated mineralization rates using the ROTHC model. We adopted a Bayesian approach using the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm to infer probability density functions of the various carbon pools at the start of incubation. The Kullback-Leibler divergence was used to quantify the information content of the mineralization rate data. Our results indicate that measured mineralization rates generally provided sufficient information to reliably estimate all carbon pools in the ROTHC model. The incubation time necessary to appropriately constrain all pools was about 900 days. The use of prior information on microbial biomass carbon significantly reduced the uncertainty of the initial carbon pools, decreasing the required incubation time to about 600 days. Simultaneous estimation of initial carbon pools and decomposition rate constants significantly increased the uncertainty of the carbon pools. This effect was most pronounced for the intermediate and slow pools. Altogether, our results demonstrate that it is particularly difficult to derive reasonable estimates of the humified organic matter pool and the inert organic matter pool from inverse modelling of mineralization rates observed during incubation experiments.

Merging Digital Surface Models Implementing Bayesian Approaches

NASA Astrophysics Data System (ADS)

Sadeq, H.; Drummond, J.; Li, Z.

2016-06-01

In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.

Bayesian functional integral method for inferring continuous data from discrete measurements.

PubMed

Heuett, William J; Miller, Bernard V; Racette, Susan B; Holloszy, John O; Chow, Carson C; Periwal, Vipul

2012-02-08

Inference of the insulin secretion rate (ISR) from C-peptide measurements as a quantification of pancreatic β-cell function is clinically important in diseases related to reduced insulin sensitivity and insulin action. ISR derived from C-peptide concentration is an example of nonparametric Bayesian model selection where a proposed ISR time-course is considered to be a "model". An inferred value of inaccessible continuous variables from discrete observable data is often problematic in biology and medicine, because it is a priori unclear how robust the inference is to the deletion of data points, and a closely related question, how much smoothness or continuity the data actually support. Predictions weighted by the posterior distribution can be cast as functional integrals as used in statistical field theory. Functional integrals are generally difficult to evaluate, especially for nonanalytic constraints such as positivity of the estimated parameters. We propose a computationally tractable method that uses the exact solution of an associated likelihood function as a prior probability distribution for a Markov-chain Monte Carlo evaluation of the posterior for the full model. As a concrete application of our method, we calculate the ISR from actual clinical C-peptide measurements in human subjects with varying degrees of insulin sensitivity. Our method demonstrates the feasibility of functional integral Bayesian model selection as a practical method for such data-driven inference, allowing the data to determine the smoothing timescale and the width of the prior probability distribution on the space of models. In particular, our model comparison method determines the discrete time-step for interpolation of the unobservable continuous variable that is supported by the data. Attempts to go to finer discrete time-steps lead to less likely models. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Sizing Up the Milky Way: A Bayesian Mixture Model Meta-analysis of Photometric Scale Length Measurements

NASA Astrophysics Data System (ADS)

Licquia, Timothy C.; Newman, Jeffrey A.

2016-11-01

The exponential scale length (L d ) of the Milky Way’s (MW’s) disk is a critical parameter for describing the global physical size of our Galaxy, important both for interpreting other Galactic measurements and helping us to understand how our Galaxy fits into extragalactic contexts. Unfortunately, current estimates span a wide range of values and are often statistically incompatible with one another. Here, we perform a Bayesian meta-analysis to determine an improved, aggregate estimate for L d , utilizing a mixture-model approach to account for the possibility that any one measurement has not properly accounted for all statistical or systematic errors. Within this machinery, we explore a variety of ways of modeling the nature of problematic measurements, and then employ a Bayesian model averaging technique to derive net posterior distributions that incorporate any model-selection uncertainty. Our meta-analysis combines 29 different (15 visible and 14 infrared) photometric measurements of L d available in the literature; these involve a broad assortment of observational data sets, MW models and assumptions, and methodologies, all tabulated herein. Analyzing the visible and infrared measurements separately yields estimates for L d of {2.71}-0.20+0.22 kpc and {2.51}-0.13+0.15 kpc, respectively, whereas considering them all combined yields 2.64 ± 0.13 kpc. The ratio between the visible and infrared scale lengths determined here is very similar to that measured in external spiral galaxies. We use these results to update the model of the Galactic disk from our previous work, constraining its stellar mass to be {4.8}-1.1+1.5× {10}10 M ⊙, and the MW’s total stellar mass to be {5.7}-1.1+1.5× {10}10 M ⊙.

Bayesian inference supports a location and neighbour-dependent model of DNA methylation propagation at the MGMT gene promoter in lung tumours.

PubMed

Bonello, Nicolas; Sampson, James; Burn, John; Wilson, Ian J; McGrown, Gail; Margison, Geoff P; Thorncroft, Mary; Crossbie, Philip; Povey, Andrew C; Santibanez-Koref, Mauro; Walters, Kevin

2013-11-07

We exploit model-based Bayesian inference methodologies to analyse lung tumour-derived methylation data from a CpG island in the O6-methylguanine-DNA methyltransferase (MGMT) promoter. Interest is in modelling the changes in methylation patterns in a CpG island in the first exon of the promoter during lung tumour development. We propose four competils of methylation state propagation based on two mechanisms. The first is the location-dependence mechanism in which the probability of a gain or loss of methylation at a CpG within the promoter depends upon its location in the CpG sequence. The second mechanism is that of neighbour-dependence in which gain or loss of methylation at a CpG depends upon the methylation status of the immediately preceding CpG. Our data comprises the methylation status at 12 CpGs near the 5' end of the CpG island in two lung tumour samples for both alleles of a nearby polymorphism. We use approximate Bayesian computation, a computationally intensive rejection-sampling algorithm to infer model parameters and compare models without the need to evaluate the likelihood function. We compare the four proposed models using two criteria: the approximate Bayes factors and the distribution of the Euclidean distance between the summary statistics of the observed and simulated datasets. Our model-based analysis demonstrates compelling evidence for both location and neighbour dependence in the process of aberrant DNA methylation of this MGMT promoter CpG island in lung tumours. We find equivocal evidence to support the hypothesis that the methylation patterns of the two alleles evolve independently. © 2013 Published by Elsevier Ltd. All rights reserved.

Atmospheric Tracer Inverse Modeling Using Markov Chain Monte Carlo (MCMC)

NASA Astrophysics Data System (ADS)

Kasibhatla, P.

2004-12-01

In recent years, there has been an increasing emphasis on the use of Bayesian statistical estimation techniques to characterize the temporal and spatial variability of atmospheric trace gas sources and sinks. The applications have been varied in terms of the particular species of interest, as well as in terms of the spatial and temporal resolution of the estimated fluxes. However, one common characteristic has been the use of relatively simple statistical models for describing the measurement and chemical transport model error statistics and prior source statistics. For example, multivariate normal probability distribution functions (pdfs) are commonly used to model these quantities and inverse source estimates are derived for fixed values of pdf paramaters. While the advantage of this approach is that closed form analytical solutions for the a posteriori pdfs of interest are available, it is worth exploring Bayesian analysis approaches which allow for a more general treatment of error and prior source statistics. Here, we present an application of the Markov Chain Monte Carlo (MCMC) methodology to an atmospheric tracer inversion problem to demonstrate how more gereral statistical models for errors can be incorporated into the analysis in a relatively straightforward manner. The MCMC approach to Bayesian analysis, which has found wide application in a variety of fields, is a statistical simulation approach that involves computing moments of interest of the a posteriori pdf by efficiently sampling this pdf. The specific inverse problem that we focus on is the annual mean CO2 source/sink estimation problem considered by the TransCom3 project. TransCom3 was a collaborative effort involving various modeling groups and followed a common modeling and analysis protocoal. As such, this problem provides a convenient case study to demonstrate the applicability of the MCMC methodology to atmospheric tracer source/sink estimation problems.

Learning classification trees

NASA Technical Reports Server (NTRS)

Buntine, Wray

1991-01-01

Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

Error-in-variables models in calibration

NASA Astrophysics Data System (ADS)

Lira, I.; Grientschnig, D.

2017-12-01

In many calibration operations, the stimuli applied to the measuring system or instrument under test are derived from measurement standards whose values may be considered to be perfectly known. In that case, it is assumed that calibration uncertainty arises solely from inexact measurement of the responses, from imperfect control of the calibration process and from the possible inaccuracy of the calibration model. However, the premise that the stimuli are completely known is never strictly fulfilled and in some instances it may be grossly inadequate. Then, error-in-variables (EIV) regression models have to be employed. In metrology, these models have been approached mostly from the frequentist perspective. In contrast, not much guidance is available on their Bayesian analysis. In this paper, we first present a brief summary of the conventional statistical techniques that have been developed to deal with EIV models in calibration. We then proceed to discuss the alternative Bayesian framework under some simplifying assumptions. Through a detailed example about the calibration of an instrument for measuring flow rates, we provide advice on how the user of the calibration function should employ the latter framework for inferring the stimulus acting on the calibrated device when, in use, a certain response is measured.

Hierarchical Bayesian Markov switching models with application to predicting spawning success of shovelnose sturgeon

USGS Publications Warehouse

Holan, S.H.; Davis, G.M.; Wildhaber, M.L.; DeLonay, A.J.; Papoulias, D.M.

2009-01-01

The timing of spawning in fish is tightly linked to environmental factors; however, these factors are not very well understood for many species. Specifically, little information is available to guide recruitment efforts for endangered species such as the sturgeon. Therefore, we propose a Bayesian hierarchical model for predicting the success of spawning of the shovelnose sturgeon which uses both biological and behavioural (longitudinal) data. In particular, we use data that were produced from a tracking study that was conducted in the Lower Missouri River. The data that were produced from this study consist of biological variables associated with readiness to spawn along with longitudinal behavioural data collected by using telemetry and archival data storage tags. These high frequency data are complex both biologically and in the underlying behavioural process. To accommodate such complexity we developed a hierarchical linear regression model that uses an eigenvalue predictor, derived from the transition probability matrix of a two-state Markov switching model with generalized auto-regressive conditional heteroscedastic dynamics. Finally, to minimize the computational burden that is associated with estimation of this model, a parallel computing approach is proposed. ?? Journal compilation 2009 Royal Statistical Society.

Sequential bearings-only-tracking initiation with particle filtering method.

PubMed

Liu, Bin; Hao, Chengpeng

2013-01-01

The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association problem). In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function (pdf) in the optimal Bayesian sequential estimation framework. The consequence of this nonlinear/non-Gaussian context is the absence of a closed-form solution. This paper models the linkage uncertainty and the nonlinear/non-Gaussian estimation problem jointly with solid Bayesian formalism. A particle filtering (PF) algorithm is derived for estimating the model's parameters in a sequential manner. Numerical results show that the proposed solution provides a significant benefit over the most commonly used methods, IPDA and IMMPDA. The posterior Cramér-Rao bounds are also involved for performance evaluation.

Assessing global vegetation activity using spatio-temporal Bayesian modelling

NASA Astrophysics Data System (ADS)

Mulder, Vera L.; van Eck, Christel M.; Friedlingstein, Pierre; Regnier, Pierre A. G.

2016-04-01

This work demonstrates the potential of modelling vegetation activity using a hierarchical Bayesian spatio-temporal model. This approach allows modelling changes in vegetation and climate simultaneous in space and time. Changes of vegetation activity such as phenology are modelled as a dynamic process depending on climate variability in both space and time. Additionally, differences in observed vegetation status can be contributed to other abiotic ecosystem properties, e.g. soil and terrain properties. Although these properties do not change in time, they do change in space and may provide valuable information in addition to the climate dynamics. The spatio-temporal Bayesian models were calibrated at a regional scale because the local trends in space and time can be better captured by the model. The regional subsets were defined according to the SREX segmentation, as defined by the IPCC. Each region is considered being relatively homogeneous in terms of large-scale climate and biomes, still capturing small-scale (grid-cell level) variability. Modelling within these regions is hence expected to be less uncertain due to the absence of these large-scale patterns, compared to a global approach. This overall modelling approach allows the comparison of model behavior for the different regions and may provide insights on the main dynamic processes driving the interaction between vegetation and climate within different regions. The data employed in this study encompasses the global datasets for soil properties (SoilGrids), terrain properties (Global Relief Model based on SRTM DEM and ETOPO), monthly time series of satellite-derived vegetation indices (GIMMS NDVI3g) and climate variables (Princeton Meteorological Forcing Dataset). The findings proved the potential of a spatio-temporal Bayesian modelling approach for assessing vegetation dynamics, at a regional scale. The observed interrelationships of the employed data and the different spatial and temporal trends support our hypothesis. That is, the change of vegetation in space and time may be better understood when modelling vegetation change as both a dynamic and multivariate process. Therefore, future research will focus on a multivariate dynamical spatio-temporal modelling approach. This ongoing research is performed within the context of the project "Global impacts of hydrological and climatic extremes on vegetation" (project acronym: SAT-EX) which is part of the Belgian research programme for Earth Observation Stereo III.

Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum

2006-01-01

A Bayesian approach is developed for analyzing nonlinear structural equation models with nonignorable missing data. The nonignorable missingness mechanism is specified by a logistic regression model. A hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm is used to produce the joint Bayesian estimates of…

Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

ERIC Educational Resources Information Center

Levy, Roy

2014-01-01

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

Bayesian techniques for analyzing group differences in the Iowa Gambling Task: A case study of intuitive and deliberate decision-makers.

PubMed

Steingroever, Helen; Pachur, Thorsten; Šmíra, Martin; Lee, Michael D

2018-06-01

The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.

Invited commentary: Lost in estimation--searching for alternatives to markov chains to fit complex Bayesian models.

PubMed

Molitor, John

2012-03-01

Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.

The Bayesian reader: explaining word recognition as an optimal Bayesian decision process.

PubMed

Norris, Dennis

2006-04-01

This article presents a theory of visual word recognition that assumes that, in the tasks of word identification, lexical decision, and semantic categorization, human readers behave as optimal Bayesian decision makers. This leads to the development of a computational model of word recognition, the Bayesian reader. The Bayesian reader successfully simulates some of the most significant data on human reading. The model accounts for the nature of the function relating word frequency to reaction time and identification threshold, the effects of neighborhood density and its interaction with frequency, and the variation in the pattern of neighborhood density effects seen in different experimental tasks. Both the general behavior of the model and the way the model predicts different patterns of results in different tasks follow entirely from the assumption that human readers approximate optimal Bayesian decision makers. ((c) 2006 APA, all rights reserved).

Bayesian cloud detection for MERIS, AATSR, and their combination

NASA Astrophysics Data System (ADS)

Hollstein, A.; Fischer, J.; Carbajal Henken, C.; Preusker, R.

2014-11-01

A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud masks were designed to be numerically efficient and suited for the processing of large amounts of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient amounts of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.

Bayesian cloud detection for MERIS, AATSR, and their combination

NASA Astrophysics Data System (ADS)

Hollstein, A.; Fischer, J.; Carbajal Henken, C.; Preusker, R.

2015-04-01

A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud detection schemes were designed to be numerically efficient and suited for the processing of large numbers of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient numbers of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.

Speech Enhancement, Gain, and Noise Spectrum Adaptation Using Approximate Bayesian Estimation

PubMed Central

Hao, Jiucang; Attias, Hagai; Nagarajan, Srikantan; Lee, Te-Won; Sejnowski, Terrence J.

2010-01-01

This paper presents a new approximate Bayesian estimator for enhancing a noisy speech signal. The speech model is assumed to be a Gaussian mixture model (GMM) in the log-spectral domain. This is in contrast to most current models in frequency domain. Exact signal estimation is a computationally intractable problem. We derive three approximations to enhance the efficiency of signal estimation. The Gaussian approximation transforms the log-spectral domain GMM into the frequency domain using minimal Kullback–Leiber (KL)-divergency criterion. The frequency domain Laplace method computes the maximum a posteriori (MAP) estimator for the spectral amplitude. Correspondingly, the log-spectral domain Laplace method computes the MAP estimator for the log-spectral amplitude. Further, the gain and noise spectrum adaptation are implemented using the expectation–maximization (EM) algorithm within the GMM under Gaussian approximation. The proposed algorithms are evaluated by applying them to enhance the speeches corrupted by the speech-shaped noise (SSN). The experimental results demonstrate that the proposed algorithms offer improved signal-to-noise ratio, lower word recognition error rate, and less spectral distortion. PMID:20428253

Stochastic modelling, Bayesian inference, and new in vivo measurements elucidate the debated mtDNA bottleneck mechanism

PubMed Central

Johnston, Iain G; Burgstaller, Joerg P; Havlicek, Vitezslav; Kolbe, Thomas; Rülicke, Thomas; Brem, Gottfried; Poulton, Jo; Jones, Nick S

2015-01-01

Dangerous damage to mitochondrial DNA (mtDNA) can be ameliorated during mammalian development through a highly debated mechanism called the mtDNA bottleneck. Uncertainty surrounding this process limits our ability to address inherited mtDNA diseases. We produce a new, physically motivated, generalisable theoretical model for mtDNA populations during development, allowing the first statistical comparison of proposed bottleneck mechanisms. Using approximate Bayesian computation and mouse data, we find most statistical support for a combination of binomial partitioning of mtDNAs at cell divisions and random mtDNA turnover, meaning that the debated exact magnitude of mtDNA copy number depletion is flexible. New experimental measurements from a wild-derived mtDNA pairing in mice confirm the theoretical predictions of this model. We analytically solve a mathematical description of this mechanism, computing probabilities of mtDNA disease onset, efficacy of clinical sampling strategies, and effects of potential dynamic interventions, thus developing a quantitative and experimentally-supported stochastic theory of the bottleneck. DOI: http://dx.doi.org/10.7554/eLife.07464.001 PMID:26035426

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been developed and widely applied, but there is still room for improvements. Future research in the context of Bayesian flood forecasting should be on assimilation of various sources of newly available information and improvement of predictive performance assessment methods.

Bayesian modeling of flexible cognitive control

PubMed Central

Jiang, Jiefeng; Heller, Katherine; Egner, Tobias

2014-01-01

“Cognitive control” describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation. PMID:24929218

Bayesian generalized linear mixed modeling of Tuberculosis using informative priors.

PubMed

Ojo, Oluwatobi Blessing; Lougue, Siaka; Woldegerima, Woldegebriel Assefa

2017-01-01

TB is rated as one of the world's deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014.

Bayesian statistics in medicine: a 25 year review.

PubMed

Ashby, Deborah

2006-11-15

This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

A pilot study: the development of a culturally tailored Malaysian Diabetes Education Module (MY-DEMO) based on the Health Belief Model

PubMed Central

2014-01-01

Background Diabetes education and self-care remains the cornerstone of diabetes management. There are many structured diabetes modules available in the United Kingdom, Europe and United States of America. Contrastingly, few structured and validated diabetes modules are available in Malaysia. This pilot study aims to develop and validate diabetes education material suitable and tailored for a multicultural society like Malaysia. Methods The theoretical framework of this module was founded from the Health Belief Model (HBM). The participants were assessed using 6-item pre- and post-test questionnaires that measured some of the known HBM constructs namely cues to action, perceived severity and perceived benefit. Data was analysed using PASW Statistics 18.0. Results The pre- and post-test questionnaires were administered to 88 participants (31 males). In general, there was a significant increase in the total score in post-test (97.34 ± 6.13%) compared to pre-test (92.80 ± 12.83%) (p < 0.05) and a significant increase in excellent score (>85%) at post-test (84.1%) compared to pre-test (70.5%) (p < 0.05). There was an improvement in post-test score in 4 of 6 items tested. The remaining 2 items which measured the perceived severity and cues to action had poorer post-test score. Conclusions The preliminary results from this pilot study suggest contextualised content material embedded within MY DEMO maybe suitable for integration with the existing diabetes education programmes. This was the first known validated diabetes education programme available in the Malay language. PMID:24708715

A BAYESIAN APPROACH TO DERIVING AGES OF INDIVIDUAL FIELD WHITE DWARFS

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Malley, Erin M.; Von Hippel, Ted; Van Dyk, David A., E-mail: ted.vonhippel@erau.edu, E-mail: dvandyke@imperial.ac.uk

2013-09-20

We apply a self-consistent and robust Bayesian statistical approach to determine the ages, distances, and zero-age main sequence (ZAMS) masses of 28 field DA white dwarfs (WDs) with ages of approximately 4-8 Gyr. Our technique requires only quality optical and near-infrared photometry to derive ages with <15% uncertainties, generally with little sensitivity to our choice of modern initial-final mass relation. We find that age, distance, and ZAMS mass are correlated in a manner that is too complex to be captured by traditional error propagation techniques. We further find that the posterior distributions of age are often asymmetric, indicating that themore » standard approach to deriving WD ages can yield misleading results.« less

Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

PubMed Central

Murakami, Yohei

2014-01-01

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor. PMID:25089832

Potential uncertainty reduction in model-averaged benchmark dose estimates informed by an additional dose study.

PubMed

Shao, Kan; Small, Mitchell J

2011-10-01

A methodology is presented for assessing the information value of an additional dosage experiment in existing bioassay studies. The analysis demonstrates the potential reduction in the uncertainty of toxicity metrics derived from expanded studies, providing insights for future studies. Bayesian methods are used to fit alternative dose-response models using Markov chain Monte Carlo (MCMC) simulation for parameter estimation and Bayesian model averaging (BMA) is used to compare and combine the alternative models. BMA predictions for benchmark dose (BMD) are developed, with uncertainty in these predictions used to derive the lower bound BMDL. The MCMC and BMA results provide a basis for a subsequent Monte Carlo analysis that backcasts the dosage where an additional test group would have been most beneficial in reducing the uncertainty in the BMD prediction, along with the magnitude of the expected uncertainty reduction. Uncertainty reductions are measured in terms of reduced interval widths of predicted BMD values and increases in BMDL values that occur as a result of this reduced uncertainty. The methodology is illustrated using two existing data sets for TCDD carcinogenicity, fitted with two alternative dose-response models (logistic and quantal-linear). The example shows that an additional dose at a relatively high value would have been most effective for reducing the uncertainty in BMA BMD estimates, with predicted reductions in the widths of uncertainty intervals of approximately 30%, and expected increases in BMDL values of 5-10%. The results demonstrate that dose selection for studies that subsequently inform dose-response models can benefit from consideration of how these models will be fit, combined, and interpreted. © 2011 Society for Risk Analysis.

Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism

PubMed Central

Marković, Dimitrije; Gläscher, Jan; Bossaerts, Peter; O’Doherty, John; Kiebel, Stefan J.

2015-01-01

For making decisions in everyday life we often have first to infer the set of environmental features that are relevant for the current task. Here we investigated the computational mechanisms underlying the evolution of beliefs about the relevance of environmental features in a dynamical and noisy environment. For this purpose we designed a probabilistic Wisconsin card sorting task (WCST) with belief solicitation, in which subjects were presented with stimuli composed of multiple visual features. At each moment in time a particular feature was relevant for obtaining reward, and participants had to infer which feature was relevant and report their beliefs accordingly. To test the hypothesis that attentional focus modulates the belief update process, we derived and fitted several probabilistic and non-probabilistic behavioral models, which either incorporate a dynamical model of attentional focus, in the form of a hierarchical winner-take-all neuronal network, or a diffusive model, without attention-like features. We used Bayesian model selection to identify the most likely generative model of subjects’ behavior and found that attention-like features in the behavioral model are essential for explaining subjects’ responses. Furthermore, we demonstrate a method for integrating both connectionist and Bayesian models of decision making within a single framework that allowed us to infer hidden belief processes of human subjects. PMID:26495984

Bayesian population analysis of a washin-washout physiologically based pharmacokinetic model for acetone

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moerk, Anna-Karin, E-mail: anna-karin.mork@ki.s; Jonsson, Fredrik; Pharsight, a Certara company, St. Louis, MO

2009-11-01

The aim of this study was to derive improved estimates of population variability and uncertainty of physiologically based pharmacokinetic (PBPK) model parameters, especially of those related to the washin-washout behavior of polar volatile substances. This was done by optimizing a previously published washin-washout PBPK model for acetone in a Bayesian framework using Markov chain Monte Carlo simulation. The sensitivity of the model parameters was investigated by creating four different prior sets, where the uncertainty surrounding the population variability of the physiological model parameters was given values corresponding to coefficients of variation of 1%, 25%, 50%, and 100%, respectively. The PBPKmore » model was calibrated to toxicokinetic data from 2 previous studies where 18 volunteers were exposed to 250-550 ppm of acetone at various levels of workload. The updated PBPK model provided a good description of the concentrations in arterial, venous, and exhaled air. The precision of most of the model parameter estimates was improved. New information was particularly gained on the population distribution of the parameters governing the washin-washout effect. The results presented herein provide a good starting point to estimate the target dose of acetone in the working and general populations for risk assessment purposes.« less

Bayesian probabilistic approach for inverse source determination from limited and noisy chemical or biological sensor concentration measurements

NASA Astrophysics Data System (ADS)

Yee, Eugene

2007-04-01

Although a great deal of research effort has been focused on the forward prediction of the dispersion of contaminants (e.g., chemical and biological warfare agents) released into the turbulent atmosphere, much less work has been directed toward the inverse prediction of agent source location and strength from the measured concentration, even though the importance of this problem for a number of practical applications is obvious. In general, the inverse problem of source reconstruction is ill-posed and unsolvable without additional information. It is demonstrated that a Bayesian probabilistic inferential framework provides a natural and logically consistent method for source reconstruction from a limited number of noisy concentration data. In particular, the Bayesian approach permits one to incorporate prior knowledge about the source as well as additional information regarding both model and data errors. The latter enables a rigorous determination of the uncertainty in the inference of the source parameters (e.g., spatial location, emission rate, release time, etc.), hence extending the potential of the methodology as a tool for quantitative source reconstruction. A model (or, source-receptor relationship) that relates the source distribution to the concentration data measured by a number of sensors is formulated, and Bayesian probability theory is used to derive the posterior probability density function of the source parameters. A computationally efficient methodology for determination of the likelihood function for the problem, based on an adjoint representation of the source-receptor relationship, is described. Furthermore, we describe the application of efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) for sampling from the posterior distribution of the source parameters, the latter of which is required to undertake the Bayesian computation. The Bayesian inferential methodology for source reconstruction is validated against real dispersion data for two cases involving contaminant dispersion in highly disturbed flows over urban and complex environments where the idealizations of horizontal homogeneity and/or temporal stationarity in the flow cannot be applied to simplify the problem. Furthermore, the methodology is applied to the case of reconstruction of multiple sources.

An introduction to Bayesian statistics in health psychology.

PubMed

Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske

2017-09-01

The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design

NASA Astrophysics Data System (ADS)

Leube, P. C.; Geiges, A.; Nowak, W.

2012-02-01

Incorporating hydro(geo)logical data, such as head and tracer data, into stochastic models of (subsurface) flow and transport helps to reduce prediction uncertainty. Because of financial limitations for investigation campaigns, information needs toward modeling or prediction goals should be satisfied efficiently and rationally. Optimal design techniques find the best one among a set of investigation strategies. They optimize the expected impact of data on prediction confidence or related objectives prior to data collection. We introduce a new optimal design method, called PreDIA(gnosis) (Preposterior Data Impact Assessor). PreDIA derives the relevant probability distributions and measures of data utility within a fully Bayesian, generalized, flexible, and accurate framework. It extends the bootstrap filter (BF) and related frameworks to optimal design by marginalizing utility measures over the yet unknown data values. PreDIA is a strictly formal information-processing scheme free of linearizations. It works with arbitrary simulation tools, provides full flexibility concerning measurement types (linear, nonlinear, direct, indirect), allows for any desired task-driven formulations, and can account for various sources of uncertainty (e.g., heterogeneity, geostatistical assumptions, boundary conditions, measurement values, model structure uncertainty, a large class of model errors) via Bayesian geostatistics and model averaging. Existing methods fail to simultaneously provide these crucial advantages, which our method buys at relatively higher-computational costs. We demonstrate the applicability and advantages of PreDIA over conventional linearized methods in a synthetic example of subsurface transport. In the example, we show that informative data is often invisible for linearized methods that confuse zero correlation with statistical independence. Hence, PreDIA will often lead to substantially better sampling designs. Finally, we extend our example to specifically highlight the consideration of conceptual model uncertainty.

A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.

PubMed

Houseman, E Andres; Virji, M Abbas

2017-08-01

Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest. A spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time exposure data. Estimates for task means and covariates from the Bayesian model are compared to those from conventional frequentist models including linear regression, mixed-effects, and time-series models with different autocorrelation structures. Simulations studies are also conducted to evaluate method performance. Simulation studies with percent of measurements below the LOD ranging from 0 to 50% showed lowest root mean squared errors for task means and the least biased standard deviations from the Bayesian model compared to the frequentist models across all levels of LOD. In the application, task means from the Bayesian model were similar to means from the frequentist models, while the standard deviations were different. Parameter estimates for covariates were significant in some frequentist models, but in the Bayesian model their credible intervals contained zero; such discrepancies were observed in multiple datasets. Variance components from the Bayesian model reflected substantial autocorrelation, consistent with the frequentist models, except for the auto-regressive moving average model. Plots of means from the Bayesian model showed good fit to the observed data. The proposed Bayesian model provides an approach for modeling non-stationary autocorrelation in a hierarchical modeling framework to estimate task means, standard deviations, quantiles, and parameter estimates for covariates that are less biased and have better performance characteristics than some of the contemporary methods. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2017.

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Risk assessment of turbine rotor failure using probabilistic ultrasonic non-destructive evaluations

NASA Astrophysics Data System (ADS)

Guan, Xuefei; Zhang, Jingdan; Zhou, S. Kevin; Rasselkorde, El Mahjoub; Abbasi, Waheed A.

2014-02-01

The study presents a method and application of risk assessment methodology for turbine rotor fatigue failure using probabilistic ultrasonic nondestructive evaluations. A rigorous probabilistic modeling for ultrasonic flaw sizing is developed by incorporating the model-assisted probability of detection, and the probability density function (PDF) of the actual flaw size is derived. Two general scenarios, namely the ultrasonic inspection with an identified flaw indication and the ultrasonic inspection without flaw indication, are considered in the derivation. To perform estimations for fatigue reliability and remaining useful life, uncertainties from ultrasonic flaw sizing and fatigue model parameters are systematically included and quantified. The model parameter PDF is estimated using Bayesian parameter estimation and actual fatigue testing data. The overall method is demonstrated using a realistic application of steam turbine rotor, and the risk analysis under given safety criteria is provided to support maintenance planning.

Bayesian Learning and the Psychology of Rule Induction

ERIC Educational Resources Information Center

Endress, Ansgar D.

2013-01-01

In recent years, Bayesian learning models have been applied to an increasing variety of domains. While such models have been criticized on theoretical grounds, the underlying assumptions and predictions are rarely made concrete and tested experimentally. Here, I use Frank and Tenenbaum's (2011) Bayesian model of rule-learning as a case study to…

Properties of the Bayesian Knowledge Tracing Model

ERIC Educational Resources Information Center

van de Sande, Brett

2013-01-01

Bayesian Knowledge Tracing is used very widely to model student learning. It comes in two different forms: The first form is the Bayesian Knowledge Tracing "hidden Markov model" which predicts the probability of correct application of a skill as a function of the number of previous opportunities to apply that skill and the model…

Bayesian Analysis of Longitudinal Data Using Growth Curve Models

ERIC Educational Resources Information Center

Zhang, Zhiyong; Hamagami, Fumiaki; Wang, Lijuan Lijuan; Nesselroade, John R.; Grimm, Kevin J.

2007-01-01

Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data…

Bayesian estimation of the discrete coefficient of determination.

PubMed

Chen, Ting; Braga-Neto, Ulisses M

2016-12-01

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

PubMed

Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Testing students’ e-learning via Facebook through Bayesian structural equation modeling

PubMed Central

Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students’ intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods’ results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated. PMID:28886019

A fast Bayesian approach to discrete object detection in astronomical data sets - PowellSnakes I

NASA Astrophysics Data System (ADS)

Carvalho, Pedro; Rocha, Graça; Hobson, M. P.

2009-03-01

A new fast Bayesian approach is introduced for the detection of discrete objects immersed in a diffuse background. This new method, called PowellSnakes, speeds up traditional Bayesian techniques by (i) replacing the standard form of the likelihood for the parameters characterizing the discrete objects by an alternative exact form that is much quicker to evaluate; (ii) using a simultaneous multiple minimization code based on Powell's direction set algorithm to locate rapidly the local maxima in the posterior and (iii) deciding whether each located posterior peak corresponds to a real object by performing a Bayesian model selection using an approximate evidence value based on a local Gaussian approximation to the peak. The construction of this Gaussian approximation also provides the covariance matrix of the uncertainties in the derived parameter values for the object in question. This new approach provides a speed up in performance by a factor of `100' as compared to existing Bayesian source extraction methods that use Monte Carlo Markov chain to explore the parameter space, such as that presented by Hobson & McLachlan. The method can be implemented in either real or Fourier space. In the case of objects embedded in a homogeneous random field, working in Fourier space provides a further speed up that takes advantage of the fact that the correlation matrix of the background is circulant. We illustrate the capabilities of the method by applying to some simplified toy models. Furthermore, PowellSnakes has the advantage of consistently defining the threshold for acceptance/rejection based on priors which cannot be said of the frequentist methods. We present here the first implementation of this technique (version I). Further improvements to this implementation are currently under investigation and will be published shortly. The application of the method to realistic simulated Planck observations will be presented in a forthcoming publication.

When mechanism matters: Bayesian forecasting using models of ecological diffusion

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

Applying the health belief model to examine news coverage regarding steroids in sports by ABC, CBS, and NBC between March 1990 and May 2008.

PubMed

Quick, Brian L

2010-04-01

The investigation described here examined ABC, CBS, and NBC news coverage of steroids in sports between March 1990 and May 2008. Employing a framing analysis guided by the health belief model (HBM), coverage of the barriers and benefits of using steroids is reported. Overall, the trend by these three news affiliates was to emphasize the illegality of using steroids, whereas considerably less coverage was devoted to the health costs, in terms of both severity and susceptibility, of using steroids. Furthermore, of the health costs reported, the specific consequences of steroid use varied considerably. The results are reported across four timeframes: 1990-2008, 1990-1996, 1997-2002, and 2003-2008.

Bayesian methods for estimating GEBVs of threshold traits

PubMed Central

Wang, C-L; Ding, X-D; Wang, J-Y; Liu, J-F; Fu, W-X; Zhang, Z; Yin, Z-J; Zhang, Q

2013-01-01

Estimation of genomic breeding values is the key step in genomic selection (GS). Many methods have been proposed for continuous traits, but methods for threshold traits are still scarce. Here we introduced threshold model to the framework of GS, and specifically, we extended the three Bayesian methods BayesA, BayesB and BayesCπ on the basis of threshold model for estimating genomic breeding values of threshold traits, and the extended methods are correspondingly termed BayesTA, BayesTB and BayesTCπ. Computing procedures of the three BayesT methods using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the benefit of the presented methods in accuracy with the genomic estimated breeding values (GEBVs) for threshold traits. Factors affecting the performance of the three BayesT methods were addressed. As expected, the three BayesT methods generally performed better than the corresponding normal Bayesian methods, in particular when the number of phenotypic categories was small. In the standard scenario (number of categories=2, incidence=30%, number of quantitative trait loci=50, h2=0.3), the accuracies were improved by 30.4%, 2.4%, and 5.7% points, respectively. In most scenarios, BayesTB and BayesTCπ generated similar accuracies and both performed better than BayesTA. In conclusion, our work proved that threshold model fits well for predicting GEBVs of threshold traits, and BayesTCπ is supposed to be the method of choice for GS of threshold traits. PMID:23149458

Lunar Terrain and Albedo Reconstruction from Apollo Imagery

NASA Technical Reports Server (NTRS)

Nefian, Ara V.; Kim, Taemin; Broxton, Michael; Moratto, Zach

2010-01-01

Generating accurate three dimensional planetary models and albedo maps is becoming increasingly more important as NASA plans more robotics missions to the Moon in the coming years. This paper describes a novel approach for separation of topography and albedo maps from orbital Lunar images. Our method uses an optimal Bayesian correlator to refine the stereo disparity map and generate a set of accurate digital elevation models (DEM). The albedo maps are obtained using a multi-image formation model that relies on the derived DEMs and the Lunar- Lambert reflectance model. The method is demonstrated on a set of high resolution scanned images from the Apollo era missions.

Farmers’ Intentions to Implement Foot and Mouth Disease Control Measures in Ethiopia

PubMed Central

Jemberu, Wudu T.; Mourits, M. C. M.; Hogeveen, H.

2015-01-01

The objectives of this study were to explore farmers’ intentions to implement foot and mouth disease (FMD) control in Ethiopia, and to identify perceptions about the disease and its control measures that influence these intentions using the Health Belief Model (HBM) framework. Data were collected using questionnaires from 293 farmers in three different production systems. The influence of perceptions on the intentions to implement control measures were analyzed using binary logistic regression. The effect of socio-demographic and husbandry variables on perceptions that were found to significantly influence the intentions were analyzed using ordinal logistic regression. Almost all farmers (99%) intended to implement FMD vaccination free of charge. The majority of farmers in the pastoral (94%) and market oriented (92%) systems also had the intention to implement vaccination with charge but only 42% of the crop-livestock mixed farmers had the intention to do so. Only 2% of pastoral and 18% of crop-livestock mixed farmers had the intention to implement herd isolation and animal movement restriction continuously. These proportions increased to 11% for pastoral and 50% for crop-livestock mixed farmers when the measure is applied only during an outbreak. The majority of farmers in the market oriented system (>80%) had the intention to implement herd isolation and animal movement restriction measure, both continuously and during an outbreak. Among the HBM perception constructs, perceived barrier was found to be the only significant predictor of the intention to implement vaccination. Perceived susceptibility, perceived benefit and perceived barrier were the significant predictors of the intention for herd isolation and animal movement restriction measure. In turn, the predicting perceived barrier on vaccination control varied significantly with the production system and the age of farmers. The significant HBM perception predictors on herd isolation and animal movement restriction control were significantly influenced only by the type of production system. The results of this study indicate that farmers’ intentions to apply FMD control measures are variable among production systems, an insight which is relevant in the development of future control programs. Promotion programs aimed at increasing farmers’ motivation to participate in FMD control by charged vaccination or animal movement restriction should give attention to the perceived barriers influencing the intentions to apply these measures. PMID:26375391

Farmers' Intentions to Implement Foot and Mouth Disease Control Measures in Ethiopia.

PubMed

Jemberu, Wudu T; Mourits, M C M; Hogeveen, H

2015-01-01

The objectives of this study were to explore farmers' intentions to implement foot and mouth disease (FMD) control in Ethiopia, and to identify perceptions about the disease and its control measures that influence these intentions using the Health Belief Model (HBM) framework. Data were collected using questionnaires from 293 farmers in three different production systems. The influence of perceptions on the intentions to implement control measures were analyzed using binary logistic regression. The effect of socio-demographic and husbandry variables on perceptions that were found to significantly influence the intentions were analyzed using ordinal logistic regression. Almost all farmers (99%) intended to implement FMD vaccination free of charge. The majority of farmers in the pastoral (94%) and market oriented (92%) systems also had the intention to implement vaccination with charge but only 42% of the crop-livestock mixed farmers had the intention to do so. Only 2% of pastoral and 18% of crop-livestock mixed farmers had the intention to implement herd isolation and animal movement restriction continuously. These proportions increased to 11% for pastoral and 50% for crop-livestock mixed farmers when the measure is applied only during an outbreak. The majority of farmers in the market oriented system (>80%) had the intention to implement herd isolation and animal movement restriction measure, both continuously and during an outbreak. Among the HBM perception constructs, perceived barrier was found to be the only significant predictor of the intention to implement vaccination. Perceived susceptibility, perceived benefit and perceived barrier were the significant predictors of the intention for herd isolation and animal movement restriction measure. In turn, the predicting perceived barrier on vaccination control varied significantly with the production system and the age of farmers. The significant HBM perception predictors on herd isolation and animal movement restriction control were significantly influenced only by the type of production system. The results of this study indicate that farmers' intentions to apply FMD control measures are variable among production systems, an insight which is relevant in the development of future control programs. Promotion programs aimed at increasing farmers' motivation to participate in FMD control by charged vaccination or animal movement restriction should give attention to the perceived barriers influencing the intentions to apply these measures.

Collaborative autonomous sensing with Bayesians in the loop

NASA Astrophysics Data System (ADS)

Ahmed, Nisar

2016-10-01

There is a strong push to develop intelligent unmanned autonomy that complements human reasoning for applications as diverse as wilderness search and rescue, military surveillance, and robotic space exploration. More than just replacing humans for `dull, dirty and dangerous' work, autonomous agents are expected to cope with a whole host of uncertainties while working closely together with humans in new situations. The robotics revolution firmly established the primacy of Bayesian algorithms for tackling challenging perception, learning and decision-making problems. Since the next frontier of autonomy demands the ability to gather information across stretches of time and space that are beyond the reach of a single autonomous agent, the next generation of Bayesian algorithms must capitalize on opportunities to draw upon the sensing and perception abilities of humans-in/on-the-loop. This work summarizes our recent research toward harnessing `human sensors' for information gathering tasks. The basic idea behind is to allow human end users (i.e. non-experts in robotics, statistics, machine learning, etc.) to directly `talk to' the information fusion engine and perceptual processes aboard any autonomous agent. Our approach is grounded in rigorous Bayesian modeling and fusion of flexible semantic information derived from user-friendly interfaces, such as natural language chat and locative hand-drawn sketches. This naturally enables `plug and play' human sensing with existing probabilistic algorithms for planning and perception, and has been successfully demonstrated with human-robot teams in target localization applications.

Pediatricians', obstetricians', gynecologists', and family medicine physicians' experiences with and attitudes about breast-feeding.

PubMed

Anchondo, Inés; Berkeley, Lizabeth; Mulla, Zuber D; Byrd, Theresa; Nuwayhid, Bahij; Handal, Gilbert; Akins, Ralitsa

2012-05-01

Investigate physicians' breast-feeding experiences and attitudes using a survey based on two behavioral theories: theory of reasoned action (TRA) and the health belief model (HBM). There were 73 participants included in the investigation. These participants were resident and faculty physicians from pediatrics, obstetrics/gynecology, and family medicine at a university campus, located on the US-Mexico border. The sample was reduced to 53 and 56 records for the attitude and confidence variables, respectively. Physicians answered a survey about their breast-feeding experiences and attitudes to learn about intention and ability applying constructs from TRA and HBM. An attitude scale, confidence variable (from self-efficacy items), and a lactation training index were created for the analysis. Analysis of the association between physicians' breastfeeding experiences and their attitudes revealed physicians are knowledgeable about breast-feeding and have positive attitudes towards breast-feeding. They did not seem to remember how long they breast-fed their children or whether they enjoyed breast-feeding, but they wanted to continue breast-feeding. Physicians cite work as a main reason for not continuing to breast-feed. Physicians' attitudes toward breast-feeding are positive. They are expected to practice health-promotion behavior including breast-feeding; however, physicians' breast-feeding rates are low and although they are knowledgeable about breast-feeding their training lacks on didactic depth and hands-on experience. If physicians learn more about breast-feeding and breast-feed exclusively and successfully, the rates in the United States would increase naturally.

Bayesian naturalness, simplicity, and testability applied to the B ‑ L MSSM GUT

NASA Astrophysics Data System (ADS)

Fundira, Panashe; Purves, Austin

2018-04-01

Recent years have seen increased use of Bayesian model comparison to quantify notions such as naturalness, simplicity, and testability, especially in the area of supersymmetric model building. After demonstrating that Bayesian model comparison can resolve a paradox that has been raised in the literature concerning the naturalness of the proton mass, we apply Bayesian model comparison to GUTs, an area to which it has not been applied before. We find that the GUTs are substantially favored over the nonunifying puzzle model. Of the GUTs we consider, the B ‑ L MSSM GUT is the most favored, but the MSSM GUT is almost equally favored.

Comparing flood loss models of different complexity

NASA Astrophysics Data System (ADS)

Schröter, Kai; Kreibich, Heidi; Vogel, Kristin; Riggelsen, Carsten; Scherbaum, Frank; Merz, Bruno

2013-04-01

Any deliberation on flood risk requires the consideration of potential flood losses. In particular, reliable flood loss models are needed to evaluate cost-effectiveness of mitigation measures, to assess vulnerability, for comparative risk analysis and financial appraisal during and after floods. In recent years, considerable improvements have been made both concerning the data basis and the methodological approaches used for the development of flood loss models. Despite of that, flood loss models remain an important source of uncertainty. Likewise the temporal and spatial transferability of flood loss models is still limited. This contribution investigates the predictive capability of different flood loss models in a split sample cross regional validation approach. For this purpose, flood loss models of different complexity, i.e. based on different numbers of explaining variables, are learned from a set of damage records that was obtained from a survey after the Elbe flood in 2002. The validation of model predictions is carried out for different flood events in the Elbe and Danube river basins in 2002, 2005 and 2006 for which damage records are available from surveys after the flood events. The models investigated are a stage-damage model, the rule based model FLEMOps+r as well as novel model approaches which are derived using data mining techniques of regression trees and Bayesian networks. The Bayesian network approach to flood loss modelling provides attractive additional information concerning the probability distribution of both model predictions and explaining variables.

Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study.

PubMed

Walker, Martin; Basáñez, María-Gloria; Ouédraogo, André Lin; Hermsen, Cornelus; Bousema, Teun; Churcher, Thomas S

2015-01-16

Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens.

Bayes and empirical Bayes estimators of abundance and density from spatial capture-recapture data

USGS Publications Warehouse

Dorazio, Robert M.

2013-01-01

In capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. In these circumstances spatially explicit capture-recapture (SECR) models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. In this paper I propose two Bayesian SECR models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. In formulating these models I used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. This separation of ecological and observational processes allowed me to derive a formal connection between Bayes and empirical Bayes estimators of population abundance that has not been established previously. I showed that this connection applies to every Poisson point-process model of SECR data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. To illustrate results of both classical and Bayesian methods of analysis, I compared Bayes and empirical Bayes esimates of abundance and density using recaptures from simulated and real populations of animals. Real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. In the datasets I analyzed, classical and Bayesian methods provided similar – and often identical – inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.

Bayes and empirical Bayes estimators of abundance and density from spatial capture-recapture data.

PubMed

Dorazio, Robert M

2013-01-01

In capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. In these circumstances spatially explicit capture-recapture (SECR) models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. In this paper I propose two Bayesian SECR models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. In formulating these models I used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. This separation of ecological and observational processes allowed me to derive a formal connection between Bayes and empirical Bayes estimators of population abundance that has not been established previously. I showed that this connection applies to every Poisson point-process model of SECR data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. To illustrate results of both classical and Bayesian methods of analysis, I compared Bayes and empirical Bayes esimates of abundance and density using recaptures from simulated and real populations of animals. Real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. In the datasets I analyzed, classical and Bayesian methods provided similar - and often identical - inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Sensitivity of Global Methane Bayesian Inversion to Surface Observation Data Sets and Chemical-Transport Model Resolution

NASA Astrophysics Data System (ADS)

Lew, E. J.; Butenhoff, C. L.; Karmakar, S.; Rice, A. L.; Khalil, A. K.

2017-12-01

Methane is the second most important greenhouse gas after carbon dioxide. In efforts to control emissions, a careful examination of the methane budget and source strengths is required. To determine methane surface fluxes, Bayesian methods are often used to provide top-down constraints. Inverse modeling derives unknown fluxes using observed methane concentrations, a chemical transport model (CTM) and prior information. The Bayesian inversion reduces prior flux uncertainties by exploiting information content in the data. While the Bayesian formalism produces internal error estimates of source fluxes, systematic or external errors that arise from user choices in the inversion scheme are often much larger. Here we examine model sensitivity and uncertainty of our inversion under different observation data sets and CTM grid resolution. We compare posterior surface fluxes using the data product GLOBALVIEW-CH4 against the event-level molar mixing ratio data available from NOAA. GLOBALVIEW-CH4 is a collection of CH4 concentration estimates from 221 sites, collected by 12 laboratories, that have been interpolated and extracted to provide weekly records from 1984-2008. Differently, the event-level NOAA data records methane mixing ratios field measurements from 102 sites, containing sampling frequency irregularities and gaps in time. Furthermore, the sampling platform types used by the data sets may influence the posterior flux estimates, namely fixed surface, tower, ship and aircraft sites. To explore the sensitivity of the posterior surface fluxes to the observation network geometry, inversions composed of all sites, only aircraft, only ship, only tower and only fixed surface sites, are performed and compared. Also, we investigate the sensitivity of the error reduction associated with the resolution of the GEOS-Chem simulation (4°×5° vs 2°×2.5°) used to calculate the response matrix. Using a higher resolution grid decreased the model-data error at most sites, thereby increasing the information at that site. These different inversions—event-level and interpolated data, higher and lower resolutions—are compared using an ensemble of descriptive and comparative statistics. Analyzing the sensitivity of the inverse model leads to more accurate estimates of the methane source category uncertainty.

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Fast genomic predictions via Bayesian G-BLUP and multilocus models of threshold traits including censored Gaussian data.

PubMed

Kärkkäinen, Hanni P; Sillanpää, Mikko J

2013-09-04

Because of the increased availability of genome-wide sets of molecular markers along with reduced cost of genotyping large samples of individuals, genomic estimated breeding values have become an essential resource in plant and animal breeding. Bayesian methods for breeding value estimation have proven to be accurate and efficient; however, the ever-increasing data sets are placing heavy demands on the parameter estimation algorithms. Although a commendable number of fast estimation algorithms are available for Bayesian models of continuous Gaussian traits, there is a shortage for corresponding models of discrete or censored phenotypes. In this work, we consider a threshold approach of binary, ordinal, and censored Gaussian observations for Bayesian multilocus association models and Bayesian genomic best linear unbiased prediction and present a high-speed generalized expectation maximization algorithm for parameter estimation under these models. We demonstrate our method with simulated and real data. Our example analyses suggest that the use of the extra information present in an ordered categorical or censored Gaussian data set, instead of dichotomizing the data into case-control observations, increases the accuracy of genomic breeding values predicted by Bayesian multilocus association models or by Bayesian genomic best linear unbiased prediction. Furthermore, the example analyses indicate that the correct threshold model is more accurate than the directly used Gaussian model with a censored Gaussian data, while with a binary or an ordinal data the superiority of the threshold model could not be confirmed.

Fast Genomic Predictions via Bayesian G-BLUP and Multilocus Models of Threshold Traits Including Censored Gaussian Data

PubMed Central

Kärkkäinen, Hanni P.; Sillanpää, Mikko J.

2013-01-01

Because of the increased availability of genome-wide sets of molecular markers along with reduced cost of genotyping large samples of individuals, genomic estimated breeding values have become an essential resource in plant and animal breeding. Bayesian methods for breeding value estimation have proven to be accurate and efficient; however, the ever-increasing data sets are placing heavy demands on the parameter estimation algorithms. Although a commendable number of fast estimation algorithms are available for Bayesian models of continuous Gaussian traits, there is a shortage for corresponding models of discrete or censored phenotypes. In this work, we consider a threshold approach of binary, ordinal, and censored Gaussian observations for Bayesian multilocus association models and Bayesian genomic best linear unbiased prediction and present a high-speed generalized expectation maximization algorithm for parameter estimation under these models. We demonstrate our method with simulated and real data. Our example analyses suggest that the use of the extra information present in an ordered categorical or censored Gaussian data set, instead of dichotomizing the data into case-control observations, increases the accuracy of genomic breeding values predicted by Bayesian multilocus association models or by Bayesian genomic best linear unbiased prediction. Furthermore, the example analyses indicate that the correct threshold model is more accurate than the directly used Gaussian model with a censored Gaussian data, while with a binary or an ordinal data the superiority of the threshold model could not be confirmed. PMID:23821618

APPLICATION OF BAYESIAN MONTE CARLO ANALYSIS TO A LAGRANGIAN PHOTOCHEMICAL AIR QUALITY MODEL. (R824792)

EPA Science Inventory

Uncertainties in ozone concentrations predicted with a Lagrangian photochemical air quality model have been estimated using Bayesian Monte Carlo (BMC) analysis. Bayesian Monte Carlo analysis provides a means of combining subjective "prior" uncertainty estimates developed ...

A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION

EPA Science Inventory

We present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is usefu...

Relationship between haemagglutination-inhibiting antibody titres and clinical protection against influenza: development and application of a bayesian random-effects model.

PubMed

Coudeville, Laurent; Bailleux, Fabrice; Riche, Benjamin; Megas, Françoise; Andre, Philippe; Ecochard, René

2010-03-08

Antibodies directed against haemagglutinin, measured by the haemagglutination inhibition (HI) assay are essential to protective immunity against influenza infection. An HI titre of 1:40 is generally accepted to correspond to a 50% reduction in the risk of contracting influenza in a susceptible population, but limited attempts have been made to further quantify the association between HI titre and protective efficacy. We present a model, using a meta-analytical approach, that estimates the level of clinical protection against influenza at any HI titre level. Source data were derived from a systematic literature review that identified 15 studies, representing a total of 5899 adult subjects and 1304 influenza cases with interval-censored information on HI titre. The parameters of the relationship between HI titre and clinical protection were estimated using Bayesian inference with a consideration of random effects and censorship in the available information. A significant and positive relationship between HI titre and clinical protection against influenza was observed in all tested models. This relationship was found to be similar irrespective of the type of viral strain (A or B) and the vaccination status of the individuals. Although limitations in the data used should not be overlooked, the relationship derived in this analysis provides a means to predict the efficacy of inactivated influenza vaccines when only immunogenicity data are available. This relationship can also be useful for comparing the efficacy of different influenza vaccines based on their immunological profile.

A Bayesian alternative for multi-objective ecohydrological model specification

NASA Astrophysics Data System (ADS)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior distributions in such approaches.

Diagnostic accuracy of a bayesian latent group analysis for the detection of malingering-related poor effort.

PubMed

Ortega, Alonso; Labrenz, Stephan; Markowitsch, Hans J; Piefke, Martina

2013-01-01

In the last decade, different statistical techniques have been introduced to improve assessment of malingering-related poor effort. In this context, we have recently shown preliminary evidence that a Bayesian latent group model may help to optimize classification accuracy using a simulation research design. In the present study, we conducted two analyses. Firstly, we evaluated how accurately this Bayesian approach can distinguish between participants answering in an honest way (honest response group) and participants feigning cognitive impairment (experimental malingering group). Secondly, we tested the accuracy of our model in the differentiation between patients who had real cognitive deficits (cognitively impaired group) and participants who belonged to the experimental malingering group. All Bayesian analyses were conducted using the raw scores of a visual recognition forced-choice task (2AFC), the Test of Memory Malingering (TOMM, Trial 2), and the Word Memory Test (WMT, primary effort subtests). The first analysis showed 100% accuracy for the Bayesian model in distinguishing participants of both groups with all effort measures. The second analysis showed outstanding overall accuracy of the Bayesian model when estimates were obtained from the 2AFC and the TOMM raw scores. Diagnostic accuracy of the Bayesian model diminished when using the WMT total raw scores. Despite, overall diagnostic accuracy can still be considered excellent. The most plausible explanation for this decrement is the low performance in verbal recognition and fluency tasks of some patients of the cognitively impaired group. Additionally, the Bayesian model provides individual estimates, p(zi |D), of examinees' effort levels. In conclusion, both high classification accuracy levels and Bayesian individual estimates of effort may be very useful for clinicians when assessing for effort in medico-legal settings.

Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling

PubMed Central

Boos, Moritz; Seer, Caroline; Lange, Florian; Kopp, Bruno

2016-01-01

Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision. PMID:27303323

Estimating Tree Height-Diameter Models with the Bayesian Method

PubMed Central

Duan, Aiguo; Zhang, Jianguo; Xiang, Congwei

2014-01-01

Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist) approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS) and the maximum likelihood method (ML). The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the “best” model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2. PMID:24711733

Estimating tree height-diameter models with the Bayesian method.

PubMed

Zhang, Xiongqing; Duan, Aiguo; Zhang, Jianguo; Xiang, Congwei

2014-01-01

Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist) approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS) and the maximum likelihood method (ML). The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the "best" model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2.

Energetics of the O-H bond and of intramolecular hydrogen bonding in HOC6H4C(O)Y (Y = H, CH3, CH2CH=CH2, C[triple bond]CH, CH2F, NH2, NHCH3, NO2, OH, OCH3, OCN, CN, F, Cl, SH, and SCH3) compounds.

PubMed

Bernardes, Carlos E S; Minas da Piedade, Manuel E

2008-10-09

The energetics of the phenolic O-H bond in a series of 2- and 4-HOC 6H 4C(O)Y (Y = H, CH3, CH 2CH=CH2, C[triple bond]CH, CH2F, NH2, NHCH 3, NO2, OH, OCH3, OCN, CN, F, Cl, SH, and SCH3) compounds and of the intramolecular O...H hydrogen bond in 2-HOC 6H 4C(O)Y, was investigated by using a combination of experimental and theoretical methods. The standard molar enthalpies of formation of 2-hydroxybenzaldehyde (2HBA), 4-hydroxybenzaldehyde (4HBA), 2'-hydroxyacetophenone (2HAP), 2-hydroxybenzamide (2HBM), and 4-hydroxybenzamide (4HBM), at 298.15 K, were determined by micro- or macrocombustion calorimetry. The corresponding enthalpies of vaporization or sublimation were also measured by Calvet drop-calorimetry and Knudsen effusion measurements. The combination of the obtained experimental data led to Delta f H m (o)(2HBA, g) = -238.3 +/- 2.5 kJ.mol (-1), DeltafHm(o)(4HBA, g) = -220.3 +/- 2.0 kJ.mol(-1), Delta f H m (o)(2HAP, g) = -291.8 +/- 2.1 kJ.mol(-1), DeltafHm(o)(2HBM, g) = -304.8 +/- 1.5 kJ.mol (-1), and DeltafHm(o) (4HBM, g) = -278.4 +/- 2.4 kJ.mol (-1). These values, were used to assess the predictions of the B3LYP/6-31G(d,p), B3LYP/6-311+G(d,p), B3LYP/aug-cc-pVDZ, B3P86/6-31G(d,p), B3P86/6-311+G(d,p), B3P86/aug-cc-pVDZ, and CBS-QB3 methods, for the enthalpies of a series of isodesmic gas phase reactions. In general, the CBS-QB3 method was able to reproduce the experimental enthalpies of reaction within their uncertainties. The B3LYP/6-311+G(d,p) method, with a slightly poorer accuracy than the CBS-QB3 approach, achieved the best performance of the tested DFT models. It was further used to analyze the trends of the intramolecular O...H hydrogen bond in 2-HOC 6H 4C(O)Y evaluated by the ortho-para method and to compare the energetics of the phenolic O-H bond in 2- and 4-HOC 6H 4C(O)Y compounds. It was concluded that the O-H bond "strength" is systematically larger for 2-hydroxybenzoyl than for the corresponding 4-hydroxybenzoyl isomers mainly due to the presence of the intramolecular O...H hydrogen bond in the 2-isomers. The observed differences are, however, significantly dependent on the nature of the substituent Y, in particular, when an intramolecular H-bond can be present in the radical obtained upon cleavage of the O-H bond.

The French human biomonitoring program: First lessons from the perinatal component and future needs.

PubMed

Dereumeaux, Clémentine; Fillol, Clémence; Charles, Marie-Aline; Denys, Sébastien

2017-03-01

This paper presents a progress report of the French human biomonitoring (HBM) program established in 2010. This program has been designed to provide a national representative estimation of the French population's exposure to various environmental chemicals and to study the determinants of exposure. This program currently consists in two surveys: a perinatal component related to a selection of 4145 pregnant women who have been enrolled in the Elfe cohort (the French Longitudinal Study since Childhood) in 2011, and a general population survey related to adults aged 18-74 years and children as from 6 years (Esteban). The aim of this manuscript is to present highlights of the French human biomonitoring program with particular focus on the prioritization of biomarkers to be analyzed in the program and the selection of biomarkers applied to both program components. The Delphi method was used to establish a consensual list of prioritized biomarkers in 2011. First results of the perinatal component of the French HBM program have shown that the biomarkers prioritized were relevant, as almost all pregnant women were exposed to them. However, for some biomarkers, levels' decreases have been observed which may partly be explained by measures taken to prohibit some of these chemicals (e.g. atrazine) and by industrial processes evolutions leading to the substitution of others (e.g. bisphenol A, di-2-ethylhexyl phthalate/DEHP, dialkyl phosphates). Therefore, the list of biomarkers to be monitored in the French HBM program has been implemented to include some substitutes of biomarkers prioritized in the first instance (e.g. bisphenol S, F). Finally, this method combines rigor and flexibility and helped us to build a prioritized list that will be shared and supported by many if not all actors. Copyright © 2016 Elsevier GmbH. All rights reserved.

A new prior for bayesian anomaly detection: application to biosurveillance.

PubMed

Shen, Y; Cooper, G F

2010-01-01

Bayesian anomaly detection computes posterior probabilities of anomalous events by combining prior beliefs and evidence from data. However, the specification of prior probabilities can be challenging. This paper describes a Bayesian prior in the context of disease outbreak detection. The goal is to provide a meaningful, easy-to-use prior that yields a posterior probability of an outbreak that performs at least as well as a standard frequentist approach. If this goal is achieved, the resulting posterior could be usefully incorporated into a decision analysis about how to act in light of a possible disease outbreak. This paper describes a Bayesian method for anomaly detection that combines learning from data with a semi-informative prior probability over patterns of anomalous events. A univariate version of the algorithm is presented here for ease of illustration of the essential ideas. The paper describes the algorithm in the context of disease-outbreak detection, but it is general and can be used in other anomaly detection applications. For this application, the semi-informative prior specifies that an increased count over baseline is expected for the variable being monitored, such as the number of respiratory chief complaints per day at a given emergency department. The semi-informative prior is derived based on the baseline prior, which is estimated from using historical data. The evaluation reported here used semi-synthetic data to evaluate the detection performance of the proposed Bayesian method and a control chart method, which is a standard frequentist algorithm that is closest to the Bayesian method in terms of the type of data it uses. The disease-outbreak detection performance of the Bayesian method was statistically significantly better than that of the control chart method when proper baseline periods were used to estimate the baseline behavior to avoid seasonal effects. When using longer baseline periods, the Bayesian method performed as well as the control chart method. The time complexity of the Bayesian algorithm is linear in the number of the observed events being monitored, due to a novel, closed-form derivation that is introduced in the paper. This paper introduces a novel prior probability for Bayesian outbreak detection that is expressive, easy-to-apply, computationally efficient, and performs as well or better than a standard frequentist method.

A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes.

PubMed

Marini, Simone; Trifoglio, Emanuele; Barbarini, Nicola; Sambo, Francesco; Di Camillo, Barbara; Malovini, Alberto; Manfrini, Marco; Cobelli, Claudio; Bellazzi, Riccardo

2015-10-01

The increasing prevalence of diabetes and its related complications is raising the need for effective methods to predict patient evolution and for stratifying cohorts in terms of risk of developing diabetes-related complications. In this paper, we present a novel approach to the simulation of a type 1 diabetes population, based on Dynamic Bayesian Networks, which combines literature knowledge with data mining of a rich longitudinal cohort of type 1 diabetes patients, the DCCT/EDIC study. In particular, in our approach we simulate the patient health state and complications through discretized variables. Two types of models are presented, one entirely learned from the data and the other partially driven by literature derived knowledge. The whole cohort is simulated for fifteen years, and the simulation error (i.e. for each variable, the percentage of patients predicted in the wrong state) is calculated every year on independent test data. For each variable, the population predicted in the wrong state is below 10% on both models over time. Furthermore, the distributions of real vs. simulated patients greatly overlap. Thus, the proposed models are viable tools to support decision making in type 1 diabetes. Copyright © 2015 Elsevier Inc. All rights reserved.

Variational Gaussian approximation for Poisson data

NASA Astrophysics Data System (ADS)

Arridge, Simon R.; Ito, Kazufumi; Jin, Bangti; Zhang, Chen

2018-02-01

The Poisson model is frequently employed to describe count data, but in a Bayesian context it leads to an analytically intractable posterior probability distribution. In this work, we analyze a variational Gaussian approximation to the posterior distribution arising from the Poisson model with a Gaussian prior. This is achieved by seeking an optimal Gaussian distribution minimizing the Kullback-Leibler divergence from the posterior distribution to the approximation, or equivalently maximizing the lower bound for the model evidence. We derive an explicit expression for the lower bound, and show the existence and uniqueness of the optimal Gaussian approximation. The lower bound functional can be viewed as a variant of classical Tikhonov regularization that penalizes also the covariance. Then we develop an efficient alternating direction maximization algorithm for solving the optimization problem, and analyze its convergence. We discuss strategies for reducing the computational complexity via low rank structure of the forward operator and the sparsity of the covariance. Further, as an application of the lower bound, we discuss hierarchical Bayesian modeling for selecting the hyperparameter in the prior distribution, and propose a monotonically convergent algorithm for determining the hyperparameter. We present extensive numerical experiments to illustrate the Gaussian approximation and the algorithms.

Bayesian multivariate Poisson abundance models for T-cell receptor data.

PubMed

Greene, Joshua; Birtwistle, Marc R; Ignatowicz, Leszek; Rempala, Grzegorz A

2013-06-07

A major feature of an adaptive immune system is its ability to generate B- and T-cell clones capable of recognizing and neutralizing specific antigens. These clones recognize antigens with the help of the surface molecules, called antigen receptors, acquired individually during the clonal development process. In order to ensure a response to a broad range of antigens, the number of different receptor molecules is extremely large, resulting in a huge clonal diversity of both B- and T-cell receptor populations and making their experimental comparisons statistically challenging. To facilitate such comparisons, we propose a flexible parametric model of multivariate count data and illustrate its use in a simultaneous analysis of multiple antigen receptor populations derived from mammalian T-cells. The model relies on a representation of the observed receptor counts as a multivariate Poisson abundance mixture (m PAM). A Bayesian parameter fitting procedure is proposed, based on the complete posterior likelihood, rather than the conditional one used typically in similar settings. The new procedure is shown to be considerably more efficient than its conditional counterpart (as measured by the Fisher information) in the regions of m PAM parameter space relevant to model T-cell data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Bayesian ensemble refinement by replica simulations and reweighting.

PubMed

Hummer, Gerhard; Köfinger, Jürgen

2015-12-28

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Bayesian ensemble refinement by replica simulations and reweighting

NASA Astrophysics Data System (ADS)

Hummer, Gerhard; Köfinger, Jürgen

2015-12-01

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Accurate Biomass Estimation via Bayesian Adaptive Sampling

NASA Technical Reports Server (NTRS)

Wheeler, Kevin R.; Knuth, Kevin H.; Castle, Joseph P.; Lvov, Nikolay

2005-01-01

The following concepts were introduced: a) Bayesian adaptive sampling for solving biomass estimation; b) Characterization of MISR Rahman model parameters conditioned upon MODIS landcover. c) Rigorous non-parametric Bayesian approach to analytic mixture model determination. d) Unique U.S. asset for science product validation and verification.

Embedding the results of focussed Bayesian fusion into a global context

NASA Astrophysics Data System (ADS)

Sander, Jennifer; Heizmann, Michael

2014-05-01

Bayesian statistics offers a well-founded and powerful fusion methodology also for the fusion of heterogeneous information sources. However, except in special cases, the needed posterior distribution is not analytically derivable. As consequence, Bayesian fusion may cause unacceptably high computational and storage costs in practice. Local Bayesian fusion approaches aim at reducing the complexity of the Bayesian fusion methodology significantly. This is done by concentrating the actual Bayesian fusion on the potentially most task relevant parts of the domain of the Properties of Interest. Our research on these approaches is motivated by an analogy to criminal investigations where criminalists pursue clues also only locally. This publication follows previous publications on a special local Bayesian fusion technique called focussed Bayesian fusion. Here, the actual calculation of the posterior distribution gets completely restricted to a suitably chosen local context. By this, the global posterior distribution is not completely determined. Strategies for using the results of a focussed Bayesian analysis appropriately are needed. In this publication, we primarily contrast different ways of embedding the results of focussed Bayesian fusion explicitly into a global context. To obtain a unique global posterior distribution, we analyze the application of the Maximum Entropy Principle that has been shown to be successfully applicable in metrology and in different other areas. To address the special need for making further decisions subsequently to the actual fusion task, we further analyze criteria for decision making under partial information.

Categorizing health-related cues to action: using Yelp reviews of restaurants in Hawaii

NASA Astrophysics Data System (ADS)

Ariyasriwatana, W.; Buente, W.; Oshiro, M.; Streveler, D.

2014-10-01

Yelp, a social media site, undeniably has an influence on consumers' food choice in spite of its ability to reflect consumers' real voice being criticized. Since unhealthy food choices contribute to health problems, such as obesity and malnourishment, we attempted to examine these problems by better understanding consumers through health-related cues to action-a construct from the Health Belief Model (HBM)- on Yelp Honolulu's restaurant reviews. Our research revealed 13 main categories: Ingredient, Type of food, Taste, Lifestyle, Cooking, Option, Price, Portion, Well-being, Nutrition, Hygiene, Emotional attachment and indulgence, and Feeling. We argue that these categories may ultimately lead consumers to make healthier food choices. In search of the most appealing way to communicate with the target group, underlying concepts that derived from these categories can be tested. Marketers in food industry (or public health policy-makers) can craft their strategies for healthy food brands/products (or healthy eating scheme) based on the concept test research. Moreover, Yelp can apply these insights in the development of their algorithm and filter system in order to help consumers find healthy food if they wish to do so. Restaurants can also improve their strategy, menu, and communication execution to meet the growing demands of health conscious consumers.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Da Rio, Nicola; Robberto, Massimo, E-mail: ndario@rssd.esa.int

We present the Tool for Astrophysical Data Analysis (TA-DA), a new software aimed to greatly simplify and improve the analysis of stellar photometric data in comparison with theoretical models, and allow the derivation of stellar parameters from multi-band photometry. Its flexibility allows one to address a number of such problems: from the interpolation of stellar models, or sets of stellar physical parameters in general, to the computation of synthetic photometry in arbitrary filters or units; from the analysis of observed color-magnitude diagrams to a Bayesian derivation of stellar parameters (and extinction) based on multi-band data. TA-DA is available as amore » pre-compiled Interactive Data Language widget-based application; its graphical user interface makes it considerably user-friendly. In this paper, we describe the software and its functionalities.« less

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

DTIC Science & Technology

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model , is able to model the rate of occurrence of...which adds specificity to the model and can make nonlinear data more manageable. Early results show that the 1. REPORT DATE (DD-MM-YYYY) 4. TITLE

Bayesian generalized linear mixed modeling of Tuberculosis using informative priors

PubMed Central

Woldegerima, Woldegebriel Assefa

2017-01-01

TB is rated as one of the world’s deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014. PMID:28257437

Unraveling multiple changes in complex climate time series using Bayesian inference

NASA Astrophysics Data System (ADS)

Berner, Nadine; Trauth, Martin H.; Holschneider, Matthias

2016-04-01

Change points in time series are perceived as heterogeneities in the statistical or dynamical characteristics of observations. Unraveling such transitions yields essential information for the understanding of the observed system. The precise detection and basic characterization of underlying changes is therefore of particular importance in environmental sciences. We present a kernel-based Bayesian inference approach to investigate direct as well as indirect climate observations for multiple generic transition events. In order to develop a diagnostic approach designed to capture a variety of natural processes, the basic statistical features of central tendency and dispersion are used to locally approximate a complex time series by a generic transition model. A Bayesian inversion approach is developed to robustly infer on the location and the generic patterns of such a transition. To systematically investigate time series for multiple changes occurring at different temporal scales, the Bayesian inversion is extended to a kernel-based inference approach. By introducing basic kernel measures, the kernel inference results are composed into a proxy probability to a posterior distribution of multiple transitions. Thus, based on a generic transition model a probability expression is derived that is capable to indicate multiple changes within a complex time series. We discuss the method's performance by investigating direct and indirect climate observations. The approach is applied to environmental time series (about 100 a), from the weather station in Tuscaloosa, Alabama, and confirms documented instrumentation changes. Moreover, the approach is used to investigate a set of complex terrigenous dust records from the ODP sites 659, 721/722 and 967 interpreted as climate indicators of the African region of the Plio-Pleistocene period (about 5 Ma). The detailed inference unravels multiple transitions underlying the indirect climate observations coinciding with established global climate events.

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.

The Bayesian Revolution Approaches Psychological Development

ERIC Educational Resources Information Center

Shultz, Thomas R.

2007-01-01

This commentary reviews five articles that apply Bayesian ideas to psychological development, some with psychology experiments, some with computational modeling, and some with both experiments and modeling. The reviewed work extends the current Bayesian revolution into tasks often studied in children, such as causal learning and word learning, and…

Incorporating approximation error in surrogate based Bayesian inversion

NASA Astrophysics Data System (ADS)

Zhang, J.; Zeng, L.; Li, W.; Wu, L.

2015-12-01

There are increasing interests in applying surrogates for inverse Bayesian modeling to reduce repetitive evaluations of original model. In this way, the computational cost is expected to be saved. However, the approximation error of surrogate model is usually overlooked. This is partly because that it is difficult to evaluate the approximation error for many surrogates. Previous studies have shown that, the direct combination of surrogates and Bayesian methods (e.g., Markov Chain Monte Carlo, MCMC) may lead to biased estimations when the surrogate cannot emulate the highly nonlinear original system. This problem can be alleviated by implementing MCMC in a two-stage manner. However, the computational cost is still high since a relatively large number of original model simulations are required. In this study, we illustrate the importance of incorporating approximation error in inverse Bayesian modeling. Gaussian process (GP) is chosen to construct the surrogate for its convenience in approximation error evaluation. Numerical cases of Bayesian experimental design and parameter estimation for contaminant source identification are used to illustrate this idea. It is shown that, once the surrogate approximation error is well incorporated into Bayesian framework, promising results can be obtained even when the surrogate is directly used, and no further original model simulations are required.

Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods.

PubMed

Rodgers, Joseph Lee

2016-01-01

The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.

A Bayesian Model of the Memory Colour Effect.

PubMed

Witzel, Christoph; Olkkonen, Maria; Gegenfurtner, Karl R

2018-01-01

According to the memory colour effect, the colour of a colour-diagnostic object is not perceived independently of the object itself. Instead, it has been shown through an achromatic adjustment method that colour-diagnostic objects still appear slightly in their typical colour, even when they are colourimetrically grey. Bayesian models provide a promising approach to capture the effect of prior knowledge on colour perception and to link these effects to more general effects of cue integration. Here, we model memory colour effects using prior knowledge about typical colours as priors for the grey adjustments in a Bayesian model. This simple model does not involve any fitting of free parameters. The Bayesian model roughly captured the magnitude of the measured memory colour effect for photographs of objects. To some extent, the model predicted observed differences in memory colour effects across objects. The model could not account for the differences in memory colour effects across different levels of realism in the object images. The Bayesian model provides a particularly simple account of memory colour effects, capturing some of the multiple sources of variation of these effects.

A Bayesian Model of the Memory Colour Effect

PubMed Central

Olkkonen, Maria; Gegenfurtner, Karl R.

2018-01-01

According to the memory colour effect, the colour of a colour-diagnostic object is not perceived independently of the object itself. Instead, it has been shown through an achromatic adjustment method that colour-diagnostic objects still appear slightly in their typical colour, even when they are colourimetrically grey. Bayesian models provide a promising approach to capture the effect of prior knowledge on colour perception and to link these effects to more general effects of cue integration. Here, we model memory colour effects using prior knowledge about typical colours as priors for the grey adjustments in a Bayesian model. This simple model does not involve any fitting of free parameters. The Bayesian model roughly captured the magnitude of the measured memory colour effect for photographs of objects. To some extent, the model predicted observed differences in memory colour effects across objects. The model could not account for the differences in memory colour effects across different levels of realism in the object images. The Bayesian model provides a particularly simple account of memory colour effects, capturing some of the multiple sources of variation of these effects. PMID:29760874

REMOVING BIASES IN RESOLVED STELLAR MASS MAPS OF GALAXY DISKS THROUGH SUCCESSIVE BAYESIAN MARGINALIZATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martínez-García, Eric E.; González-Lópezlira, Rosa A.; Bruzual A, Gustavo

2017-01-20

Stellar masses of galaxies are frequently obtained by fitting stellar population synthesis models to galaxy photometry or spectra. The state of the art method resolves spatial structures within a galaxy to assess the total stellar mass content. In comparison to unresolved studies, resolved methods yield, on average, higher fractions of stellar mass for galaxies. In this work we improve the current method in order to mitigate a bias related to the resolved spatial distribution derived for the mass. The bias consists in an apparent filamentary mass distribution and a spatial coincidence between mass structures and dust lanes near spiral arms.more » The improved method is based on iterative Bayesian marginalization, through a new algorithm we have named Bayesian Successive Priors (BSP). We have applied BSP to M51 and to a pilot sample of 90 spiral galaxies from the Ohio State University Bright Spiral Galaxy Survey. By quantitatively comparing both methods, we find that the average fraction of stellar mass missed by unresolved studies is only half what previously thought. In contrast with the previous method, the output BSP mass maps bear a better resemblance to near-infrared images.« less

Using Bayesian Networks to Improve Knowledge Assessment

ERIC Educational Resources Information Center

Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra

2013-01-01

In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…

Bayesian Posterior Odds Ratios: Statistical Tools for Collaborative Evaluations

ERIC Educational Resources Information Center

Hicks, Tyler; Rodríguez-Campos, Liliana; Choi, Jeong Hoon

2018-01-01

To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices…

ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability.

PubMed

Liu, Zhihong; Zheng, Minghao; Yan, Xin; Gu, Qiong; Gasteiger, Johann; Tijhuis, Johan; Maas, Peter; Li, Jiabo; Xu, Jun

2014-09-01

Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p(s)) and an unstable probability (p(uns)). 13,340 ACFs, together with their p(s) and p(uns) data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p(s) and p(uns) values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes' theorem, based upon the p(s) and p(uns) values of the compound ACFs. We were able to achieve performance with an AUC value of 84% and a tenfold cross validation accuracy of 76.5%. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.

Fractional viscoelasticity in fractal and non-fractal media: Theory, experimental validation, and uncertainty analysis

NASA Astrophysics Data System (ADS)

Mashayekhi, Somayeh; Miles, Paul; Hussaini, M. Yousuff; Oates, William S.

2018-02-01

In this paper, fractional and non-fractional viscoelastic models for elastomeric materials are derived and analyzed in comparison to experimental results. The viscoelastic models are derived by expanding thermodynamic balance equations for both fractal and non-fractal media. The order of the fractional time derivative is shown to strongly affect the accuracy of the viscoelastic constitutive predictions. Model validation uses experimental data describing viscoelasticity of the dielectric elastomer Very High Bond (VHB) 4910. Since these materials are known for their broad applications in smart structures, it is important to characterize and accurately predict their behavior across a large range of time scales. Whereas integer order viscoelastic models can yield reasonable agreement with data, the model parameters often lack robustness in prediction at different deformation rates. Alternatively, fractional order models of viscoelasticity provide an alternative framework to more accurately quantify complex rate-dependent behavior. Prior research that has considered fractional order viscoelasticity lacks experimental validation and contains limited links between viscoelastic theory and fractional order derivatives. To address these issues, we use fractional order operators to experimentally validate fractional and non-fractional viscoelastic models in elastomeric solids using Bayesian uncertainty quantification. The fractional order model is found to be advantageous as predictions are significantly more accurate than integer order viscoelastic models for deformation rates spanning four orders of magnitude.

Improved Determination of the Myelin Water Fraction in Human Brain using Magnetic Resonance Imaging through Bayesian Analysis of mcDESPOT

PubMed Central

Bouhrara, Mustapha; Spencer, Richard G.

2015-01-01

Myelin water fraction (MWF) mapping with magnetic resonance imaging has led to the ability to directly observe myelination and demyelination in both the developing brain and in disease. Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry and has been applied to map MWF in human brain. However, even for the simplest two-pool signal model consisting of MWF and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNR), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. In this study, we introduce three Bayesian approaches for analysis of the mcDESPOT signal model to determine MWF. Given the high dimensional nature of mcDESPOT signal model, and, thereby, the high dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of MWF parameter, the introduced Bayesian analyses use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis of in-vivo human brain datasets exhibiting a range of SNR and spatial resolution, we demonstrated the markedly improved accuracy and precision in the estimation of MWF using these Bayesian methods as compared to the stochastic region contraction (SRC) implementation of NLLS. PMID:26499810

Electrical conductivity of the Earth's mantle from the first Swarm magnetic field measurements

NASA Astrophysics Data System (ADS)

Civet, F.; Thébault, E.; Verhoeven, O.; Langlais, B.; Saturnino, D.

2015-05-01

We present a 1-D electrical conductivity profile of the Earth's mantle down to 2000 km derived from L1b Swarm satellite magnetic field measurements from November 2013 to September 2014. We first derive a model for the main magnetic field, correct the data for a lithospheric field model, and additionally select the data to reduce the contributions of the ionospheric field. We then model the primary and induced magnetospheric fields for periods between 2 and 256 days and perform a Bayesian inversion to obtain the probability density function for the electrical conductivity as function of depth. The conductivity increases by 3 orders of magnitude in the 400-900 km depth range. Assuming a pyrolitic mantle composition, this profile is interpreted in terms of temperature variations leading to a temperature gradient in the lower mantle that is close to adiabatic.

BCM: toolkit for Bayesian analysis of Computational Models using samplers.

PubMed

Thijssen, Bram; Dijkstra, Tjeerd M H; Heskes, Tom; Wessels, Lodewyk F A

2016-10-21

Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model. We present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved. BCM represents an efficient one-stop-shop for computational modelers wishing to use sampler-based Bayesian statistics.

Simulation of an ensemble of future climate time series with an hourly weather generator

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.

2010-12-01

There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous downscaling techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic downscaling technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the downscaling procedure derives distributions of factors of change for several climate statistics from a multi-model ensemble of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model ensemble. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an ensemble of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated ensemble of scenarios also accounts for the uncertainty derived from multiple GCMs used in downscaling. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ), Detroit (MI), and Firenze (Italy). The stochastic downscaling is carried out with eight GCMs from the CMIP3 multi-model dataset (IPCC 4AR, A1B scenario).

Two Approaches to Calibration in Metrology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campanelli, Mark

2014-04-01

Inferring mathematical relationships with quantified uncertainty from measurement data is common to computational science and metrology. Sufficient knowledge of measurement process noise enables Bayesian inference. Otherwise, an alternative approach is required, here termed compartmentalized inference, because collection of uncertain data and model inference occur independently. Bayesian parameterized model inference is compared to a Bayesian-compatible compartmentalized approach for ISO-GUM compliant calibration problems in renewable energy metrology. In either approach, model evidence can help reduce model discrepancy.

Bayesian data analysis in population ecology: motivations, methods, and benefits

USGS Publications Warehouse

Dorazio, Robert

2016-01-01

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

PubMed

Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L

2017-03-10

To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.

A Preliminary Bayesian Analysis of Incomplete Longitudinal Data from a Small Sample: Methodological Advances in an International Comparative Study of Educational Inequality

ERIC Educational Resources Information Center

Hsieh, Chueh-An; Maier, Kimberly S.

2009-01-01

The capacity of Bayesian methods in estimating complex statistical models is undeniable. Bayesian data analysis is seen as having a range of advantages, such as an intuitive probabilistic interpretation of the parameters of interest, the efficient incorporation of prior information to empirical data analysis, model averaging and model selection.…

Estimating thermal performance curves from repeated field observations

USGS Publications Warehouse

Childress, Evan; Letcher, Benjamin H.

2017-01-01

Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.

Do marginalized neighbourhoods have less healthy retail food environments? An analysis using Bayesian spatial latent factor and hurdle models.

PubMed

Luan, Hui; Minaker, Leia M; Law, Jane

2016-08-22

Findings of whether marginalized neighbourhoods have less healthy retail food environments (RFE) are mixed across countries, in part because inconsistent approaches have been used to characterize RFE 'healthfulness' and marginalization, and researchers have used non-spatial statistical methods to respond to this ultimately spatial issue. This study uses in-store features to categorize healthy and less healthy food outlets. Bayesian spatial hierarchical models are applied to explore the association between marginalization dimensions and RFE healthfulness (i.e., relative healthy food access that modelled via a probability distribution) at various geographical scales. Marginalization dimensions are derived from a spatial latent factor model. Zero-inflation occurring at the walkable-distance scale is accounted for with a spatial hurdle model. Neighbourhoods with higher residential instability, material deprivation, and population density are more likely to have access to healthy food outlets within a walkable distance from a binary 'have' or 'not have' access perspective. At the walkable distance scale however, materially deprived neighbourhoods are found to have less healthy RFE (lower relative healthy food access). Food intervention programs should be developed for striking the balance between healthy and less healthy food access in the study region as well as improving opportunities for residents to buy and consume foods consistent with dietary recommendations.

Dynamic Bayesian network modeling for longitudinal brain morphometry

PubMed Central

Chen, Rong; Resnick, Susan M; Davatzikos, Christos; Herskovits, Edward H

2011-01-01

Identifying interactions among brain regions from structural magnetic-resonance images presents one of the major challenges in computational neuroanatomy. We propose a Bayesian data-mining approach to the detection of longitudinal morphological changes in the human brain. Our method uses a dynamic Bayesian network to represent evolving inter-regional dependencies. The major advantage of dynamic Bayesian network modeling is that it can represent complicated interactions among temporal processes. We validated our approach by analyzing a simulated atrophy study, and found that this approach requires only a small number of samples to detect the ground-truth temporal model. We further applied dynamic Bayesian network modeling to a longitudinal study of normal aging and mild cognitive impairment — the Baltimore Longitudinal Study of Aging. We found that interactions among regional volume-change rates for the mild cognitive impairment group are different from those for the normal-aging group. PMID:21963916

Variational learning and bits-back coding: an information-theoretic view to Bayesian learning.

PubMed

Honkela, Antti; Valpola, Harri

2004-07-01

The bits-back coding first introduced by Wallace in 1990 and later by Hinton and van Camp in 1993 provides an interesting link between Bayesian learning and information-theoretic minimum-description-length (MDL) learning approaches. The bits-back coding allows interpreting the cost function used in the variational Bayesian method called ensemble learning as a code length in addition to the Bayesian view of misfit of the posterior approximation and a lower bound of model evidence. Combining these two viewpoints provides interesting insights to the learning process and the functions of different parts of the model. In this paper, the problem of variational Bayesian learning of hierarchical latent variable models is used to demonstrate the benefits of the two views. The code-length interpretation provides new views to many parts of the problem such as model comparison and pruning and helps explain many phenomena occurring in learning.