Improved Online Support Vector Machines Spam Filtering Using String Kernels

NASA Astrophysics Data System (ADS)

Amayri, Ola; Bouguila, Nizar

A major bottleneck in electronic communications is the enormous dissemination of spam emails. Developing of suitable filters that can adequately capture those emails and achieve high performance rate become a main concern. Support vector machines (SVMs) have made a large contribution to the development of spam email filtering. Based on SVMs, the crucial problems in email classification are feature mapping of input emails and the choice of the kernels. In this paper, we present thorough investigation of several distance-based kernels and propose the use of string kernels and prove its efficiency in blocking spam emails. We detail a feature mapping variants in text classification (TC) that yield improved performance for the standard SVMs in filtering task. Furthermore, to cope for realtime scenarios we propose an online active framework for spam filtering.

Information Assurance: Detection & Response to Web Spam Attacks

DTIC Science & Technology

2010-08-28

such as blogs, social bookmarking ( folksonomies ), and wikis continue to gain its popularity, concerns about the rapid proliferation of Web spam has...Attacks Report Title ABSTRACT As online social media applications such as blogs, social bookmarking ( folksonomies ), and wikis continue to gain its... folksonomies ), and wikis continue to gain its popularity, concerns about the rapid proliferation of Web spam has grown in recent years. These applications

Analysis of an Anti-Phishing Lab Activity

ERIC Educational Resources Information Center

Werner, Laurie A.; Courte, Jill

2010-01-01

Despite advances in spam detection software, anti-spam laws, and increasingly sophisticated users, the number of successful phishing scams continues to grow. In addition to monetary losses attributable to phishing, there is also a loss of confidence that stifles use of online services. Using in-class activities in an introductory computer course…

Clusterin Facilitates Exchange of Glycosyl Phosphatidylinositol-Linked SPAM1 Between Reproductive Luminal Fluids and Mouse and Human Sperm Membranes1

PubMed Central

Griffiths, Genevieve S.; Galileo, Deni S.; Aravindan, Rolands G.; Martin-DeLeon, Patricia A.

2009-01-01

Glycosyl phosphatidylinositol (GPI)-linked proteins, which are involved in post-testicular maturation of sperm and have a role in fertilization, are acquired on the sperm surface from both vesicular and membrane-free soluble fractions of epididymal luminal fluid (LF) and uterine LF. Herein, we investigate the mechanism of uptake of these proteins from the soluble fraction of LFs using sperm adhesion molecule 1 (SPAM1) as a model. Ultracentrifugation and native Western blot analysis of the soluble fraction revealed that SPAM1 is present in low-molecular-weight (monomeric) and high-molecular-weight (oligomeric) complexes. The latter are incapable of transferring SPAM1 and may serve to produce monomers. Monomers are stabilized by hydrophobic interactions with clusterin (CLU), a lipid carrier that is abundantly expressed in LFs. We show that CLU is involved in the transfer of SPAM1 monomers, whose delivery was decreased by anti-CLU antibody under normal and apolipoprotein-enhanced conditions. Coimmunoprecipitation revealed an intimate association of CLU with SPAM1. Both plasma and recombinant CLU had a dose-related effect on transfer efficiency: high concentrations reduced and low concentrations enhanced delivery of SPAM1 to human and mouse sperm membranes, reflecting physiological states in the epididymal tract. We propose a lipid exchange model (akin to the lipid-poor model for cholesterol efflux) for the delivery of GPI-linked proteins to sperm membranes via CLU. Our investigation defines specific conditions for membrane-free GPI-linked protein transfer in vitro and could lead to technology for improving fertility or treating sperm pathology by the addition of relevant GPI-linked proteins critical for successful fertilization in humans and domestic animals. PMID:19357365

SPONGY (SPam ONtoloGY): Email Classification Using Two-Level Dynamic Ontology

PubMed Central

2014-01-01

Email is one of common communication methods between people on the Internet. However, the increase of email misuse/abuse has resulted in an increasing volume of spam emails over recent years. An experimental system has been designed and implemented with the hypothesis that this method would outperform existing techniques, and the experimental results showed that indeed the proposed ontology-based approach improves spam filtering accuracy significantly. In this paper, two levels of ontology spam filters were implemented: a first level global ontology filter and a second level user-customized ontology filter. The use of the global ontology filter showed about 91% of spam filtered, which is comparable with other methods. The user-customized ontology filter was created based on the specific user's background as well as the filtering mechanism used in the global ontology filter creation. The main contributions of the paper are (1) to introduce an ontology-based multilevel filtering technique that uses both a global ontology and an individual filter for each user to increase spam filtering accuracy and (2) to create a spam filter in the form of ontology, which is user-customized, scalable, and modularized, so that it can be embedded to many other systems for better performance. PMID:25254240

SPONGY (SPam ONtoloGY): email classification using two-level dynamic ontology.

PubMed

Youn, Seongwook

2014-01-01

Email is one of common communication methods between people on the Internet. However, the increase of email misuse/abuse has resulted in an increasing volume of spam emails over recent years. An experimental system has been designed and implemented with the hypothesis that this method would outperform existing techniques, and the experimental results showed that indeed the proposed ontology-based approach improves spam filtering accuracy significantly. In this paper, two levels of ontology spam filters were implemented: a first level global ontology filter and a second level user-customized ontology filter. The use of the global ontology filter showed about 91% of spam filtered, which is comparable with other methods. The user-customized ontology filter was created based on the specific user's background as well as the filtering mechanism used in the global ontology filter creation. The main contributions of the paper are (1) to introduce an ontology-based multilevel filtering technique that uses both a global ontology and an individual filter for each user to increase spam filtering accuracy and (2) to create a spam filter in the form of ontology, which is user-customized, scalable, and modularized, so that it can be embedded to many other systems for better performance.

Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery.

PubMed

Fang, Jiansong; Yang, Ranyao; Gao, Li; Zhou, Dan; Yang, Shengqian; Liu, Ai-Lin; Du, Guan-hua

2013-11-25

Butyrylcholinesterase (BuChE, EC 3.1.1.8) is an important pharmacological target for Alzheimer's disease (AD) treatment. However, the currently available BuChE inhibitor screening assays are expensive, labor-intensive, and compound-dependent. It is necessary to develop robust in silico methods to predict the activities of BuChE inhibitors for the lead identification. In this investigation, support vector machine (SVM) models and naive Bayesian models were built to discriminate BuChE inhibitors (BuChEIs) from the noninhibitors. Each molecule was initially represented in 1870 structural descriptors (1235 from ADRIANA.Code, 334 from MOE, and 301 from Discovery studio). Correlation analysis and stepwise variable selection method were applied to figure out activity-related descriptors for prediction models. Additionally, structural fingerprint descriptors were added to improve the predictive ability of models, which were measured by cross-validation, a test set validation with 1001 compounds and an external test set validation with 317 diverse chemicals. The best two models gave Matthews correlation coefficient of 0.9551 and 0.9550 for the test set and 0.9132 and 0.9221 for the external test set. To demonstrate the practical applicability of the models in virtual screening, we screened an in-house data set with 3601 compounds, and 30 compounds were selected for further bioactivity assay. The assay results showed that 10 out of 30 compounds exerted significant BuChE inhibitory activities with IC50 values ranging from 0.32 to 22.22 μM, at which three new scaffolds as BuChE inhibitors were identified for the first time. To our best knowledge, this is the first report on BuChE inhibitors using machine learning approaches. The models generated from SVM and naive Bayesian approaches successfully predicted BuChE inhibitors. The study proved the feasibility of a new method for predicting bioactivities of ligands and discovering novel lead compounds.

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.

Detecting spam comments on Indonesia’s Instagram posts

NASA Astrophysics Data System (ADS)

Septiandri, Ali Akbar; Wibisono, Okiriza

2017-01-01

In this paper we experimented with several feature sets for detecting spam comments in social media contents authored by Indonesian public figures. We define spam comments as comments which have promotional purposes (e.g. referring other users to products and services) and thus not related to the content to which the comments are posted. Three sets of features are evaluated for detecting spams: (1) hand-engineered features such as comment length, number of capital letters, and number of emojis, (2) keyword features such as whether the comment contains advertising words or product-related words, and (3) text features, namely, bag-of-words, TF-IDF, and fastText embeddings, each combined with latent semantic analysis. With 24,000 manually-annotated comments scraped from Instagram posts authored by more than 100 Indonesian public figures, we compared the performance of these feature sets and their combinations using 3 popular classification algorithms: Na¨ıve Bayes, SVM, and XGBoost. We find that using all three feature sets (with fastText embedding for the text features) gave the best F 1-score of 0.9601 on a holdout dataset. More interestingly, fastText embedding combined with hand-engineered features (i.e. without keyword features) yield similar F 1-score of 0.9523, and McNemar’s test failed to reject the hypothesis that the two results are not significantly different. This result is important as keyword features are largely dependent on the dataset and may not be as generalisable as the other feature sets when applied to new data. For future work, we hope to collect bigger and more diverse dataset of Indonesian spam comments, improve our model’s performance and generalisability, and publish a programming package for others to reliably detect spam comments.

We read spam a lot: prospective cohort study of unsolicited and unwanted academic invitations

PubMed Central

Bolland, Mark J; Dalbeth, Nicola; Gamble, Greg; Sadler, Lynn

2016-01-01

Objectives To assess the amount, relevance, content, and suppressibility of academic electronic spam invitations to attend conferences or submit manuscripts. Design Prospective cohort study. Setting Email accounts of participating academics. Participants Five intrepid academics and a great many publishers, editors, and conference organisers. Intervention Unsubscribing from sender’s distribution lists. Main outcome measures Number of spam invitations received before, immediately after, and one year after unsubscribing from senders’ distribution lists. The proportion of duplicate invitations was also assessed and the relevance of each invitation graded to the recipient’s research interests. A qualitative assessment of the content of spam invitations was conducted. Results At baseline, recipients received an average of 312 spam invitations each month. Unsubscribing reduced the frequency of the invitations by 39% after one month but by only 19% after one year. Overall, 16% of spam invitations were duplicates and 83% had little or no relevance to the recipients’ research interests. Spam invitations were characterised by inventive language, flattery, and exuberance, and they were sometimes baffling and amusing. Conclusions Academic spam is common, repetitive, often irrelevant, and difficult to avoid or prevent. PMID:27974354

An efficient incremental learning mechanism for tracking concept drift in spam filtering

PubMed Central

Sheu, Jyh-Jian; Chu, Ko-Tsung; Li, Nien-Feng; Lee, Cheng-Chi

2017-01-01

This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email’s header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1) Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email’s content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2) Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3) We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment. PMID:28182691

Detecting opinion spams through supervised boosting approach.

PubMed

Hazim, Mohamad; Anuar, Nor Badrul; Ab Razak, Mohd Faizal; Abdullah, Nor Aniza

2018-01-01

Product reviews are the individual's opinions, judgement or belief about a certain product or service provided by certain companies. Such reviews serve as guides for these companies to plan and monitor their business ventures in terms of increasing productivity or enhancing their product/service qualities. Product reviews can also increase business profits by convincing future customers about the products which they have interest in. In the mobile application marketplace such as Google Playstore, reviews and star ratings are used as indicators of the application quality. However, among all these reviews, hereby also known as opinions, spams also exist, to disrupt the online business balance. Previous studies used the time series and neural network approach (which require a lot of computational power) to detect these opinion spams. However, the detection performance can be restricted in terms of accuracy because the approach focusses on basic, discrete and document level features only thereby, projecting little statistical relationships. Aiming to improve the detection of opinion spams in mobile application marketplace, this study proposes using statistical based features that are modelled through the supervised boosting approach such as the Extreme Gradient Boost (XGBoost) and the Generalized Boosted Regression Model (GBM) to evaluate two multilingual datasets (i.e. English and Malay language). From the evaluation done, it was found that the XGBoost is most suitable for detecting opinion spams in the English dataset while the GBM Gaussian is most suitable for the Malay dataset. The comparative analysis also indicates that the implementation of the proposed statistical based features had achieved a detection accuracy rate of 87.43 per cent on the English dataset and 86.13 per cent on the Malay dataset.

Weight problems and spam e-mail for weight loss products.

PubMed

Fogel, Joshua; Shlivko, Sam

2010-01-01

This study focuses on young adult behaviors with regard to spam e-mails that sell weight loss products. Participants (N = 200) with and without weight problems were asked if they received, opened, and bought products from spam e-mail about weight loss topics in the past year. Psychological factors of self-esteem and perceived stress were measured. Those with weight problems had significantly greater percentages than those without weight problems for receiving (87.7% vs. 73.3%, P = 0.02), opening (41.5% vs. 17.8%, P <0.001), and buying products (18.5% vs. 5.2%, P = 0.003). In the multivariate logistic regression analyses, weight problems were significantly associated with receiving (OR: 3.39, 95% CI: 1.31, 8.82), opening (OR: 3.10, 95% CI: 1.53, 6.29), and buying products (OR: 3.38, 95% CI: 1.16, 9.82). Physicians should consider discussing with patients the potential risks of opening and/or purchasing weight loss products from spam e-mails.

Series Pneumatic Artificial Muscles (sPAMs) and Application to a Soft Continuum Robot.

PubMed

Greer, Joseph D; Morimoto, Tania K; Okamura, Allison M; Hawkes, Elliot W

2017-01-01

We describe a new series pneumatic artificial muscle (sPAM) and its application as an actuator for a soft continuum robot. The robot consists of three sPAMs arranged radially round a tubular pneumatic backbone. Analogous to tendons, the sPAMs exert a tension force on the robot's pneumatic backbone, causing bending that is approximately constant curvature. Unlike a traditional tendon driven continuum robot, the robot is entirely soft and contains no hard components, making it safer for human interaction. Models of both the sPAM and soft continuum robot kinematics are presented and experimentally verified. We found a mean position accuracy of 5.5 cm for predicting the end-effector position of a 42 cm long robot with the kinematic model. Finally, closed-loop control is demonstrated using an eye-in-hand visual servo control law which provides a simple interface for operation by a human. The soft continuum robot with closed-loop control was found to have a step-response rise time and settling time of less than two seconds.

Series Pneumatic Artificial Muscles (sPAMs) and Application to a Soft Continuum Robot

PubMed Central

Greer, Joseph D.; Morimoto, Tania K.; Okamura, Allison M.; Hawkes, Elliot W.

2017-01-01

We describe a new series pneumatic artificial muscle (sPAM) and its application as an actuator for a soft continuum robot. The robot consists of three sPAMs arranged radially round a tubular pneumatic backbone. Analogous to tendons, the sPAMs exert a tension force on the robot’s pneumatic backbone, causing bending that is approximately constant curvature. Unlike a traditional tendon driven continuum robot, the robot is entirely soft and contains no hard components, making it safer for human interaction. Models of both the sPAM and soft continuum robot kinematics are presented and experimentally verified. We found a mean position accuracy of 5.5 cm for predicting the end-effector position of a 42 cm long robot with the kinematic model. Finally, closed-loop control is demonstrated using an eye-in-hand visual servo control law which provides a simple interface for operation by a human. The soft continuum robot with closed-loop control was found to have a step-response rise time and settling time of less than two seconds. PMID:29379672

Computing a Comprehensible Model for Spam Filtering

NASA Astrophysics Data System (ADS)

Ruiz-Sepúlveda, Amparo; Triviño-Rodriguez, José L.; Morales-Bueno, Rafael

In this paper, we describe the application of the Desicion Tree Boosting (DTB) learning model to spam email filtering.This classification task implies the learning in a high dimensional feature space. So, it is an example of how the DTB algorithm performs in such feature space problems. In [1], it has been shown that hypotheses computed by the DTB model are more comprehensible that the ones computed by another ensemble methods. Hence, this paper tries to show that the DTB algorithm maintains the same comprehensibility of hypothesis in high dimensional feature space problems while achieving the performance of other ensemble methods. Four traditional evaluation measures (precision, recall, F1 and accuracy) have been considered for performance comparison between DTB and others models usually applied to spam email filtering. The size of the hypothesis computed by a DTB is smaller and more comprehensible than the hypothesis computed by Adaboost and Naïve Bayes.

Impact of the Data Collection on Adverse Events of Anti-HIV Drugs cohort study on abacavir prescription among treatment-naive, HIV-infected patients in Canada.

PubMed

Antoniou, Tony; Gillis, Jennifer; Loutfy, Mona R; Cooper, Curtis; Hogg, Robert S; Klein, Marina B; Machouf, Nima; Montaner, Julio S G; Rourke, Sean B; Tsoukas, Chris; Raboud, Janet M

2014-01-01

To evaluate the trends in abacavir (ABC) prescription among antiretroviral (ARV) medication-naive individuals following the presentation of the Data Collection on Adverse Events of Anti-HIV Drugs (DAD) cohort study. We conducted a retrospective cohort study of ARV medication-naive individuals in the Canadian Observational Cohort (CANOC). Between January 1, 2000, and February 28, 2010, a total of 7280 ARV medication-naive patients were included in CANOC. We observed a significant change in the proportion of new ABC prescriptions immediately following the release of DAD (-11%; 95% confidence interval [CI]: -20% to -2.4%) and in the months following the presentation of these data (-0.66% per month; 95% CI: -1.2% to -0.073%). A post-DAD presentation decrease in the odds of being prescribed ABC versus tenofovir (TDF) was observed (adjusted odds ratio, 0.72 per year, 95% CI: 0.54-0.97). Presentation of the DAD was associated with a significant decrease in ABC use among ARV medication-naive, HIV-positive patients initiating therapy.

Collection efficiency of the soot-particle aerosol mass spectrometer (SP-AMS) for internally mixed particulate black carbon

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

Willis, M. D.; Lee, A. K. Y.; Onasch, T. B.

The soot-particle aerosol mass spectrometer (SP-AMS) uses an intra-cavity infrared laser to vaporize refractory black carbon (rBC) containing particles, making the particle beam–laser beam overlap critical in determining the collection efficiency (CE) for rBC and associated non-refractory particulate matter (NR-PM). This work evaluates the ability of the SP-AMS to quantify rBC and NR-PM mass in internally mixed particles with different thicknesses of organic coating. Using apparent relative ionization efficiencies for uncoated and thickly coated rBC particles, we report measurements of SP-AMS sensitivity to NR-PM and rBC, for Regal Black, the recommended particulate calibration material. Beam width probe (BWP) measurements aremore » used to illustrate an increase in sensitivity for highly coated particles due to narrowing of the particle beam, which enhances the CE of the SP-AMS by increasing the laser beam–particle beam overlap. Assuming complete overlap for thick coatings, we estimate CE for bare Regal Black particles of 0.6 ± 0.1, which suggests that previously measured SP-AMS sensitivities to Regal Black were underestimated by up to a factor of 2. The efficacy of the BWP measurements is highlighted by studies at a busy road in downtown Toronto and at a non-roadside location, which show particle beam widths similar to, but greater than that of bare Regal Black and coated Regal Black, respectively. Further BWP measurements at field locations will help to constrain the range of CE for fresh and aged rBC-containing particles. The ability of the SP-AMS to quantitatively assess the composition of internally mixed particles is validated through measurements of laboratory-generated organic coated particles, which demonstrate that the SP-AMS can quantify rBC and NR-PM over a wide range of particle compositions and rBC core sizes.« less

Collection efficiency of the soot-particle aerosol mass spectrometer (SP-AMS) for internally mixed particulate black carbon

DOE PAGES

Willis, M. D.; Lee, A. K. Y.; Onasch, T. B.; ...

2014-12-18

The soot-particle aerosol mass spectrometer (SP-AMS) uses an intra-cavity infrared laser to vaporize refractory black carbon (rBC) containing particles, making the particle beam–laser beam overlap critical in determining the collection efficiency (CE) for rBC and associated non-refractory particulate matter (NR-PM). This work evaluates the ability of the SP-AMS to quantify rBC and NR-PM mass in internally mixed particles with different thicknesses of organic coating. Using apparent relative ionization efficiencies for uncoated and thickly coated rBC particles, we report measurements of SP-AMS sensitivity to NR-PM and rBC, for Regal Black, the recommended particulate calibration material. Beam width probe (BWP) measurements aremore » used to illustrate an increase in sensitivity for highly coated particles due to narrowing of the particle beam, which enhances the CE of the SP-AMS by increasing the laser beam–particle beam overlap. Assuming complete overlap for thick coatings, we estimate CE for bare Regal Black particles of 0.6 ± 0.1, which suggests that previously measured SP-AMS sensitivities to Regal Black were underestimated by up to a factor of 2. The efficacy of the BWP measurements is highlighted by studies at a busy road in downtown Toronto and at a non-roadside location, which show particle beam widths similar to, but greater than that of bare Regal Black and coated Regal Black, respectively. Further BWP measurements at field locations will help to constrain the range of CE for fresh and aged rBC-containing particles. The ability of the SP-AMS to quantitatively assess the composition of internally mixed particles is validated through measurements of laboratory-generated organic coated particles, which demonstrate that the SP-AMS can quantify rBC and NR-PM over a wide range of particle compositions and rBC core sizes.« less

Filtering SPAM in P2PSIP Communities with Web of Trust

NASA Astrophysics Data System (ADS)

Heikkilä, Juho; Gurtov, Andrei

Spam is a dominant problem on email systems today. One of the reasons is the lack of infrastructure for security and trust. As Voice over IP (VoIP) communication becomes increasingly popular, proliferation of spam calls is only a matter of time. As SIP identity scheme is practically similar to email, those share the same threats. We utilized Host Identity Protocol (HIP) to provide basic security, such as end-to-end encryption. To provide call filtering, however, other tools are needed. In this paper, we suggest applying trust paths familiar from the PGP web of trust to prevent unwanted communication in P2PSIP communities.

A Comparative Study with RapidMiner and WEKA Tools over some Classification Techniques for SMS Spam

NASA Astrophysics Data System (ADS)

Foozy, Cik Feresa Mohd; Ahmad, Rabiah; Faizal Abdollah, M. A.; Chai Wen, Chuah

2017-08-01

SMS Spamming is a serious attack that can manipulate the use of the SMS by spreading the advertisement in bulk. By sending the unwanted SMS that contain advertisement can make the users feeling disturb and this against the privacy of the mobile users. To overcome these issues, many studies have proposed to detect SMS Spam by using data mining tools. This paper will do a comparative study using five machine learning techniques such as Naïve Bayes, K-NN (K-Nearest Neighbour Algorithm), Decision Tree, Random Forest and Decision Stumps to observe the accuracy result between RapidMiner and WEKA for dataset SMS Spam UCI Machine Learning repository.

Assessing the Factors Affecting the Amount of E-Mail Spam Delivery in a Public School District

ERIC Educational Resources Information Center

Yasenchock, David A.

2013-01-01

Eighty to ninety percent of e-mail is considered unsolicited commercial communication, or spam. Not only does spam violate the privacy of users, but it also incurs societal costs associated with time-related losses, serves as a vehicle for cyber-crime, and threatens the success of e-commerce by lowering consumer confidence. This quantitative…

Impact of food on the pharmacokinetics of first-line anti-TB drugs in treatment-naive TB patients: a randomized cross-over trial.

PubMed

Saktiawati, Antonia M I; Sturkenboom, Marieke G G; Stienstra, Ymkje; Subronto, Yanri W; Sumardi; Kosterink, Jos G W; van der Werf, Tjip S; Alffenaar, Jan-Willem C

2016-03-01

Concomitant food intake influences pharmacokinetics of first-line anti-TB drugs in healthy volunteers. However, in treatment-naive TB patients who are starting with drug treatment, data on the influence of food intake on the pharmacokinetics are absent. This study aimed to quantify the influence of food on the pharmacokinetics of isoniazid, rifampicin, ethambutol and pyrazinamide in TB patients starting anti-TB treatment. A prospective randomized cross-over pharmacokinetic study was conducted in treatment-naive adults with drug-susceptible TB. They received isoniazid, rifampicin and ethambutol intravenously and oral pyrazinamide on day 1, followed by oral administration of these drugs under fasted and fed conditions on two consecutive days. Primary outcome was the bioavailability while fasting and with concomitant food intake. This study was registered with clinicaltrials.gov identifier NCT02121314. Twenty subjects completed the study protocol. Absolute bioavailability in the fasted state and the fed state was 93% and 78% for isoniazid, 87% and 71% for rifampicin and 87% and 82% for ethambutol. Food decreased absolute bioavailability of isoniazid and rifampicin by 15% and 16%, respectively. Pyrazinamide AUC0-24 was comparable for the fasted state (481 mg·h/L) and the fed state (468 mg·h/L). Food lowered the maximum concentrations of isoniazid, rifampicin and pyrazinamide by 42%, 22% and 10%, respectively. Time to maximum concentration was delayed for isoniazid, rifampicin and pyrazinamide. The pharmacokinetics of ethambutol were unaffected by food. Food decreased absolute bioavailability and maximum concentration of isoniazid and rifampicin, but not of ethambutol or pyrazinamide, in treatment-naive TB patients. In patients prone to low drug exposure, this may further compromise treatment efficacy and increase the risk of acquired drug resistance. © The Author 2015. Published by Oxford University Press on behalf of the British Society for Antimicrobial

"I Am Sorry to Send You SPAM": Cross-Cultural Differences in Use of Apologies in Email Advertising in Korea and the U.S.

ERIC Educational Resources Information Center

Park, Hee Sun; Lee, Hye Eun; Song, Jeong An

2005-01-01

A series of studies investigating cultural differences in apology usage in unsolicited email advertising messages (i.e., SPAM) are reported. Study 1 documented that in comparison to American SPAM, a greater percentage of Korean SPAM included apologies. The next five studies ("Ns" = 516, 3132, 662, 524, 536) tested various explanations…

A native Bayesian classifier based routing protocol for VANETS

NASA Astrophysics Data System (ADS)

Bao, Zhenshan; Zhou, Keqin; Zhang, Wenbo; Gong, Xiaolei

2016-12-01

Geographic routing protocols are one of the most hot research areas in VANET (Vehicular Ad-hoc Network). However, there are few routing protocols can take both the transmission efficient and the usage of ratio into account. As we have noticed, different messages in VANET may ask different quality of service. So we raised a Native Bayesian Classifier based routing protocol (Naive Bayesian Classifier-Greedy, NBC-Greedy), which can classify and transmit different messages by its emergency degree. As a result, we can balance the transmission efficient and the usage of ratio with this protocol. Based on Matlab simulation, we can draw a conclusion that NBC-Greedy is more efficient and stable than LR-Greedy and GPSR.

DEXAMETHASONE IMPLANT FOR DIABETIC MACULAR EDEMA IN NAIVE COMPARED WITH REFRACTORY EYES: The International Retina Group Real-Life 24-Month Multicenter Study. The IRGREL-DEX Study.

PubMed

Iglicki, Matias; Busch, Catharina; Zur, Dinah; Okada, Mali; Mariussi, Miriana; Chhablani, Jay Kumar; Cebeci, Zafer; Fraser-Bell, Samantha; Chaikitmongkol, Voraporn; Couturier, Aude; Giancipoli, Ermete; Lupidi, Marco; Rodríguez-Valdés, Patricio J; Rehak, Matus; Fung, Adrian Tien-Chin; Goldstein, Michaella; Loewenstein, Anat

2018-04-24

To investigate efficacy and safety of repeated dexamethasone (DEX) implants over 24 months, in diabetic macular edema (DME) eyes that were treatment naive compared with eyes refractory to anti-vascular endothelial growth factor treatment, in a real-life environment. This multicenter international retrospective study assessed best-corrected visual acuity and central subfield thickness (CST) of naive and refractory eyes to anti-vascular endothelial growth factor injections treated with dexamethasone implants. Safety data (intraocular pressure rise and cataract surgery) were recorded. A total of 130 eyes from 125 patients were included. Baseline best-corrected visual acuity and CST were similar for naive (n = 71) and refractory eyes (n = 59). Both groups improved significantly in vision after 24 months (P < 0.001). However, naive eyes gained statistically significantly more vision than refractory eyes (+11.3 ± 10.0 vs. 7.3 ± 2.7 letters, P = 0.01) and were more likely to gain ≥10 letters (OR 3.31, 95% CI 1.19-9.24, P = 0.02). At 6, 12, and 24 months, CST was significantly decreased compared with baseline in both naive and refractory eyes; however, CST was higher in refractory eyes than in naive eyes (CST 279 ± 61 vs. 313 ± 125 μm, P = 0.10). Over a follow-up of 24 months, vision improved in diabetic macular edema eyes after treatment with dexamethasone implants, both in eyes that were treatment naive and eyes refractory to anti-vascular endothelial growth factor treatment; however, improvement was greater in naive eyes.

WARCProcessor: An Integrative Tool for Building and Management of Web Spam Corpora.

PubMed

Callón, Miguel; Fdez-Glez, Jorge; Ruano-Ordás, David; Laza, Rosalía; Pavón, Reyes; Fdez-Riverola, Florentino; Méndez, Jose Ramón

2017-12-22

In this work we present the design and implementation of WARCProcessor, a novel multiplatform integrative tool aimed to build scientific datasets to facilitate experimentation in web spam research. The developed application allows the user to specify multiple criteria that change the way in which new corpora are generated whilst reducing the number of repetitive and error prone tasks related with existing corpus maintenance. For this goal, WARCProcessor supports up to six commonly used data sources for web spam research, being able to store output corpus in standard WARC format together with complementary metadata files. Additionally, the application facilitates the automatic and concurrent download of web sites from Internet, giving the possibility of configuring the deep of the links to be followed as well as the behaviour when redirected URLs appear. WARCProcessor supports both an interactive GUI interface and a command line utility for being executed in background.

WARCProcessor: An Integrative Tool for Building and Management of Web Spam Corpora

PubMed Central

Callón, Miguel; Fdez-Glez, Jorge; Ruano-Ordás, David; Laza, Rosalía; Pavón, Reyes; Méndez, Jose Ramón

2017-01-01

In this work we present the design and implementation of WARCProcessor, a novel multiplatform integrative tool aimed to build scientific datasets to facilitate experimentation in web spam research. The developed application allows the user to specify multiple criteria that change the way in which new corpora are generated whilst reducing the number of repetitive and error prone tasks related with existing corpus maintenance. For this goal, WARCProcessor supports up to six commonly used data sources for web spam research, being able to store output corpus in standard WARC format together with complementary metadata files. Additionally, the application facilitates the automatic and concurrent download of web sites from Internet, giving the possibility of configuring the deep of the links to be followed as well as the behaviour when redirected URLs appear. WARCProcessor supports both an interactive GUI interface and a command line utility for being executed in background. PMID:29271913

Bayesian network modelling of upper gastrointestinal bleeding

NASA Astrophysics Data System (ADS)

Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri

2013-09-01

Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.

A fast image retrieval method based on SVM and imbalanced samples in filtering multimedia message spam

NASA Astrophysics Data System (ADS)

Chen, Zhang; Peng, Zhenming; Peng, Lingbing; Liao, Dongyi; He, Xin

2011-11-01

With the swift and violent development of the Multimedia Messaging Service (MMS), it becomes an urgent task to filter the Multimedia Message (MM) spam effectively in real-time. For the fact that most MMs contain images or videos, a method based on retrieving images is given in this paper for filtering MM spam. The detection method used in this paper is a combination of skin-color detection, texture detection, and face detection, and the classifier for this imbalanced problem is a very fast multi-classification combining Support vector machine (SVM) with unilateral binary decision tree. The experiments on 3 test sets show that the proposed method is effective, with the interception rate up to 60% and the average detection time for each image less than 1 second.

Bayesian Correlation Analysis for Sequence Count Data

PubMed Central

Lau, Nelson; Perkins, Theodore J.

2016-01-01

Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities’ measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low—especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities’ signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset. PMID:27701449

Oral immunisation of naive and primed animals with transgenic potato tubers expressing LT-B.

PubMed

Lauterslager, T G; Florack, D E; van der Wal, T J; Molthoff, J W; Langeveld, J P; Bosch, D; Boersma, W J; Hilgers, L A

2001-03-21

The efficacy of edible vaccines produced in potato tubers was examined in mice. Transgenic plants were developed by Agrobacterium tumefaciens-mediated transformation. The antigen selected was the non-toxic B subunit of the Escherichia coli enterotoxin (recLT-B). A synthetic gene coding for recLT-B was made and optimised for expression in potato tubers and accumulation in the endoplasmic reticulum. Introduction of this gene under control of the tuber-specific patatin promoter in potato plants resulted in the production of functional, i.e. Gm1-binding, recLT-B pentamers in tubers. Selected tubers containing about 13 microg of recLT-B per gram fresh weight were used for immunisation. Subcutaneous immunisation with an extract of recLT-B tubers yielded high antibody titres in serum that were similar to those obtained with bacterial recLT-B. The efficacy of oral administration of recLT-B tubers was determined by measuring mucosal and systemic immune responses in naive and primed mice. Animals were primed by subcutaneous injection of an extract of recLT-B tuber plus adjuvant. Naive and primed mice were fed 5 g of tubers ( approximately 65 microg of recLT-B) or were intubated intragastrically with 0.4 ml of tuber extract ( approximately 2 microg of recLT-B). In naive mice, feeding recLT-B tubers or intubation of tuber extract did not induce detectable anti-LT antibody titres. In primed animals, however, oral immunisation resulted in significant anti-LT IgA antibody responses in serum and faeces. Intragastric intubation of tuber extract revealed higher responses than feeding of tubers. These results indicate clearly that functional recLT-B can be produced in potato tubers, that this recombinant protein is immunogenic and that oral administration thereof elicits both systemic and local IgA responses in parentally primed, but not naive, animals.

Spartan Auxiliary Mount Panel (SPAM): A Metal Matrix Composite Honeycomb Panel for Space Flight Use

NASA Technical Reports Server (NTRS)

Segal, Kenneth N.; Stevens, Edward J.

1998-01-01

This presentation focus on the use of metal matrix composite (MMC) material option in spaceflight hardware applications. It addresses the important questions and issues such as: what is SPAM; why the use of MMC; design requirements and flexibility; qualification testing; and flight concerns.

Quantum Bayesian networks with application to games displaying Parrondo's paradox

NASA Astrophysics Data System (ADS)

Pejic, Michael

Bayesian networks and their accompanying graphical models are widely used for prediction and analysis across many disciplines. We will reformulate these in terms of linear maps. This reformulation will suggest a natural extension, which we will show is equivalent to standard textbook quantum mechanics. Therefore, this extension will be termed quantum. However, the term quantum should not be taken to imply this extension is necessarily only of utility in situations traditionally thought of as in the domain of quantum mechanics. In principle, it may be employed in any modelling situation, say forecasting the weather or the stock market---it is up to experiment to determine if this extension is useful in practice. Even restricting to the domain of quantum mechanics, with this new formulation the advantages of Bayesian networks can be maintained for models incorporating quantum and mixed classical-quantum behavior. The use of these will be illustrated by various basic examples. Parrondo's paradox refers to the situation where two, multi-round games with a fixed winning criteria, both with probability greater than one-half for one player to win, are combined. Using a possibly biased coin to determine the rule to employ for each round, paradoxically, the previously losing player now wins the combined game with probabilitygreater than one-half. Using the extended Bayesian networks, we will formulate and analyze classical observed, classical hidden, and quantum versions of a game that displays this paradox, finding bounds for the discrepancy from naive expectations for the occurrence of the paradox. A quantum paradox inspired by Parrondo's paradox will also be analyzed. We will prove a bound for the discrepancy from naive expectations for this paradox as well. Games involving quantum walks that achieve this bound will be presented.

Behind the Spam: A ``Spectral Analysis'' of Predatory Publishers

NASA Astrophysics Data System (ADS)

Beall, Jeffrey

2016-10-01

Most researchers today are bombarded with spam email solicitations from questionable scholarly publishers. These emails solicit article manuscripts, editorial board service, and even ad hoc peer reviews. These ``predatory'' publishers exploit the scholarly publishing process, patterning themselves after legitimate scholarly publishers yet performing little or no peer review and quickly accepting submitted manuscripts and collecting fees from submitting authors. These counterfeit publishers and journals have published much junk science? especially in the field of cosmology? threatening the integrity of the academic record. This paper examines the current state of predatory publishing and advises researchers how to navigate scholarly publishing to best avoid predatory publishers and other scholarly publishing-related perils.

A combined Fuzzy and Naive Bayesian strategy can be used to assign event codes to injury narratives.

PubMed

Marucci-Wellman, H; Lehto, M; Corns, H

2011-12-01

Bayesian methods show promise for classifying injury narratives from large administrative datasets into cause groups. This study examined a combined approach where two Bayesian models (Fuzzy and Naïve) were used to either classify a narrative or select it for manual review. Injury narratives were extracted from claims filed with a worker's compensation insurance provider between January 2002 and December 2004. Narratives were separated into a training set (n=11,000) and prediction set (n=3,000). Expert coders assigned two-digit Bureau of Labor Statistics Occupational Injury and Illness Classification event codes to each narrative. Fuzzy and Naïve Bayesian models were developed using manually classified cases in the training set. Two semi-automatic machine coding strategies were evaluated. The first strategy assigned cases for manual review if the Fuzzy and Naïve models disagreed on the classification. The second strategy selected additional cases for manual review from the Agree dataset using prediction strength to reach a level of 50% computer coding and 50% manual coding. When agreement alone was used as the filtering strategy, the majority were coded by the computer (n=1,928, 64%) leaving 36% for manual review. The overall combined (human plus computer) sensitivity was 0.90 and positive predictive value (PPV) was >0.90 for 11 of 18 2-digit event categories. Implementing the 2nd strategy improved results with an overall sensitivity of 0.95 and PPV >0.90 for 17 of 18 categories. A combined Naïve-Fuzzy Bayesian approach can classify some narratives with high accuracy and identify others most beneficial for manual review, reducing the burden on human coders.

An Inexpensive and Easy Ultrasound Phantom: A Novel Use for SPAM.

PubMed

Nolting, Laura; Hunt, Patrick; Cook, Thomas; Douglas, Barton

2016-04-01

Ultrasound models, commonly referred to as "phantoms," are simulation tools for ultrasound education. Commercially produced phantoms are available, but there are "homemade" alternatives such as raw poultry and gelatin molds. Precooked, processed meat, better known as SPAM (Hormel Foods Corporation, Austin, MN), can be used as an ultrasound phantom to teach several ultrasound applications. It is a versatile, hygienic, and easily manipulated medium that does not require refrigeration or preparatory work and can be easily discarded at the end of use. © 2016 by the American Institute of Ultrasound in Medicine.

Antibody to soluble 1,3/1,6-beta-D-glucan, SCG in sera of naive DBA/2 mice.

PubMed

Harada, Toshie; Nagi Miura, Noriko; Adachi, Yoshiyuki; Nakajima, Mitsuhiro; Yadomae, Toshiro; Ohno, Naohito

2003-08-01

A branched beta-glucan from Sparassis crispa (SCG) is a major 6-branched 1,3-beta-D-glucan showing antitumor activity. In the present study, we examined the anti-SCG antibody in naive mice by ELISA. Using SCG coated plate, sera of naive DBA/1 and DBA/2 mice contained significantly higher titers of antibody than other strains of mice. Anti-SCG Ab titers of each DBA/1 and DBA/2 mice were significantly varied. Using various polysaccharide-coated plate, sera of DBA/2 mice also reacted with a beta-glucan from Candida spp. (CSBG) having 1,3-beta and 1,6-beta-glucosidic linkages. The SCG specific immunoglobulin (Ig) M but G was detected in sera. The reactivity of sera to coated SCG was neutralized by adding soluble SCG and CSBG as competitor. These results suggested that DBA/1 and DBA/2 strains carry specific and unique immunological characteristics to branched 1,3-/1,6-beta-glucan.

Behind the Spam: A "Spectral Analysis" of Predatory Publishers

NASA Astrophysics Data System (ADS)

Beall, Jeffrey

2015-08-01

Most researchers today are bombarded with spam email solicitations from questionable scholarly publishers. These emails solicit article manuscripts, editorial board service, and even ad hoc peer reviews. These "predatory" publishers exploit the scholarly publishing process, patterning themselves after legitimate scholarly publishers yet performing little or no peer review and quickly accepting submitted manuscripts and collecting fees from submitting authors. These counterfeit publishers and journals have published much junk science — especially in the field of cosmology — threatening the integrity of the academic record. This presentation examines the current state of predatory publishing and related scams such as fake impact factors and advises researchers how to navigate scholarly publishing to best avoid predatory publishers and other scholarly publishing-related perils.

Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.

PubMed

Kaiser, Jacob L; Bland, Cassidy L; Klinke, David J

2016-03-01

Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016. © 2016 American Institute of Chemical Engineers.

Exploring the Influence of User Perception of Spam on Managers of Small Manufacturing Firms in North Carolina

ERIC Educational Resources Information Center

Wilson, Darrell G.

2010-01-01

Unsolicited bulk email received by business users has increased exponentially in volume and complexity since the introduction of email. However, the factors that influence managers to purchase spam filters have never been fully documented or identified, only assumed. The purpose of this qualitative study was to identify, understand and document…

Differential effects of ibogaine on local cerebral glucose utilization in drug-naive and morphine-dependent rats.

PubMed

Levant, Beth; Pazdernik, Thomas L

2004-04-02

Ibogaine, a hallucinogenic indole alkaloid, has been proposed as a treatment for addiction to opioids and other drugs of abuse. The mechanism for its putative anti-addictive effects is unknown. In this study, the effects of ibogaine on local cerebral glucose utilization (LCGU) were determined in freely moving, drug-naive, or morphine-dependent adult, male, Sprague-Dawley rats using the [(14)C]2-deoxyglucose (2-DG) method. Morphine-dependent rats were treated with increasing doses of morphine (5-25 mg/kg, s.c., b.i.d.) and then maintained at 25 mg/kg (b.i.d.) for 4-7 days. For the 2-DG procedure, rats were injected with saline or ibogaine (40 mg/kg, i.p.). 2-DG was administered 1 h after administration of ibogaine. The rate of LCGU was determined by quantitative autoradiography in 46 brain regions. In drug-naive animals, ibogaine produced significant increases in LCGU in the parietal, cingulate, and occipital cortices and cerebellum compared to controls consistent with its activity as a hallucinogen and a tremorogen. Morphine-dependent rats had only minor alterations in LCGU at the time assessed in this experiment. However, in morphine-dependent animals, ibogaine produced a global decrease in LCGU that was greatest in brain regions such as the lateral and medial preoptic areas, nucleus of the diagonal band, nucleus accumbens shell, inferior colliculus, locus coeruleus, and flocculus compared to morphine-dependent animals treated with saline. These findings indicate that ibogaine produces distinctly different effects on LCGU in drug-naive and morphine-dependent rats. This suggests that different mechanisms may underlie ibogaine's hallucinogenic and anti-addictive effects.

Evaluation of Interruption Behavior by Naive Encoders.

ERIC Educational Resources Information Center

Coon, Christine A.; Schwanenflugel, Paula J.

1996-01-01

Determines the characteristics of interactions that influence judgments of interruption behavior in naive observers. Asks subjects to decide whether an example of an interruption was an interruption and then rate it in terms of how "good" or "bad" it was. Finds that naive observers use some of the same features described in…

Risk of Erectile Dysfunction in Transfusion-naive Thalassemia Men

PubMed Central

Chen, Yu-Guang; Lin, Te-Yu; Lin, Cheng-Li; Dai, Ming-Shen; Ho, Ching-Liang; Kao, Chia-Hung

2015-01-01

Abstract Based on the mechanism of pathophysiology, thalassemia major or transfusion-dependent thalassemia patients may have an increased risk of developing organic erectile dysfunction resulting from hypogonadism. However, there have been few studies investigating the association between erectile dysfunction and transfusion-naive thalassemia populations. We constructed a population-based cohort study to elucidate the association between transfusion-naive thalassemia populations and organic erectile dysfunction This nationwide population-based cohort study involved analyzing data from 1998 to 2010 obtained from the Taiwanese National Health Insurance Research Database, with a follow-up period extending to the end of 2011. We identified men with transfusion-naive thalassemia and selected a comparison cohort that was frequency-matched with these according to age, and year of diagnosis thalassemia at a ratio of 1 thalassemia man to 4 control men. We analyzed the risks for transfusion-naive thalassemia men and organic erectile dysfunction by using Cox proportional hazards regression models. In this study, 588 transfusion-naive thalassemia men and 2337 controls were included. Total 12 patients were identified within the thalassaemia group and 10 within the control group. The overall risks for developing organic erectile dysfunction were 4.56-fold in patients with transfusion-naive thalassemia men compared with the comparison cohort after we adjusted for age and comorbidities. Our long-term cohort study results showed that in transfusion-naive thalassemia men, there was a higher risk for the development of organic erectile dysfunction, particularly in those patients with comorbidities. PMID:25837766

Human Naive T Cells Express Functional CXCL8 and Promote Tumorigenesis.

PubMed

Crespo, Joel; Wu, Ke; Li, Wei; Kryczek, Ilona; Maj, Tomasz; Vatan, Linda; Wei, Shuang; Opipari, Anthony W; Zou, Weiping

2018-05-25

Naive T cells are thought to be functionally quiescent. In this study, we studied and compared the phenotype, cytokine profile, and potential function of human naive CD4 + T cells in umbilical cord and peripheral blood. We found that naive CD4 + T cells, but not memory T cells, expressed high levels of chemokine CXCL8. CXCL8 + naive T cells were preferentially enriched CD31 + T cells and did not express T cell activation markers or typical Th effector cytokines, including IFN-γ, IL-4, IL-17, and IL-22. In addition, upon activation, naive T cells retained high levels of CXCL8 expression. Furthermore, we showed that naive T cell-derived CXCL8 mediated neutrophil migration in the in vitro migration assay, supported tumor sphere formation, and promoted tumor growth in an in vivo human xenograft model. Thus, human naive T cells are phenotypically and functionally heterogeneous and can carry out active functions in immune responses. Copyright © 2018 by The American Association of Immunologists, Inc.

Diagnostic performance of ELISA, IFAT and Western blot for the detection of anti-Leishmania infantum antibodies in cats using a Bayesian analysis without a gold standard.

PubMed

Persichetti, Maria Flaminia; Solano-Gallego, Laia; Vullo, Angela; Masucci, Marisa; Marty, Pierre; Delaunay, Pascal; Vitale, Fabrizio; Pennisi, Maria Grazia

2017-03-13

Anti-Leishmania antibodies are increasingly investigated in cats for epidemiological studies or for the diagnosis of clinical feline leishmaniosis. The immunofluorescent antibody test (IFAT), the enzyme-linked immunosorbent assay (ELISA) and western blot (WB) are the serological tests more frequently used. The aim of the present study was to assess diagnostic performance of IFAT, ELISA and WB to detect anti-L. infantum antibodies in feline serum samples obtained from endemic (n = 76) and non-endemic (n = 64) areas and from cats affected by feline leishmaniosis (n = 21) by a Bayesian approach without a gold standard. Cut-offs were set at 80 titre for IFAT and 40 ELISA units for ELISA. WB was considered positive in presence of at least a 18 KDa band. Statistical analysis was performed through a written routine with MATLAB software in the Bayesian framework. The latent data and observations from the joint posterior were simulated in the Bayesian approach by an iterative Markov Chain Monte Carlo technique using the Gibbs sampler for estimating sensitivity and specificity of the three tests. The median seroprevalence in the sample used for evaluating the performance of tests was estimated at 0.27 [credible interval (CI) = 0.20-0.34]. The median sensitivity of the three different methods was 0.97 (CI: 0.86-1.00), 0.75 (CI: 0.61-0.87) and 0.70 (CI: 0.56-0.83) for WB, IFAT and ELISA, respectively. Median specificity reached 0.99 (CI: 0.96-1.00) with WB, 0.97 (CI: 0.93-0.99) with IFAT and 0.98 (CI: 0.94-1.00) with ELISA. IFAT was more sensitive than ELISA (75 vs 70%) for the detection of subclinical infection while ELISA was better for diagnosing clinical leishmaniosis when compared with IFAT (98 vs 97%). The overall performance of all serological techniques was good and the most accurate test for anti-Leishmania antibody detection in feline serum samples was WB.

Derivation of novel human ground state naive pluripotent stem cells.

PubMed

Gafni, Ohad; Weinberger, Leehee; Mansour, Abed AlFatah; Manor, Yair S; Chomsky, Elad; Ben-Yosef, Dalit; Kalma, Yael; Viukov, Sergey; Maza, Itay; Zviran, Asaf; Rais, Yoach; Shipony, Zohar; Mukamel, Zohar; Krupalnik, Vladislav; Zerbib, Mirie; Geula, Shay; Caspi, Inbal; Schneir, Dan; Shwartz, Tamar; Gilad, Shlomit; Amann-Zalcenstein, Daniela; Benjamin, Sima; Amit, Ido; Tanay, Amos; Massarwa, Rada; Novershtern, Noa; Hanna, Jacob H

2013-12-12

Mouse embryonic stem (ES) cells are isolated from the inner cell mass of blastocysts, and can be preserved in vitro in a naive inner-cell-mass-like configuration by providing exogenous stimulation with leukaemia inhibitory factor (LIF) and small molecule inhibition of ERK1/ERK2 and GSK3β signalling (termed 2i/LIF conditions). Hallmarks of naive pluripotency include driving Oct4 (also known as Pou5f1) transcription by its distal enhancer, retaining a pre-inactivation X chromosome state, and global reduction in DNA methylation and in H3K27me3 repressive chromatin mark deposition on developmental regulatory gene promoters. Upon withdrawal of 2i/LIF, naive mouse ES cells can drift towards a primed pluripotent state resembling that of the post-implantation epiblast. Although human ES cells share several molecular features with naive mouse ES cells, they also share a variety of epigenetic properties with primed murine epiblast stem cells (EpiSCs). These include predominant use of the proximal enhancer element to maintain OCT4 expression, pronounced tendency for X chromosome inactivation in most female human ES cells, increase in DNA methylation and prominent deposition of H3K27me3 and bivalent domain acquisition on lineage regulatory genes. The feasibility of establishing human ground state naive pluripotency in vitro with equivalent molecular and functional features to those characterized in mouse ES cells remains to be defined. Here we establish defined conditions that facilitate the derivation of genetically unmodified human naive pluripotent stem cells from already established primed human ES cells, from somatic cells through induced pluripotent stem (iPS) cell reprogramming or directly from blastocysts. The novel naive pluripotent cells validated herein retain molecular characteristics and functional properties that are highly similar to mouse naive ES cells, and distinct from conventional primed human pluripotent cells. This includes competence in the generation

The Preference for Symmetry in Flower-Naive and Not-so-Naive Bumblebees

ERIC Educational Resources Information Center

Plowright, C. M. S.; Evans, S. A.; Leung, J. Chew; Collin, C. A.

2011-01-01

Truly flower-naive bumblebees, with no prior rewarded experience for visits on any visual patterns outside the colony, were tested for their choice of bilaterally symmetric over asymmetric patterns in a radial-arm maze. No preference for symmetry was found. Prior training with rewarded black and white disks did, however, lead to a significant…

Impact of censoring on learning Bayesian networks in survival modelling.

PubMed

Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

2009-11-01

Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from

Naive Probability: A Mental Model Theory of Extensional Reasoning.

ERIC Educational Resources Information Center

Johnson-Laird, P. N.; Legrenzi, Paolo; Girotto, Vittorio; Legrenzi, Maria Sonino; Caverni, Jean-Paul

1999-01-01

Outlines a theory of naive probability in which individuals who are unfamiliar with the probability calculus can infer the probabilities of events in an "extensional" way. The theory accommodates reasoning based on numerical premises, and explains how naive reasoners can infer posterior probabilities without relying on Bayes's theorem.…

Early Remission Is a Realistic Target in a Majority of Patients with DMARD-naive Rheumatoid Arthritis.

PubMed

Rannio, Tuomas; Asikainen, Juha; Kokko, Arto; Hannonen, Pekka; Sokka, Tuulikki

2016-04-01

We analyzed remission rates at 3 and 12 months in patients with rheumatoid arthritis (RA) who were naive for disease-modifying antirheumatic drugs (DMARD) and who were treated in a Finnish rheumatology clinic from 2008 to 2011. We compared remission rates and drug treatments between patients with RA and patients with undifferentiated arthritis (UA). Data from all DMARD-naive RA and UA patients from the healthcare district were collected using software that includes demographic and clinical characteristics, disease activity, medications, and patient-reported outcomes. Our rheumatology clinic applies the treat-to-target principle, electronic monitoring of patients, and multidisciplinary care. Out of 409 patients, 406 had data for classification by the 2010 RA criteria of the American College of Rheumatology/European League Against Rheumatism. A total of 68% were female, and mean age (SD) was 58 (16) years. Respectively, 56%, 60%, and 68% were positive for anticyclic citrullinated peptide antibodies (anti-CCP), rheumatoid factor (RF), and RF/anti-CCP, and 19% had erosive disease. The median (interquartile range) duration of symptoms was 6 (4-12) months. A total of 310 were classified as RA and 96 as UA. The patients with UA were younger, had better functional status and lower disease activity, and were more often seronegative than the patients with RA. The 28-joint Disease Activity Score (3 variables) remission rates of RA and UA patients at 3 months were 67% and 58% (p = 0.13), and at 12 months, 71% and 79%, respectively (p = 0.16). Sustained remission was observed in 57%/56% of RA/UA patients. Patients with RA used more conventional synthetic DMARD combinations than did patients with UA. None used biological DMARD at 3 months, and only 2.7%/1.1% of the patients (RA/UA) used them at 12 months (p = 0.36). Remarkably high remission rates are achievable in real-world DMARD-naive patients with RA or UA.

miRNA profiling of human naive CD4 T cells links miR-34c-5p to cell activation and HIV replication.

PubMed

Amaral, Andreia J; Andrade, Jorge; Foxall, Russell B; Matoso, Paula; Matos, Ana M; Soares, Rui S; Rocha, Cheila; Ramos, Christian G; Tendeiro, Rita; Serra-Caetano, Ana; Guerra-Assunção, José A; Santa-Marta, Mariana; Gonçalves, João; Gama-Carvalho, Margarida; Sousa, Ana E

2017-02-01

Cell activation is a vital step for T-cell memory/effector differentiation as well as for productive HIV infection. To identify novel regulators of this process, we used next-generation sequencing to profile changes in microRNA expression occurring in purified human naive CD4 T cells in response to TCR stimulation and/or HIV infection. Our results demonstrate, for the first time, the transcriptional up-regulation of miR-34c-5p in response to TCR stimulation in naive CD4 T cells. The induction of this miR was further consistently found to be reduced by both HIV-1 and HIV-2 infections. Overexpression of miR-34c-5p led to changes in the expression of several genes involved in TCR signaling and cell activation, confirming its role as a novel regulator of naive CD4 T-cell activation. We additionally show that miR-34c-5p promotes HIV-1 replication, suggesting that its down-regulation during HIV infection may be part of an anti-viral host response. © 2016 The Authors.

Acidic hyaluronidase activity is present in mouse sperm and is reduced in the absence of SPAM1: Evidence for a Role for Hyaluronidase 3 in mouse and human sperm

PubMed Central

Reese, Kristen L.; Aravindan, Rolands G.; Griffiths, Genevieve S.; Shao, Minghai; Wang, Yipei; Galileo, Deni S.; Atmuri, Vasantha; Triggs-Raine, Barbara L.; Martin-DeLeon, Patricia A.

2010-01-01

The molecular mechanisms underlying sperm penetration of the physical barriers surrounding the oocyte have not been completely delineated. Although neutral-active or “reproductive” hyaluronidases (hyases), exemplified by Sperm Adhesion Molecule 1 (SPAM1), are thought to be responsible for hyaluronan digestion in the egg vestments and for sperm-zona binding, their roles in mouse sperm have been recently questioned. Here we report that acidic “somatic” Hyaluronidase 3 (HYAL3) exists in two isoforms in human (~47 kDa, ~55 kDa) and mouse (~44, ~47kDa) sperm where it resides on the plasma membrane over the head and midpiece. Mouse isoforms are differentially distributed in the soluble (SAP), membrane (MBP), and acrosome-reacted (AR) fraction where they are most abundant. Comparisons of zymography of Hyal3 null and wild-type (WT) AR and MBP fractions show significant HYAL3 activity at pH 3 and 4, and less at 7. At pH 4, a second acid-active hyase band at ~57 kDa is present in the AR fraction. HYAL3 activity was confirmed using immunoprecipitated HYAL3 and spectrophotometry. In total proteins, hyase activity was higher at pH 6 than at 4 where Spam1 nulls had significantly (P<0.01) diminished activity, indicating that murine SPAM1 has acidic activity. Although fully fertile, Hyal3 null sperm showed delayed cumulus penetration and reduced acrosomal exocytosis. HYAL3, similar to SPAM1 with which it shares 74.6% structural similarity, exists in epididymal tissue/fluid from which it is acquired by caudal mouse sperm in vitro. Our results indicate for the first time the concerted activity of both neutral- and acid-active hyaluronidases in sperm. PMID:20586096

Do the Naive Know Best? The Predictive Power of Naive Ratings of Couple Interactions

ERIC Educational Resources Information Center

Baucom, Katherine J. W.; Baucom, Brian R.; Christensen, Andrew

2012-01-01

We examined the utility of naive ratings of communication patterns and relationship quality in a large sample of distressed couples. Untrained raters assessed 10-min videotaped interactions from 134 distressed couples who participated in both problem-solving and social support discussions at each of 3 time points (pre-therapy, post-therapy, and…

Blocking the recruitment of naive CD4+ T cells reverses immunosuppression in breast cancer

PubMed Central

Su, Shicheng; Liao, Jianyou; Liu, Jiang; Huang, Di; He, Chonghua; Chen, Fei; Yang, LinBing; Wu, Wei; Chen, Jianing; Lin, Ling; Zeng, Yunjie; Ouyang, Nengtai; Cui, Xiuying; Yao, Herui; Su, Fengxi; Huang, Jian-dong; Lieberman, Judy; Liu, Qiang; Song, Erwei

2017-01-01

The origin of tumor-infiltrating Tregs, critical mediators of tumor immunosuppression, is unclear. Here, we show that tumor-infiltrating naive CD4+ T cells and Tregs in human breast cancer have overlapping TCR repertoires, while hardly overlap with circulating Tregs, suggesting that intratumoral Tregs mainly develop from naive T cells in situ rather than from recruited Tregs. Furthermore, the abundance of naive CD4+ T cells and Tregs is closely correlated, both indicating poor prognosis for breast cancer patients. Naive CD4+ T cells adhere to tumor slices in proportion to the abundance of CCL18-producing macrophages. Moreover, adoptively transferred human naive CD4+ T cells infiltrate human breast cancer orthotopic xenografts in a CCL18-dependent manner. In human breast cancer xenografts in humanized mice, blocking the recruitment of naive CD4+ T cells into tumor by knocking down the expression of PITPNM3, a CCL18 receptor, significantly reduces intratumoral Tregs and inhibits tumor progression. These findings suggest that breast tumor-infiltrating Tregs arise from chemotaxis of circulating naive CD4+ T cells that differentiate into Tregs in situ. Inhibiting naive CD4+ T cell recruitment into tumors by interfering with PITPNM3 recognition of CCL18 may be an attractive strategy for anticancer immunotherapy. PMID:28290464

Naive Juveniles Are More Likely to Become Breeders after Witnessing Predator Mobbing.

PubMed

Griesser, Michael; Suzuki, Toshitaka N

2017-01-01

Responding appropriately during the first predatory attack in life is often critical for survival. In many social species, naive juveniles acquire this skill from conspecifics, but its fitness consequences remain virtually unknown. Here we experimentally demonstrate how naive juvenile Siberian jays (Perisoreus infaustus) derive a long-term fitness benefit from witnessing knowledgeable adults mobbing their principal predator, the goshawk (Accipiter gentilis). Siberian jays live in family groups of two to six individuals that also can include unrelated nonbreeders. Field observations showed that Siberian jays encounter predators only rarely, and, indeed, naive juveniles do not respond to predator models when on their own but do when observing other individuals mobbing them. Predator exposure experiments demonstrated that naive juveniles had a substantially higher first-winter survival after observing knowledgeable group members mobbing a goshawk model, increasing their likelihood of acquiring a breeding position later in life. Previous research showed that naive individuals may learn from others how to respond to predators, care for offspring, or choose mates, generally assuming that social learning has long-term fitness consequences without empirical evidence. Our results demonstrate a long-term fitness benefit of vertical social learning for naive individuals in the wild, emphasizing its evolutionary importance in animals, including humans.

Two separate defects affecting true naive or virtual memory T cell precursors combine to reduce naive T cell responses with aging.

PubMed

Renkema, Kristin R; Li, Gang; Wu, Angela; Smithey, Megan J; Nikolich-Žugich, Janko

2014-01-01

Naive T cell responses are eroded with aging. We and others have recently shown that unimmunized old mice lose ≥ 70% of Ag-specific CD8 T cell precursors and that many of the remaining precursors acquire a virtual (central) memory (VM; CD44(hi)CD62L(hi)) phenotype. In this study, we demonstrate that unimmunized TCR transgenic (TCRTg) mice also undergo massive VM conversion with age, exhibiting rapid effector function upon both TCR and cytokine triggering. Age-related VM conversion in TCRTg mice directly depended on replacement of the original TCRTg specificity by endogenous TCRα rearrangements, indicating that TCR signals must be critical in VM conversion. Importantly, we found that VM conversion had adverse functional effects in both old wild-type and old TCRTg mice; that is, old VM, but not old true naive, T cells exhibited blunted TCR-mediated, but not IL-15-mediated, proliferation. This selective proliferative senescence correlated with increased apoptosis in old VM cells in response to peptide, but decreased apoptosis in response to homeostatic cytokines IL-7 and IL-15. Our results identify TCR as the key factor in differential maintenance and function of Ag-specific precursors in unimmunized mice with aging, and they demonstrate that two separate age-related defects--drastic reduction in true naive T cell precursors and impaired proliferative capacity of their VM cousins--combine to reduce naive T cell responses with aging.

Naive Theories of Social Groups

ERIC Educational Resources Information Center

Rhodes, Marjorie

2012-01-01

Four studies examined children's (ages 3-10, Total N = 235) naive theories of social groups, in particular, their expectations about how group memberships constrain social interactions. After introduction to novel groups of people, preschoolers (ages 3-5) reliably expected agents from one group to harm members of the other group (rather than…

Essays in Information Economics

ERIC Educational Resources Information Center

Chiao, Hak Fung

2010-01-01

I study two economic responses to the challenges of copyright infringements and spam brought about by the birth of the Internet. These responses are anti-spam mechanisms and open contents. I derive conditions under which distribution and care level taken to avoid damages in open contents are socially efficient or inefficient. Then I report…

Evaluation of Bayesian approaches to identify DDT source contributions to soils in Southeast China.

PubMed

Zeng, Faming; Yang, Dan; Xing, Xinli; Qi, Shihua

2017-06-01

Dicofol application may be an important source to elevate the dichlorodiphenyltrichloroethane (DDT) residues to soils in Fujian, Southeast China, after the technical DDT was banned, which left DDT residues from the historical application. The DDT residues varied geographically, corresponding to the varied potential sources of DDT. In this study, a novel approach based on the Bayesian method (BM) was developed to identify the source contributions of DDT to soils, composed with both historical DDT and dicofol. The Naive Bayesian classifier was used basing on the subset of the samples, which were determined by chemical analysis independent of the Bayesian approach. The results show that BM (95%) was higher than that using the ratio of o, p'-/p, p'-DDT (84%) to identify DDT source contributions. High detection rate (97%) of dicofol (p, p'-OH-DDT) was observed in the subset, showing dicofol application influenced the DDX levels in soils in Fujian. However, the contribution from historical technical DDT source was greater than that from dicofol in Fujian, indicating historical technical DDT was still an important pollution source to soils. In addition, both the DDX (DDT isomers and derivatives) level and dicofol contribution in non-agricultural soils were higher than other agricultural land uses, especially in hilly regions, the potential cause may be the atmospheric transport of dicofol type DDT, after spraying during daytime, or regional difference on production and application. Copyright © 2017 Elsevier Ltd. All rights reserved.

THUIR at TREC 2009 Web Track: Finding Relevant and Diverse Results for Large Scale Web Search

DTIC Science & Technology

2009-11-01

Porn words‟ filtering is also one of the anti-spam techniques in real world search engines. A list of porn words was found from the internet [2...When the numbers of the porn words in the page is larger than α, then the page is taken as the spam. In our experiments, the threshold is set to 16

Naive Theory of Biology: The Pre-School Child's Explanation of Death

ERIC Educational Resources Information Center

Vlok, Milandre; de Witt, Marike W.

2012-01-01

This article explains the naive theory of biology that the pre-school child uses to explain the cause of death. The empirical investigation showed that the young participants do use a naive theory of biology to explain function and do make reference to "vitalistic causality" in explaining organ function. Furthermore, most of these…

Calcium-mediated shaping of naive CD4 T-cell phenotype and function

PubMed Central

Guichard, Vincent; Bonilla, Nelly; Durand, Aurélie; Audemard-Verger, Alexandra; Guilbert, Thomas; Martin, Bruno

2017-01-01

Continuous contact with self-major histocompatibility complex ligands is essential for the survival of naive CD4 T cells. We have previously shown that the resulting tonic TCR signaling also influences their fate upon activation by increasing their ability to differentiate into induced/peripheral regulatory T cells. To decipher the molecular mechanisms governing this process, we here focus on the TCR signaling cascade and demonstrate that a rise in intracellular calcium levels is sufficient to modulate the phenotype of mouse naive CD4 T cells and to increase their sensitivity to regulatory T-cell polarization signals, both processes relying on calcineurin activation. Accordingly, in vivo calcineurin inhibition leads the most self-reactive naive CD4 T cells to adopt the phenotype of their less self-reactive cell-counterparts. Collectively, our findings demonstrate that calcium-mediated activation of the calcineurin pathway acts as a rheostat to shape both the phenotype and effector potential of naive CD4 T cells in the steady-state. PMID:29239722

LORETA functional imaging in antipsychotic-naive and olanzapine-, clozapine- and risperidone-treated patients with schizophrenia.

PubMed

Tislerova, Barbora; Brunovsky, Martin; Horacek, Jiri; Novak, Tomas; Kopecek, Miloslav; Mohr, Pavel; Krajca, Vladimír

2008-01-01

The aim of our study was to detect changes in the distribution of electrical brain activity in schizophrenic patients who were antipsychotic naive and those who received treatment with clozapine, olanzapine or risperidone. We included 41 subjects with schizophrenia (antipsychotic naive = 11; clozapine = 8; olanzapine = 10; risperidone = 12) and 20 healthy controls. Low-resolution brain electromagnetic tomography was computed from 19-channel electroencephalography for the frequency bands delta, theta, alpha-1, alpha-2, beta-1, beta-2 and beta-3. We compared antipsychotic-naive subjects with healthy controls and medicated patients. (1) Comparing antipsychotic-naive subjects and controls we found a general increase in the slow delta and theta frequencies over the fronto-temporo-occipital cortex, particularly in the temporolimbic structures, an increase in alpha-1 and alpha-2 in the temporal cortex and an increase in beta-1 and beta-2 in the temporo-occipital and posterior limbic structures. (2) Comparing patients who received clozapine and those who were antipsychotic naive, we found an increase in delta and theta frequencies in the anterior cingulate and medial frontal cortex, and a decrease in alpha-1 and beta-2 in the occipital structures. (3) Comparing patients taking olanzapine with those who were antipsychotic naive, there was an increase in theta frequencies in the anterior cingulum, a decrease in alpha-1, beta-2 and beta-3 in the occipital cortex and posterior limbic structures, and a decrease in beta-3 in the frontotemporal cortex and anterior cingulum. (4) In patients taking risperidone, we found no significant changes from those who were antipsychotic naive. Our results in antipsychotic-naive patients are in agreement with existing functional findings. Changes in those taking clozapine and olanzapine versus those who were antipsychotic naive suggest a compensatory mechanism in the neurobiological substrate for schizophrenia. The lack of difference in

The Persistence of "Solid" and "Liquid" Naive Conceptions: A Reaction Time Study

ERIC Educational Resources Information Center

Babai, Reuven; Amsterdamer, Anat

2008-01-01

The study explores whether the naive concepts of "solid" and "liquid" persist in adolescence. Accuracy of responses and reaction times where measured while 41 ninth graders classified different solids (rigid, non-rigid and powders) and different liquids (runny, dense) into solid or liquid. The results show that these naive conceptions affect…

Single molecule force spectroscopy at high data acquisition: A Bayesian nonparametric analysis

NASA Astrophysics Data System (ADS)

Sgouralis, Ioannis; Whitmore, Miles; Lapidus, Lisa; Comstock, Matthew J.; Pressé, Steve

2018-03-01

Bayesian nonparametrics (BNPs) are poised to have a deep impact in the analysis of single molecule data as they provide posterior probabilities over entire models consistent with the supplied data, not just model parameters of one preferred model. Thus they provide an elegant and rigorous solution to the difficult problem encountered when selecting an appropriate candidate model. Nevertheless, BNPs' flexibility to learn models and their associated parameters from experimental data is a double-edged sword. Most importantly, BNPs are prone to increasing the complexity of the estimated models due to artifactual features present in time traces. Thus, because of experimental challenges unique to single molecule methods, naive application of available BNP tools is not possible. Here we consider traces with time correlations and, as a specific example, we deal with force spectroscopy traces collected at high acquisition rates. While high acquisition rates are required in order to capture dwells in short-lived molecular states, in this setup, a slow response of the optical trap instrumentation (i.e., trapped beads, ambient fluid, and tethering handles) distorts the molecular signals introducing time correlations into the data that may be misinterpreted as true states by naive BNPs. Our adaptation of BNP tools explicitly takes into consideration these response dynamics, in addition to drift and noise, and makes unsupervised time series analysis of correlated single molecule force spectroscopy measurements possible, even at acquisition rates similar to or below the trap's response times.

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

What Fits into a Mirror: Naive Beliefs about the Field of View

ERIC Educational Resources Information Center

Bianchi, Ivana; Savardi, Ugo

2012-01-01

Research on naive physics and naive optics have shown that people hold surprising beliefs about everyday phenomena that are in contrast with what they see. In this article, we investigated what adults expect to be the field of view of a mirror from various viewpoints. The studies presented here confirm that humans have difficulty dealing with the…

Impaired processing speed and attention in first-episode drug naive schizophrenia with deficit syndrome.

PubMed

Chen, Ce; Jiang, Wenhui; Zhong, Na; Wu, Jin; Jiang, Haifeng; Du, Jiang; Li, Ye; Ma, Xiancang; Zhao, Min; Hashimoto, Kenji; Gao, Chengge

2014-11-01

Although first-episode drug naive patients with schizophrenia are known to show cognitive impairment, the cognitive performances of these patients, who suffer deficit syndrome, compared with those who suffer non-deficit syndrome is undetermined. The aim of this study was to compare cognitive performances in first-episode drug-naive schizophrenia with deficit syndrome or non-deficit syndrome. First-episode drug naive patients (n=49) and medicated patients (n=108) with schizophrenia, and age, sex, and education matched healthy controls (n=57 for the first-episode group, and n=128 for the medicated group) were enrolled. Patients were divided into deficit or non-deficit syndrome groups, using the Schedule for Deficit Syndrome. Cognitive performance was assessed using the CogState computerized cognitive battery. All cognitive domains in first-episode drug naive and medicated patients showed significant impairment compared with their respective control groups. Furthermore, cognitive performance in first-episode drug naive patients was significantly worse than in medicated patients. Interestingly, the cognitive performance markers of processing speed and attention, in first-episode drug naive patients with deficit syndrome, were both significantly worse than in equivalent patients without deficit syndrome. In contrast, no differences in cognitive performance were found between the two groups of medicated patients. In conclusion, this study found that first-episode drug naive schizophrenia with deficit syndrome showed significantly impaired processing speed and attention, compared with patients with non-deficit syndrome. These findings highlight processing speed and attention as potential targets for pharmacological and psychosocial interventions in first-episode schizophrenia with deficit syndrome, since these domains are associated with social outcomes. Copyright © 2014 Elsevier B.V. All rights reserved.

'Educated' dendritic cells act as messengers from memory to naive T helper cells.

PubMed

Alpan, Oral; Bachelder, Eric; Isil, Eda; Arnheiter, Heinz; Matzinger, Polly

2004-06-01

Ingested antigens lead to the generation of effector T cells that secrete interleukin 4 (IL-4) rather than interferon-gamma (IFN-gamma) and are capable of influencing naive T cells in their immediate environment to do the same. Using chimeric mice generated by aggregation of two genotypically different embryos, we found that the conversion of a naive T cell occurs only if it can interact with the same antigen-presenting cell, although not necessarily the same antigen, as the effector T cell. Using a two-step culture system in vitro, we found that antigen-presenting dendritic cells can act as 'temporal bridges' to relay information from orally immunized memory CD4 T cells to naive CD4 T cells. The orally immunized T cells use IL-4 and IL-10 (but not CD40 ligand) to 'educate' dendritic cells, which in turn induce naive T cells to produce the same cytokines as those produced by the orally immunized memory T cells.

Top predators affect the composition of naive protist communities, but only in their early-successional stage.

PubMed

Zander, Axel; Gravel, Dominique; Bersier, Louis-Félix; Gray, Sarah M

2016-02-01

Introduced top predators have the potential to disrupt community dynamics when prey species are naive to predation. The impact of introduced predators may also vary depending on the stage of community development. Early-succession communities are likely to have small-bodied and fast-growing species, but are not necessarily good at defending against predators. In contrast, late-succession communities are typically composed of larger-bodied species that are more predator resistant relative to small-bodied species. Yet, these aspects are greatly neglected in invasion studies. We therefore tested the effect of top predator presence on early- and late-succession communities that were either naive or non-naive to top predators. We used the aquatic community held within the leaves of Sarracenia purpurea. In North America, communities have experienced the S. purpurea top predator and are therefore non-naive. In Europe, this predator is not present and its niche has not been filled, making these communities top-predator naive. We collected early- and late-succession communities from two non-naive and two naive sites, which are climatically similar. We then conducted a common-garden experiment, with and without the presence of the top predator, in which we recorded changes in community composition, body size spectra, bacterial density, and respiration. We found that the top predator had no statistical effect on global measures of community structure and functioning. However, it significantly altered protist composition, but only in naive, early-succession communities, highlighting that the state of community development is important for understanding the impact of invasion.

Children and Adolescents' Understandings of Family Resemblance: A Study of Naive Inheritance Concepts

ERIC Educational Resources Information Center

Williams, Joanne M.

2012-01-01

This paper aims to provide developmental data on two connected naive inheritance concepts and to explore the coherence of children's naive biology knowledge. Two tasks examined children and adolescents' (4, 7, 10, and 14 years) conceptions of phenotypic resemblance across kin (in physical characteristics, disabilities, and personality traits). The…

Automated high resolution mapping of coffee in Rwanda using an expert Bayesian network

NASA Astrophysics Data System (ADS)

Mukashema, A.; Veldkamp, A.; Vrieling, A.

2014-12-01

African highland agro-ecosystems are dominated by small-scale agricultural fields that often contain a mix of annual and perennial crops. This makes such systems difficult to map by remote sensing. We developed an expert Bayesian network model to extract the small-scale coffee fields of Rwanda from very high resolution data. The model was subsequently applied to aerial orthophotos covering more than 99% of Rwanda and on one QuickBird image for the remaining part. The method consists of a stepwise adjustment of pixel probabilities, which incorporates expert knowledge on size of coffee trees and fields, and on their location. The initial naive Bayesian network, which is a spectral-based classification, yielded a coffee map with an overall accuracy of around 50%. This confirms that standard spectral variables alone cannot accurately identify coffee fields from high resolution images. The combination of spectral and ancillary data (DEM and a forest map) allowed mapping of coffee fields and associated uncertainties with an overall accuracy of 87%. Aggregated to district units, the mapped coffee areas demonstrated a high correlation with the coffee areas reported in the detailed national coffee census of 2009 (R2 = 0.92). Unlike the census data our map provides high spatial resolution of coffee area patterns of Rwanda. The proposed method has potential for mapping other perennial small scale cropping systems in the East African Highlands and elsewhere.

IL-7-Induced Proliferation of Human Naive CD4 T-Cells Relies on Continued Thymic Activity.

PubMed

Silva, Susana L; Albuquerque, Adriana S; Matoso, Paula; Charmeteau-de-Muylder, Bénédicte; Cheynier, Rémi; Ligeiro, Dário; Abecasis, Miguel; Anjos, Rui; Barata, João T; Victorino, Rui M M; Sousa, Ana E

2017-01-01

Naive CD4 T-cell maintenance is critical for immune competence. We investigated here the fine-tuning of homeostatic mechanisms of the naive compartment to counteract the loss of de novo CD4 T-cell generation. Adults thymectomized in early childhood during corrective cardiac surgery were grouped based on presence or absence of thymopoiesis and compared with age-matched controls. We found that the preservation of the CD31 - subset was independent of the thymus and that its size is tightly controlled by peripheral mechanisms, including prolonged cell survival as attested by Bcl-2 levels. Conversely, a significant contraction of the CD31 + naive subset was observed in the absence of thymic activity. This was associated with impaired responses of purified naive CD4 T-cells to IL-7, namely, in vitro proliferation and upregulation of CD31 expression, which likely potentiated the decline in recent thymic emigrants. Additionally, we found no apparent constraint in the differentiation of naive cells into the memory compartment in individuals completely lacking thymic activity despite upregulation of DUSP6 , a phosphatase associated with increased TCR threshold. Of note, thymectomized individuals featuring some degree of thymopoiesis were able to preserve the size and diversity of the naive CD4 compartment, further arguing against complete thymectomy in infancy. Overall, our data suggest that robust peripheral mechanisms ensure the homeostasis of CD31 - naive CD4 pool and point to the requirement of continuous thymic activity to the maintenance of IL-7-driven homeostatic proliferation of CD31 + naive CD4 T-cells, which is essential to secure T-cell diversity throughout life.

Bayesian data analysis for newcomers.

PubMed

Kruschke, John K; Liddell, Torrin M

2018-02-01

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

Bayesian and “Anti-Bayesian” Biases in Sensory Integration for Action and Perception in the Size–Weight Illusion

PubMed Central

Brayanov, Jordan B.

2010-01-01

Which is heavier: a pound of lead or a pound of feathers? This classic trick question belies a simple but surprising truth: when lifted, the pound of lead feels heavier—a phenomenon known as the size–weight illusion. To estimate the weight of an object, our CNS combines two imperfect sources of information: a prior expectation, based on the object's appearance, and direct sensory information from lifting it. Bayes' theorem (or Bayes' law) defines the statistically optimal way to combine multiple information sources for maximally accurate estimation. Here we asked whether the mechanisms for combining these information sources produce statistically optimal weight estimates for both perceptions and actions. We first studied the ability of subjects to hold one hand steady when the other removed an object from it, under conditions in which sensory information about the object's weight sometimes conflicted with prior expectations based on its size. Since the ability to steady the supporting hand depends on the generation of a motor command that accounts for lift timing and object weight, hand motion can be used to gauge biases in weight estimation by the motor system. We found that these motor system weight estimates reflected the integration of prior expectations with real-time proprioceptive information in a Bayesian, statistically optimal fashion that discounted unexpected sensory information. This produces a motor size–weight illusion that consistently biases weight estimates toward prior expectations. In contrast, when subjects compared the weights of two objects, their perceptions defied Bayes' law, exaggerating the value of unexpected sensory information. This produces a perceptual size–weight illusion that biases weight perceptions away from prior expectations. We term this effect “anti-Bayesian” because the bias is opposite that seen in Bayesian integration. Our findings suggest that two fundamentally different strategies for the integration of prior

The Proposal Auto-Categorizer and Manager for Time Allocation Review at the Space Telescope Science Institute

NASA Astrophysics Data System (ADS)

Strolger, Louis-Gregory; Porter, Sophia; Lagerstrom, Jill; Weissman, Sarah; Reid, I. Neill; Garcia, Michael

2017-04-01

The Proposal Auto-Categorizer and Manager (PACMan) tool was written to respond to concerns about subjective flaws and potential biases in some aspects of the proposal review process for time allocation for the Hubble Space Telescope (HST), and to partially alleviate some of the anticipated additional workload from the James Webb Space Telescope (JWST) proposal review. PACMan is essentially a mixed-method Naive Bayesian spam filtering routine, with multiple pools representing scientific categories, that utilizes the Robinson method for combining token (or word) probabilities. PACMan was trained to make similar programmatic decisions in science category sorting, panelist selection, and proposal-to-panelists assignments to those made by individuals and committees in the Science Policies Group (SPG) at the Space Telescope Science Institute. Based on training from the previous cycle’s proposals, at an average of 87%, PACMan made the same science category assignments for proposals in Cycle 24 as the SPG. Tests for similar science categorizations, based on training using proposals from additional cycles, show that this accuracy can be further improved, to the > 95 % level. This tool will be used to augment or replace key functions in the Time Allocation Committee review processes in future HST and JWST cycles.

Risk of erectile dysfunction in transfusion-naive thalassemia men: a nationwide population-based retrospective cohort study.

PubMed

Chen, Yu-Guang; Lin, Te-Yu; Lin, Cheng-Li; Dai, Ming-Shen; Ho, Ching-Liang; Kao, Chia-Hung

2015-04-01

Based on the mechanism of pathophysiology, thalassemia major or transfusion-dependent thalassemia patients may have an increased risk of developing organic erectile dysfunction resulting from hypogonadism. However, there have been few studies investigating the association between erectile dysfunction and transfusion-naive thalassemia populations. We constructed a population-based cohort study to elucidate the association between transfusion-naive thalassemia populations and organic erectile dysfunction. This nationwide population-based cohort study involved analyzing data from 1998 to 2010 obtained from the Taiwanese National Health Insurance Research Database, with a follow-up period extending to the end of 2011. We identified men with transfusion-naive thalassemia and selected a comparison cohort that was frequency-matched with these according to age, and year of diagnosis thalassemia at a ratio of 1 thalassemia man to 4 control men. We analyzed the risks for transfusion-naive thalassemia men and organic erectile dysfunction by using Cox proportional hazards regression models. In this study, 588 transfusion-naive thalassemia men and 2337 controls were included. Total 12 patients were identified within the thalassaemia group and 10 within the control group. The overall risks for developing organic erectile dysfunction were 4.56-fold in patients with transfusion-naive thalassemia men compared with the comparison cohort after we adjusted for age and comorbidities. Our long-term cohort study results showed that in transfusion-naive thalassemia men, there was a higher risk for the development of organic erectile dysfunction, particularly in those patients with comorbidities.

Evaluation of the impact of chitosan/DNA nanoparticles on the differentiation of human naive CD4+ T cells

NASA Astrophysics Data System (ADS)

Liu, Lanxia; Bai, Yuanyuan; Zhu, Dunwan; Song, Liping; Wang, Hai; Dong, Xia; Zhang, Hailing; Leng, Xigang

2011-06-01

Chitosan (CS) is one of the most widely studied polymers in non-viral gene delivery since it is a cationic polysaccharide that forms nanoparticles with DNA and hence protects the DNA against digestion by DNase. However, the impact of CS/DNA nanoparticle on the immune system still remains poorly understood. Previous investigations did not found CS/DNA nanoparticles had any significant impact on the function of human and murine macrophages. To date, little is known about the interaction between CS/DNA nanoparticles and naive CD4+ T cells. This study was designed to investigate whether CS/DNA nanoparticles affect the initial differentiation direction of human naive CD4+ T cells. The indirect impact of CS/DNA nanoparticles on naive CD4+ T cell differentiation was investigated by incubating the nanoparticles with human macrophage THP-1 cells in one chamber of a transwell co-incubation system, with the enriched human naive CD4+ T cells being placed in the other chamber of the transwell. The nanoparticles were also co-incubated with the naive CD4+ T cells to explore their direct impact on naive CD4+ T cell differentiation by measuring the release of IL-4 and IFN-γ from the cells. It was demonstrated that CS/DNA nanoparticles induced slightly elevated production of IL-12 by THP-1 cells, possibly owing to the presence of CpG motifs in the plasmid. However, this macrophage stimulating activity was much less significant as compared with lipopolysaccharide and did not impact on the differentiation of the naive CD4+ T cells. It was also demonstrated that, when directly exposed to the naive CD4+ T cells, the nanoparticles induced neither the activation of the naive CD4+ T cells in the absence of recombinant cytokines (recombinant human IL-4 or IFN-γ) that induce naive CD4+ T cell polarization, nor any changes in the differentiation direction of naive CD4+ T cells in the presence of the corresponding cytokines.

Bayesian Probability Theory

NASA Astrophysics Data System (ADS)

von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo

2014-06-01

Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.

Improving Naive Bayes with Online Feature Selection for Quick Adaptation to Evolving Feature Usefulness

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

Pon, R K; Cardenas, A F; Buttler, D J

The definition of what makes an article interesting varies from user to user and continually evolves even for a single user. As a result, for news recommendation systems, useless document features can not be determined a priori and all features are usually considered for interestingness classification. Consequently, the presence of currently useless features degrades classification performance [1], particularly over the initial set of news articles being classified. The initial set of document is critical for a user when considering which particular news recommendation system to adopt. To address these problems, we introduce an improved version of the naive Bayes classifiermore » with online feature selection. We use correlation to determine the utility of each feature and take advantage of the conditional independence assumption used by naive Bayes for online feature selection and classification. The augmented naive Bayes classifier performs 28% better than the traditional naive Bayes classifier in recommending news articles from the Yahoo! RSS feeds.« less

CD95 co-stimulation blocks activation of naive T cells by inhibiting T cell receptor signaling

PubMed Central

Lindquist, Jonathan A.; Arhel, Nathalie; Felder, Edward; Karl, Sabine; Haas, Tobias L.; Fulda, Simone; Walczak, Henning; Kirchhoff, Frank; Debatin, Klaus-Michael

2009-01-01

CD95 is a multifunctional receptor that induces cell death or proliferation depending on the signal, cell type, and cellular context. Here, we describe a thus far unknown function of CD95 as a silencer of T cell activation. Naive human T cells triggered by antigen-presenting cells expressing a membrane-bound form of CD95 ligand (CD95L) or stimulated by anti-CD3 and -CD28 antibodies in the presence of recombinant CD95L had reduced activation and proliferation, whereas preactivated, CD95-sensitive T cells underwent apoptosis. Triggering of CD95 during T cell priming interfered with proximal T cell receptor signaling by inhibiting the recruitment of ζ-chain–associated protein of 70 kD, phospholipase-γ, and protein kinase C-θ into lipid rafts, thereby preventing their mutual tyrosine protein phosphorylation. Subsequently, Ca2+ mobilization and nuclear translocation of transcription factors NFAT, AP1, and NF-κB were strongly reduced, leading to impaired cytokine secretion. CD95-mediated inhibition of proliferation in naive T cells could not be reverted by the addition of exogenous interleukin-2 and T cells primed by CD95 co-stimulation remained partially unresponsive upon secondary T cell stimulation. HIV infection induced CD95L expression in primary human antigeen-presenting cells, and thereby suppressed T cell activation, suggesting that CD95/CD95L-mediated silencing of T cell activation represents a novel mechanism of immune evasion. PMID:19487421

The Hayflick Limit May Determine the Effective Clonal Diversity of Naive T Cells.

PubMed

Ndifon, Wilfred; Dushoff, Jonathan

2016-06-15

Having a large number of sufficiently abundant T cell clones is important for adequate protection against diseases. However, as shown in this paper and elsewhere, between young adulthood and >70 y of age the effective clonal diversity of naive CD4/CD8 T cells found in human blood declines by a factor of >10. (Effective clonal diversity accounts for both the number and the abundance of T cell clones.) The causes of this observation are incompletely understood. A previous study proposed that it might result from the emergence of certain rare, replication-enhancing mutations in T cells. In this paper, we propose an even simpler explanation: that it results from the loss of T cells that have attained replicative senescence (i.e., the Hayflick limit). Stochastic numerical simulations of naive T cell population dynamics, based on experimental parameters, show that the rate of homeostatic T cell proliferation increases after the age of ∼60 y because naive T cells collectively approach replicative senescence. This leads to a sharp decline of effective clonal diversity after ∼70 y, in agreement with empirical data. A mathematical analysis predicts that, without an increase in the naive T cell proliferation rate, this decline will occur >50 yr later than empirically observed. These results are consistent with a model in which exhaustion of the proliferative capacity of naive T cells causes a sharp decline of their effective clonal diversity and imply that therapeutic potentiation of thymopoiesis might either prevent or reverse this outcome. Copyright © 2016 by The American Association of Immunologists, Inc.

A novel approach to describing and detecting performance anti-patterns

NASA Astrophysics Data System (ADS)

Sheng, Jinfang; Wang, Yihan; Hu, Peipei; Wang, Bin

2017-08-01

Anti-pattern, as an extension to pattern, describes a widely used poor solution which can bring negative influence to application systems. Aiming at the shortcomings of the existing anti-pattern descriptions, an anti-pattern description method based on first order predicate is proposed. This method synthesizes anti-pattern forms and symptoms, which makes the description more accurate and has good scalability and versatility as well. In order to improve the accuracy of anti-pattern detection, a Bayesian classification method is applied in validation for detection results, which can reduce false negatives and false positives of anti-pattern detection. Finally, the proposed approach in this paper is applied to a small e-commerce system, the feasibility and effectiveness of the approach is demonstrated further through experiments.

Naive B cells generate regulatory T cells in the presence of a mature immunologic synapse.

PubMed

Reichardt, Peter; Dornbach, Bastian; Rong, Song; Beissert, Stefan; Gueler, Faikah; Loser, Karin; Gunzer, Matthias

2007-09-01

Naive B cells are ineffective antigen-presenting cells and are considered unable to activate naive T cells. However, antigen-specific contact of these cells leads to stable cell pairs that remain associated over hours in vivo. The physiologic role of such pairs has not been evaluated. We show here that antigen-specific conjugates between naive B cells and naive T cells display a mature immunologic synapse in the contact zone that is absent in T-cell-dendritic-cell (DC) pairs. B cells induce substantial proliferation but, contrary to DCs, no loss of L-selectin in T cells. Surprisingly, while DC-triggered T cells develop into normal effector cells, B-cell stimulation over 72 hours induces regulatory T cells inhibiting priming of fresh T cells in a contact-dependent manner in vitro. In vivo, the regulatory T cells home to lymph nodes where they potently suppress immune responses such as in cutaneous hypersensitivity and ectopic allogeneic heart transplant rejection. Our finding might help to explain old observations on tolerance induction by B cells, identify the mature immunologic synapse as a central functional module of this process, and suggest the use of naive B-cell-primed regulatory T cells, "bTregs," as a useful approach for therapeutic intervention in adverse adaptive immune responses.

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…

Clinical Outcomes of Golimumab as First, Second or Third Anti-TNF Agent in Patients with Moderate-to-Severe Ulcerative Colitis.

PubMed

Taxonera, Carlos; Rodríguez, Cristina; Bertoletti, Federico; Menchén, Luís; Arribas, Julia; Sierra, Mónica; Arias, Lara; Martínez-Montiel, Pilar; Juan, Alba; Iglesias, Eva; Algaba, Alicia; Manceñido, Noemí; Rivero, Montserrat; Barreiro-de Acosta, Manuel; López-Serrano, Pilar; Argüelles-Arias, Federico; Gutierrez, Ana; Busquets, David; Gisbert, Javier P; Olivares, David; Calvo, Marta; Alba, Cristina

2017-08-01

Golimumab efficacy data in ulcerative colitis (UC) are limited to anti-tumor necrosis factor α (TNF)-naive patients. The aim of this study was to assess the short-term and long-term efficacy of golimumab used as first, second, or third anti-TNF in UC in a real-life clinical setting. This retrospective multicenter cohort study included patients with moderate-to-severe UC treated with golimumab. The primary efficacy endpoints were short-term partial Mayo score response, long-term golimumab failure-free survival, and colectomy-free survival. In 142 patients with UC, golimumab was administered as first (40%), second (23%), or third anti-TNF (37%). Ninety-two patients (65%, 95% confidence interval 56.6-73) achieved short-term clinical response. Forty-five patients (32%, 95% confidence interval 23.7-39.7) achieved clinical remission. Response rates for golimumab were 75% as first anti-TNF, 70% as second anti-TNF (ns versus first anti-TNF), and 50% as third anti-TNF (P = 0.007 versus first anti-TNF). After 12 months median follow-up (interquartile range 6-18), 60 patients (42%, 95% confidence interval 34-51) had golimumab failure, and 15 patients (11%) needed colectomy. Thirty-one patients (22%) needed golimumab dose escalation, and 71% of these regained response after escalation. Starting maintenance with 100 mg golimumab doses and short-term nonresponse were independent predictors of golimumab failure. In this real-life cohort of patients with UC, golimumab therapy was effective for inducing and maintaining clinical response. Although anti-TNF-naive patients had better outcomes, golimumab was also effective in anti-TNF-experienced patients. Only the patients given golimumab after previous failure of 2 anti-TNF agents had significantly worse outcomes. Golimumab dose escalation was beneficial and safe.

Naive T-cell receptor transgenic T cells help memory B cells produce antibody

PubMed Central

Duffy, Darragh; Yang, Chun-Ping; Heath, Andrew; Garside, Paul; Bell, Eric B

2006-01-01

Injection of the same antigen following primary immunization induces a classic secondary response characterized by a large quantity of high-affinity antibody of an immunoglobulin G class produced more rapidly than in the initial response – the products of memory B cells are qualitatively distinct from that of the original naive B lymphocytes. Very little is known of the help provided by the CD4 T cells that stimulate memory B cells. Using antigen-specific T-cell receptor transgenic CD4 T cells (DO11.10) as a source of help, we found that naive transgenic T cells stimulated memory B cells almost as well (in terms of quantity and speed) as transgenic T cells that had been recently primed. There was a direct correlation between serum antibody levels and the number of naive transgenic T cells transferred. Using T cells from transgenic interleukin-2-deficient mice we showed that interleukin-2 was not required for a secondary response, although it was necessary for a primary response. The results suggested that the signals delivered by CD4 T cells and required by memory B cells for their activation were common to both antigen-primed and naive CD4 T cells. PMID:17067314

IL-21 sustains CD28 expression on IL-15-activated human naive CD8+ T cells.

PubMed

Alves, Nuno L; Arosa, Fernando A; van Lier, René A W

2005-07-15

Human naive CD8+ T cells are able to respond in an Ag-independent manner to IL-7 and IL-15. Whereas IL-7 largely maintains CD8+ T cells in a naive phenotype, IL-15 drives these cells to an effector phenotype characterized, among other features, by down-regulation of the costimulatory molecule CD28. We evaluated the influence of the CD4+ Th cell-derived common gamma-chain cytokine IL-21 on cytokine-induced naive CD8+ T cell activation. Stimulation with IL-21 did not induce division and only slightly increased IL-15-induced proliferation of naive CD8+ T cells. Strikingly, however, IL-15-induced down-modulation of CD28 was completely prevented by IL-21 at the protein and transcriptional level. Subsequent stimulation via combined TCR/CD3 and CD28 triggering led to a markedly higher production of IL-2 and IFN-gamma in IL-15/IL-21-stimulated cells compared with IL-15-stimulated T cells. Our data show that IL-21 modulates the phenotype of naive CD8+ T cells that have undergone IL-15 induced homeostatic proliferation and preserves their responsiveness to CD28 ligands.

Left Ventricular Strain in Chemotherapy-Naive and Radiotherapy-Naive Patients With Cancer.

PubMed

Tadic, Marijana; Genger, Martin; Baudisch, Ana; Kelle, Sebastian; Cuspidi, Cesare; Belyavskiy, Evgeny; Burkhardt, Franziska; Venneri, Lucia; Attanasio, Philipp; Pieske, Burkert

2018-03-01

We sought to investigate left ventricular (LV) function and mechanics in patients with cancer before they received chemotherapy or radiotherapy, as well as the relationship between cancer and reduced LV multidirectional strain in the whole study population. The retrospective study involved 122 chemotherapy- and radiotherapy-naive patients with cancer and 45 age- and sex-matched controls with a cardiovascular risk profile similar to that of the patients with cancer. All the patients underwent echocardiographic examination before introduction of chemotherapy or radiotherapy. LV longitudinal (-19.1% ± 2.1% vs -17.8% ± 3.5%; P = 0.022), circumferential (-22.9% ± 3.5% vs -20.1% ± 4.1%; P < 0.001), and radial (40.5% ± 8.8% vs 35.2% ± 10.7%; P = 0.004) strain was significantly lower in the patients with cancer than in the control group. Endocardial and midmyocardial longitudinal LV strain was significantly reduced in the patients with cancer compared with the controls, whereas epicardial longitudinal strain was similar between these groups. Endocardial, midmyocardial, and epicardial circumferential strain was significantly lower in the chemotherapy- or radiotherapy-naive patients with cancer than in the controls. Cancer was associated with reduced longitudinal (odds ratio [OR], 9.0; 95% confidence interval [CI], 2.20-23.50; P < 0.001), reduced circumferential (OR, 7.1; 95% CI, 3.80-20.40; P < 0.001), and reduced radial strain (OR, 7.2; 95% CI, 3.41-25.10; P < 0.001) independent of age, sex, body mass index, diabetes, and hypertension. LV mechanics was impaired in the patients with cancer compared with the controls even before initiation of chemotherapy and radiotherapy. Cancer and hypertension were associated with reduced LV multidirectional strain independent of other clinical parameters. The present results indicate that cancer itself potentially induces cardiac remodelling independent of chemotherapy and radiotherapy. Copyright © 2017 Canadian

Touring DNS Open Houses for Trends and Configurations

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

Kalafut, Prof. Andrew; Shue, Craig A; Gupta, Prof. Minaxi

2011-01-01

DNS is a critical component of the Internet. It maps domain names to IP addresses and serves as a distributed database for various other applications, including mail, Web, and spam filtering. This paper examines DNS zones in the Internet for diversity, adoption rates of new technologies, and prevalence of configuration issues. To gather data, we sweep 60% of the Internet's domains in June - August 2007 for zone transfers. 6.6% of them allow us to transfer their complete information. Surprisingly, this includes a large fraction of the domains deploying DNSSEC. We find that DNS zones vary significantly in size andmore » some span many ASes. Also, while anti-spam technologies appear to be getting deployed, the adoption rates of DNSSEC and IPv6 continue to be low. Finally, we also find that carelessness in handing DNS records can lead to reduced availability of name servers, email, and Web servers. This also undermines anti-spam efforts and the efforts to shut down phishing sites or to contain malware infections.« less

Dynamic Dimensionality Selection for Bayesian Classifier Ensembles

DTIC Science & Technology

2015-03-19

learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but much more...classifier, Generative learning, Discriminative learning, Naïve Bayes, Feature selection, Logistic regression , higher order attribute independence 16...discriminative learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but

Children's Conceptions of Mental Illness: A Naive Theory Approach

ERIC Educational Resources Information Center

Fox, Claudine; Buchanan-Barrow, Eithne; Barrett, Martyn

2010-01-01

This paper reports two studies that investigated children's conceptions of mental illness using a naive theory approach, drawing upon a conceptual framework for analysing illness representations which distinguishes between the identity, causes, consequences, curability, and timeline of an illness. The studies utilized semi-structured interviewing…

Factors Contributing to the Preference of Korean Patients with Crohn’s Disease When Selecting an Anti-Tumor Necrosis Factor Agent (CHOICE Study)

PubMed Central

Kim, Eun Soo; Kim, Kyeong Ok; Jang, Byung Ik; Lee, Chang Kyun; Kim, Hyo Jong; Lee, Kang-Moon; Kim, You Sun; Eun, Chang Soo; Jung, Sung-Ae; Yang, Suk-Kyun; Lee, Jun; Kim, Tae-Oh; Jung, Yunho; Seo, Geom Seog; Yoon, Soon Man

2016-01-01

Background/Aims Two comparable anti-tumor necrosis factor (TNF) agents with different routes of administration (intravenous [iv] infliximab [IFX] vs subcutaneous [sc] adalimumab [ADA]) are available for patients with Crohn’s disease (CD) in Korea. This study aimed to identify the preferences of Korean CD patients for a specific anti-TNF agent and the factors contributing to the decision. Methods A prospective survey was performed among anti-TNF-naive CD patients in 10 tertiary referral hospitals. A 16-item questionnaire addressed patient preferences and the factors contributing to the decision in favor of a particular anti-TNF agent. A logistic regression was conducted to assess predictive factors for ADA preference. Results Overall, 189 patients (139 males; mean age, 32.47±11.71 years) completed the questionnaire. IFX and ADA were preferred by 63.5% (120/189) and 36.5% (69/189) of patients, respectively. The most influential reason for choosing IFX was ‘doctor’s presence’ (68.3%, 82/120), and ADA was “easy to use” (34.8%, 24/69). Amid various clinicodemographic data, having a >60-minute travel time to the hospital was a significant independent predictive factor for ADA preference. Conclusions A large number of anti-TNF-naive Korean patients with CD preferred anti-TNFs with an iv route of administration. The reassuring effect of a doctor’s presence might be the main contributing factor for this decision. PMID:26347512

Mathematical Model of Naive T Cell Division and Survival IL-7 Thresholds.

PubMed

Reynolds, Joseph; Coles, Mark; Lythe, Grant; Molina-París, Carmen

2013-01-01

We develop a mathematical model of the peripheral naive T cell population to study the change in human naive T cell numbers from birth to adulthood, incorporating thymic output and the availability of interleukin-7 (IL-7). The model is formulated as three ordinary differential equations: two describe T cell numbers, in a resting state and progressing through the cell cycle. The third is introduced to describe changes in IL-7 availability. Thymic output is a decreasing function of time, representative of the thymic atrophy observed in aging humans. Each T cell is assumed to possess two interleukin-7 receptor (IL-7R) signaling thresholds: a survival threshold and a second, higher, proliferation threshold. If the IL-7R signaling strength is below its survival threshold, a cell may undergo apoptosis. When the signaling strength is above the survival threshold, but below the proliferation threshold, the cell survives but does not divide. Signaling strength above the proliferation threshold enables entry into cell cycle. Assuming that individual cell thresholds are log-normally distributed, we derive population-average rates for apoptosis and entry into cell cycle. We have analyzed the adiabatic change in homeostasis as thymic output decreases. With a parameter set representative of a healthy individual, the model predicts a unique equilibrium number of T cells. In a parameter range representative of persistent viral or bacterial infection, where naive T cell cycle progression is impaired, a decrease in thymic output may result in the collapse of the naive T cell repertoire.

Bayesian Mediation Analysis

ERIC Educational Resources Information Center

Yuan, Ying; MacKinnon, David P.

2009-01-01

In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…

Analysis and implementation of cross lingual short message service spam filtering using graph-based k-nearest neighbor

NASA Astrophysics Data System (ADS)

Ayu Cyntya Dewi, Dyah; Shaufiah; Asror, Ibnu

2018-03-01

SMS (Short Message Service) is on e of the communication services that still be the main choice, although now the phone grow with various applications. Along with the development of various other communication media, some countries lowered SMS rates to keep the interest of mobile users. It resulted in increased spam SMS that used by several parties, one of them for advertisement. Given the kind of multi-lingual documents in a message SMS, the Web, and others, necessary for effective multilingual or cross-lingual processing techniques is becoming increasingly important. The steps that performed in this research is data / messages first preprocessing then represented into a graph model. Then calculated using GKNN method. From this research we get the maximum accuracy is 98.86 with training data in Indonesian language and testing data in indonesian language with K 10 and threshold 0.001.

Highly efficient gene transfer in naive human T cells with a murine leukemia virus-based vector.

PubMed

Dardalhon, V; Jaleco, S; Rebouissou, C; Ferrand, C; Skander, N; Swainson, L; Tiberghien, P; Spits, H; Noraz, N; Taylor, N

2000-08-01

Retroviral vectors based on the Moloney murine leukemia virus (MuLV) have become the primary tool for gene delivery into hematopoietic cells, but clinical trials have been hampered by low transduction efficiencies. Recently, we and others have shown that gene transfer of MuLV-based vectors into T cells can be significantly augmented using a fibronectin-facilitated protocol. Nevertheless, the relative abilities of naive (CD45RA(+)) and memory (CD45RO(+)) lymphocyte subsets to be transduced has not been assessed. Although naive T cells demonstrate a restricted cytokine profile following antigen stimulation and a decreased susceptibility to infection with human immunodeficiency virus, it was not clear whether they could be efficiently infected with a MuLV vector. This study describes conditions that permitted gene transfer of an enhanced green fluorescent protein-expressing retroviral vector in more than 50% of naive umbilical cord (UC) blood and peripheral blood (PB) T cells following CD3/CD28 ligation. Moreover, treatment of naive T cells with interleukin-7 resulted in the maintenance of a CD45RA phenotype and gene transfer levels approached 20%. Finally, it was determined that parameters for optimal transduction of CD45RA(+) T cells isolated from PB and UC blood differed: transduction of the UC cells was significantly increased by the presence of autologous mononuclear cells (24.5% versus 56.5%). Because naive T cells harbor a receptor repertoire that allows them to respond to novel antigens, the development of protocols targeting their transduction is crucial for gene therapy applications. This approach will also allow the functions of exogenous genes to be evaluated in primary nontransformed naive T cells.

Naive vs. Sophisticated Methods of Forecasting Public Library Circulations.

ERIC Educational Resources Information Center

Brooks, Terrence A.

1984-01-01

Two sophisticated--autoregressive integrated moving average (ARIMA), straight-line regression--and two naive--simple average, monthly average--forecasting techniques were used to forecast monthly circulation totals of 34 public libraries. Comparisons of forecasts and actual totals revealed that ARIMA and monthly average methods had smallest mean…

Practical Bayesian tomography

NASA Astrophysics Data System (ADS)

Granade, Christopher; Combes, Joshua; Cory, D. G.

2016-03-01

In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of-the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a lack of informative prior distributions, and an inability to track time-dependent processes. Here, we address all three problems. First, we use modern statistical methods, as pioneered by Huszár and Houlsby (2012 Phys. Rev. A 85 052120) and by Ferrie (2014 New J. Phys. 16 093035), to make Bayesian tomography numerically tractable. Our approach allows for practical computation of Bayesian point and region estimators for quantum states and channels. Second, we propose the first priors on quantum states and channels that allow for including useful experimental insight. Finally, we develop a method that allows tracking of time-dependent states and estimates the drift and diffusion processes affecting a state. We provide source code and animated visual examples for our methods.

Bayesian power spectrum inference with foreground and target contamination treatment

NASA Astrophysics Data System (ADS)

Jasche, J.; Lavaux, G.

2017-10-01

This work presents a joint and self-consistent Bayesian treatment of various foreground and target contaminations when inferring cosmological power spectra and three-dimensional density fields from galaxy redshift surveys. This is achieved by introducing additional block-sampling procedures for unknown coefficients of foreground and target contamination templates to the previously presented ARES framework for Bayesian large-scale structure analyses. As a result, the method infers jointly and fully self-consistently three-dimensional density fields, cosmological power spectra, luminosity-dependent galaxy biases, noise levels of the respective galaxy distributions, and coefficients for a set of a priori specified foreground templates. In addition, this fully Bayesian approach permits detailed quantification of correlated uncertainties amongst all inferred quantities and correctly marginalizes over observational systematic effects. We demonstrate the validity and efficiency of our approach in obtaining unbiased estimates of power spectra via applications to realistic mock galaxy observations that are subject to stellar contamination and dust extinction. While simultaneously accounting for galaxy biases and unknown noise levels, our method reliably and robustly infers three-dimensional density fields and corresponding cosmological power spectra from deep galaxy surveys. Furthermore, our approach correctly accounts for joint and correlated uncertainties between unknown coefficients of foreground templates and the amplitudes of the power spectrum. This effect amounts to correlations and anti-correlations of up to 10 per cent across wide ranges in Fourier space.

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 demography 250 years after Bayes

PubMed Central

Bijak, Jakub; Bryant, John

2016-01-01

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms. PMID:26902889

Telomerase Is Involved in IL-7-Mediated Differential Survival of Naive and Memory CD4+ T Cells1

PubMed Central

Yang, Yinhua; An, Jie; Weng, Nan-ping

2008-01-01

IL-7 plays an essential role in T cell maintenance and survival. The survival effect of IL-7 is thought to be mediated through regulation of Bcl2 family proteins. After a comparative analysis of IL-7-induced growth and cell death of human naive and memory CD4+ T cells, we observed that more memory CD4+ T cells underwent cell division and proceeded to apoptosis than naive cells in response to IL-7. However, IL-7-induced expressions of Bcl2 family members (Bcl2, Bcl-xL, Bax, and Bad) were similar between naive and memory cells. Instead, we found that IL-7 induced higher levels of telomerase activity in naive cells than in memory cells, and the levels of IL-7-induced telomerase activity had a significant inverse correlation with cell death in CD4+ T cells. Furthermore, we showed that reducing expression of telomerase reverse transcriptase and telomerase activity significantly increased cell death of IL-7-cultured CD4+ T cells. Together, these findings demonstrate that telomerase is involved in IL-7-mediated differential survival of naive and memory CD4+ T cells. PMID:18322183

Bayesian Variable Selection for Hierarchical Gene-Environment and Gene-Gene Interactions

PubMed Central

Liu, Changlu; Ma, Jianzhong; Amos, Christopher I.

2014-01-01

We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions and gene by environment interactions in the same model. Our approach incorporates the natural hierarchical structure between the main effects and interaction effects into a mixture model, such that our methods tend to remove the irrelevant interaction effects more effectively, resulting in more robust and parsimonious models. We consider both strong and weak hierarchical models. For a strong hierarchical model, both of the main effects between interacting factors must be present for the interactions to be considered in the model development, while for a weak hierarchical model, only one of the two main effects is required to be present for the interaction to be evaluated. Our simulation results show that the proposed strong and weak hierarchical mixture models work well in controlling false positive rates and provide a powerful approach for identifying the predisposing effects and interactions in gene-environment interaction studies, in comparison with the naive model that does not impose this hierarchical constraint in most of the scenarios simulated. We illustrated our approach using data for lung cancer and cutaneous melanoma. PMID:25154630

Solving graph data issues using a layered architecture approach with applications to web spam detection.

PubMed

Scarselli, Franco; Tsoi, Ah Chung; Hagenbuchner, Markus; Noi, Lucia Di

2013-12-01

This paper proposes the combination of two state-of-the-art algorithms for processing graph input data, viz., the probabilistic mapping graph self organizing map, an unsupervised learning approach, and the graph neural network, a supervised learning approach. We organize these two algorithms in a cascade architecture containing a probabilistic mapping graph self organizing map, and a graph neural network. We show that this combined approach helps us to limit the long-term dependency problem that exists when training the graph neural network resulting in an overall improvement in performance. This is demonstrated in an application to a benchmark problem requiring the detection of spam in a relatively large set of web sites. It is found that the proposed method produces results which reach the state of the art when compared with some of the best results obtained by others using quite different approaches. A particular strength of our method is its applicability towards any input domain which can be represented as a graph. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Psychology of Bayesian Reasoning

DTIC Science & Technology

2014-10-21

The psychology of Bayesian reasoning David R. Mandel* Socio-Cognitive Systems Section, Defence Research and Development Canada and Department...belief revision, subjective probability, human judgment, psychological methods. Most psychological research on Bayesian reasoning since the 1970s has...attention to some important problems with the conventional approach to studying Bayesian reasoning in psychology that has been dominant since the

Visualizing Non Infectious and Infectious Anopheles gambiae Blood Feedings in Naive and Saliva-Immunized Mice

PubMed Central

Choumet, Valerie; Attout, Tarik; Chartier, Loïc; Khun, Huot; Sautereau, Jean; Robbe-Vincent, Annie; Brey, Paul; Huerre, Michel; Bain, Odile

2012-01-01

Background Anopheles gambiae is a major vector of malaria and lymphatic filariasis. The arthropod-host interactions occurring at the skin interface are complex and dynamic. We used a global approach to describe the interaction between the mosquito (infected or uninfected) and the skin of mammals during blood feeding. Methods Intravital video microscopy was used to characterize several features during blood feeding. The deposition and movement of Plasmodium berghei sporozoites in the dermis were also observed. We also used histological techniques to analyze the impact of infected and uninfected feedings on the skin cell response in naive mice. Results The mouthparts were highly mobile within the skin during the probing phase. Probing time increased with mosquito age, with possible effects on pathogen transmission. Repletion was achieved by capillary feeding. The presence of sporozoites in the salivary glands modified the behavior of the mosquitoes, with infected females tending to probe more than uninfected females (86% versus 44%). A white area around the tip of the proboscis was observed when the mosquitoes fed on blood from the vessels of mice immunized with saliva. Mosquito feedings elicited an acute inflammatory response in naive mice that peaked three hours after the bite. Polynuclear and mast cells were associated with saliva deposits. We describe the first visualization of saliva in the skin by immunohistochemistry (IHC) with antibodies directed against saliva. Both saliva deposits and sporozoites were detected in the skin for up to 18 h after the bite. Conclusion This study, in which we visualized the probing and engorgement phases of Anopheles gambiae blood meals, provides precise information about the behavior of the insect as a function of its infection status and the presence or absence of anti-saliva antibodies. It also provides insight into the possible consequences of the inflammatory reaction for blood feeding and pathogen transmission. PMID

Visualizing non infectious and infectious Anopheles gambiae blood feedings in naive and saliva-immunized mice.

PubMed

Choumet, Valerie; Attout, Tarik; Chartier, Loïc; Khun, Huot; Sautereau, Jean; Robbe-Vincent, Annie; Brey, Paul; Huerre, Michel; Bain, Odile

2012-01-01

Anopheles gambiae is a major vector of malaria and lymphatic filariasis. The arthropod-host interactions occurring at the skin interface are complex and dynamic. We used a global approach to describe the interaction between the mosquito (infected or uninfected) and the skin of mammals during blood feeding. Intravital video microscopy was used to characterize several features during blood feeding. The deposition and movement of Plasmodium berghei sporozoites in the dermis were also observed. We also used histological techniques to analyze the impact of infected and uninfected feedings on the skin cell response in naive mice. The mouthparts were highly mobile within the skin during the probing phase. Probing time increased with mosquito age, with possible effects on pathogen transmission. Repletion was achieved by capillary feeding. The presence of sporozoites in the salivary glands modified the behavior of the mosquitoes, with infected females tending to probe more than uninfected females (86% versus 44%). A white area around the tip of the proboscis was observed when the mosquitoes fed on blood from the vessels of mice immunized with saliva. Mosquito feedings elicited an acute inflammatory response in naive mice that peaked three hours after the bite. Polynuclear and mast cells were associated with saliva deposits. We describe the first visualization of saliva in the skin by immunohistochemistry (IHC) with antibodies directed against saliva. Both saliva deposits and sporozoites were detected in the skin for up to 18 h after the bite. This study, in which we visualized the probing and engorgement phases of Anopheles gambiae blood meals, provides precise information about the behavior of the insect as a function of its infection status and the presence or absence of anti-saliva antibodies. It also provides insight into the possible consequences of the inflammatory reaction for blood feeding and pathogen transmission.

Basics of Bayesian methods.

PubMed

Ghosh, Sujit K

2010-01-01

Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.

Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem

NASA Astrophysics Data System (ADS)

Rahmadani, S.; Dongoran, A.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses the problem of feature selection using genetic algorithms on a dataset for classification problems. The classification model used is the decicion tree (DT), and Naive Bayes. In this paper we will discuss how the Naive Bayes and Decision Tree models to overcome the classification problem in the dataset, where the dataset feature is selectively selected using GA. Then both models compared their performance, whether there is an increase in accuracy or not. From the results obtained shows an increase in accuracy if the feature selection using GA. The proposed model is referred to as GADT (GA-Decision Tree) and GANB (GA-Naive Bayes). The data sets tested in this paper are taken from the UCI Machine Learning repository.

Ensemble of Chaotic and Naive Approaches for Performance Enhancement in Video Encryption.

PubMed

Chandrasekaran, Jeyamala; Thiruvengadam, S J

2015-01-01

Owing to the growth of high performance network technologies, multimedia applications over the Internet are increasing exponentially. Applications like video conferencing, video-on-demand, and pay-per-view depend upon encryption algorithms for providing confidentiality. Video communication is characterized by distinct features such as large volume, high redundancy between adjacent frames, video codec compliance, syntax compliance, and application specific requirements. Naive approaches for video encryption encrypt the entire video stream with conventional text based cryptographic algorithms. Although naive approaches are the most secure for video encryption, the computational cost associated with them is very high. This research work aims at enhancing the speed of naive approaches through chaos based S-box design. Chaotic equations are popularly known for randomness, extreme sensitivity to initial conditions, and ergodicity. The proposed methodology employs two-dimensional discrete Henon map for (i) generation of dynamic and key-dependent S-box that could be integrated with symmetric algorithms like Blowfish and Data Encryption Standard (DES) and (ii) generation of one-time keys for simple substitution ciphers. The proposed design is tested for randomness, nonlinearity, avalanche effect, bit independence criterion, and key sensitivity. Experimental results confirm that chaos based S-box design and key generation significantly reduce the computational cost of video encryption with no compromise in security.

Naive (commonsense) geography and geobrowser usability after ten years of Google Earth

NASA Astrophysics Data System (ADS)

Hamerlinck, J. D.

2016-04-01

In 1995, the concept of ‘naive geography’ was formally introduced as an area of cognitive geographic information science representing ‘the body of knowledge that people have about the surrounding geographic world’ and reflecting ‘the way people think and reason about geographic space and time, both consciously and subconsciously’. The need to incorporate such commonsense knowledge and reasoning into design of geospatial technologies was identified but faced challenges in formalizing these relationships and processes in software implementation. Ten years later, the Google Earth geobrowser was released, marking the beginning of a new era of open access to, and application of, geographic data and information in society. Fast-forward to today, and the opportunity presents itself to take stock of twenty years of naive geography and a decade of the ubiquitous virtual globe. This paper introduces an ongoing research effort to explore the integration of naive (or commonsense) geography concepts in the Google Earth geobrowser virtual globe and their possible impact on Google Earth's usability, utility, and usefulness. A multi-phase methodology is described, combining usability reviews and usability testing with use-case scenarios involving the U.S.-Canadian Yellowstone to Yukon Initiative. Initial progress on a usability review combining cognitive walkthroughs and heuristics evaluation is presented.

Ensemble of Chaotic and Naive Approaches for Performance Enhancement in Video Encryption

PubMed Central

Chandrasekaran, Jeyamala; Thiruvengadam, S. J.

2015-01-01

Owing to the growth of high performance network technologies, multimedia applications over the Internet are increasing exponentially. Applications like video conferencing, video-on-demand, and pay-per-view depend upon encryption algorithms for providing confidentiality. Video communication is characterized by distinct features such as large volume, high redundancy between adjacent frames, video codec compliance, syntax compliance, and application specific requirements. Naive approaches for video encryption encrypt the entire video stream with conventional text based cryptographic algorithms. Although naive approaches are the most secure for video encryption, the computational cost associated with them is very high. This research work aims at enhancing the speed of naive approaches through chaos based S-box design. Chaotic equations are popularly known for randomness, extreme sensitivity to initial conditions, and ergodicity. The proposed methodology employs two-dimensional discrete Henon map for (i) generation of dynamic and key-dependent S-box that could be integrated with symmetric algorithms like Blowfish and Data Encryption Standard (DES) and (ii) generation of one-time keys for simple substitution ciphers. The proposed design is tested for randomness, nonlinearity, avalanche effect, bit independence criterion, and key sensitivity. Experimental results confirm that chaos based S-box design and key generation significantly reduce the computational cost of video encryption with no compromise in security. PMID:26550603

Three Naive Questions: Addressed to the Modern Educational Optimism

ERIC Educational Resources Information Center

Krstic, Predrag

2016-01-01

This paper aims to question anew the popular and supposedly self-evident affirmation of education, in its modern incarnation as in its historical notion. The "naive" questions suggest that we have recently taken for granted that education ought to be for the masses, that it ought to be upbringing, and that it is better than ignorance.…

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

Intra-articular clearance of labeled dextrans from naive and arthritic rat knee joints.

PubMed

Mwangi, Timothy K; Berke, Ian M; Nieves, Eduardo H; Bell, Richard D; Adams, Samuel B; Setton, Lori A

2018-05-26

Determine the effects of arthritis on the trans-synovial clearance of small and large model compounds following local delivery to the knee joint in a rat model. Intra-articular delivery was studied in rat knee joints in an osteoarthritis model of joint instability (medial collateral ligament and meniscus transection model or MMT). Fluorescently-labeled 10 kDa or 500 kDa dextran was injected in the arthritic or unoperated control (naive) joints 3 weeks after surgical destabilization, and the temporal clearance pattern was evaluated via in vivo regional fluorescence imaging, dextran concentrations in plasma and draining lymph nodes, and by quantification of fluorescence in histological synovium sections. Together these data were used to evaluate the effect of osteoarthritis and solute size on the rate of drug clearance from the joint. Clearance of 10 kDa dextran from the joint space quantified using in vivo fluorescence imaging of the knee joint region was not significantly different between naive and MMT joints. In contrast, clearance of 500 kDa dextran was significantly reduced for MMT joints when compared to naive joints by fluorescence in vivo imaging. Drug accumulation in lymph nodes and plasma were lower for the 500 kDa dextran as compared to 10 kDa dextran, and lymph node levels were further reduced with the presence of osteoarthritis. Furthermore, synovium was significantly thicker in MMT joints than in naive joints and image analysis of joint tissue sections revealed different trans-synovial distributions of 10 and 500 kDa dextran. Large macromolecules were retained in the arthritic joint longer than in the healthy joint, while smaller molecules were cleared similarly in healthy and arthritic joints. In vivo fluorescence imaging, plasma and lymph node concentrations, and spatial distributions of drug fluorescence identified differences in higher molecular weight clearance between naive and arthritic disease states. Findings may relate to a

Expert and Naive Raters Using the PAG: Does it Matter?

ERIC Educational Resources Information Center

Cornelius, Edwin T.; And Others

1984-01-01

Questions the observed correlation between job experts and naive raters using the Position Analysis Questionnaire (PAQ); and conducts a replication of the Smith and Hakel study (1979) with college students (N=39). Concluded that PAQ ratings from job experts and college students are not equivalent and therefore are not interchangeable. (LLL)

Right lateralized white matter abnormalities in first-episode, drug-naive paranoid schizophrenia.

PubMed

Guo, Wenbin; Liu, Feng; Liu, Zhening; Gao, Keming; Xiao, Changqing; Chen, Huafu; Zhao, Jingping

2012-11-30

Numerous studies in first-episode schizophrenia suggest the involvement of white matter (WM) abnormalities in multiple regions underlying the pathogenesis of this condition. However, there has never been a neuroimaging study in patients with first-episode, drug-naive paranoid schizophrenia by using tract-based spatial statistics (TBSS) method. Here, we used diffusion tensor imaging (DTI) with TBSS method to investigate the brain WM integrity in patients with first-episode, drug-naive paranoid schizophrenia. Twenty patients with first-episode, drug-naive paranoid schizophrenia and 26 healthy subjects matched with age, gender, and education level were scanned with DTI. An automated TBSS approach was employed to analyze the data. Voxel-wise statistics revealed that patients with paranoid schizophrenia had decreased fractional anisotropy (FA) values in the right superior longitudinal fasciculus (SLF) II, the right fornix, the right internal capsule, and the right external capsule compared to healthy subjects. Patients did not have increased FA values in any brain regions compared to healthy subjects. There was no correlation between the FA values in any brain regions and patient demographics and the severity of illness. Our findings suggest right-sided alterations of WM integrity in the WM tracts of cortical and subcortical regions may play an important role in the pathogenesis of paranoid schizophrenia. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Anti-resorptive osteonecrosis of the jaws: facts forgotten, questions answered, lessons learned.

PubMed

Carlson, Eric R; Schlott, Benjamin J

2014-05-01

Osteonecrosis of the jaws associated with bisphosphonate and other anti-resorptive medications (ARONJ) has historically been a poorly understood disease process in terms of its pathophysiology, prevention and treatment since it was originally described in 2003. In association with its original discovery 11 years ago, non-evidence based speculation of these issues have been published in the international literature and are currently being challenged. A critical analysis of cancer patients with ARONJ, for example, reveals that their osteonecrosis is nearly identical to that of cancer patients who are naive to anti-resorptive medications. In addition, osteonecrosis of the jaws is not unique to patients exposed to anti-resorptive medications, but is also seen in patients with osteomyelitis and other pathologic processes of the jaws. This article represents a review of facts forgotten, questions answered, and lessons learned in general regarding osteonecrosis of the jaws. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

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

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.

Thinking Process of Naive Problem Solvers to Solve Mathematical Problems

ERIC Educational Resources Information Center

Mairing, Jackson Pasini

2017-01-01

Solving problems is not only a goal of mathematical learning. Students acquire ways of thinking, habits of persistence and curiosity, and confidence in unfamiliar situations by learning to solve problems. In fact, there were students who had difficulty in solving problems. The students were naive problem solvers. This research aimed to describe…

The Profession of Psychology Scale: Sophisticated and Naive Students' Responses

ERIC Educational Resources Information Center

Rosenthal, Gary T.; Soper, Barlow; Rachal, Chris; McKnight, Richard R.; Price, A. W.

2004-01-01

The Profession of Psychology Scale (Rosenthal, McKnight & Price, 2001) was used to investigate whether taking more psychology courses results in a more accurate understanding of what is required to become a psychologist. Data indicate that though misconceptions exist in both Naive students (those who had not completed any psychology courses) and…

IL-15 induces antigen-independent expansion and differentiation of human naive CD8+ T cells in vitro.

PubMed

Alves, Nuno L; Hooibrink, Berend; Arosa, Fernando A; van Lier, René A W

2003-10-01

Recent studies in mice have shown that although interleukin 15 (IL-15) plays an important role in regulating homeostasis of memory CD8+ T cells, it has no apparent function in controlling homeostatic proliferation of naive T cells. We here assessed the influence of IL-15 on antigen-independent expansion and differentiation of human CD8+ T cells. Both naive and primed human T cells divided in response to IL-15. In this process, naive CD8+ T cells successively down-regulated CD45RA and CD28 but maintained CD27 expression. Concomitant with these phenotypic changes, naive cells acquired the ability to produce interferon gamma (IFN-gamma) and tumor necrosis factor alpha (TNF-alpha), expressed perforin and granzyme B, and acquired cytotoxic properties. Primed CD8+ T cells, from both noncytotoxic (CD45RA-CD27+) and cytotoxic (CD45RA+CD27-) subsets, responded to IL-15 and yielded ample numbers of cytokine-secreting and cytotoxic effector cells. In summary, all human CD8+ T-cell subsets had the ability to respond to IL-15, which suggests a generic influence of this cytokine on CD8+ T-cell homeostasis in man.

Naive and effector B-cell subtypes are increased in chronic rhinosinusitis with polyps.

PubMed

Miljkovic, Dijana; Psaltis, Alkis; Wormald, Peter-John; Vreugde, Sarah

2018-01-01

Recent studies demonstrated that B cells and their chemoattractants are elevated in the nasal mucosa of patients with chronic rhinosinusitis (CRS) with nasal polyposis (CRSwNP). However, the presence of naive B cells and of plasmablasts and memory B-cell subsets in the mucosa and periphery of the same patient with CRS is yet to be characterized. Here we sought to quantify naive, plasmablasts, and memory B cells in mucosal tissue and peripheral blood of patients with CRSwNP, patients with CRS without nasal polyps (CRSsNP), and control patients. Polyps, mucosa, and peripheral blood samples were prospectively collected from the patients with CRS and from the non-CRS controls. We used flow cytometry to distinguish among naive, plasmablast, and memory B cells in sinus tissue and peripheral blood. A total of 45 patients were recruited for the study. The patients with CRSwNP had significantly increased mucosal B-cell numbers versus the controls (3.39 ± 4.05% versus 0.39 ± 1.05% of live cells; p < 0.01, Kruskal-Wallis test), which included naive B cells (0.61 ± 0.94 versus 0.11 ± 0.24% of live cells; p < 0.03, Kruskal-Wallis test), plasmablasts (0.06 ± 0.26 versus 0.00 ± 0.00% of live cells; p < 0.055, Kruskal-Wallis test), and memory B cells (0.62 ± 1.26 versus 0.05 ± 0.15% of live cells; p < 0.02, Kruskal-Wallis test). Our study identified increased frequencies of different B-cell subtypes in the mucosa of patients with CRSwNP but not in the peripheral blood. We also found that patients with CRSwNP had significantly increased B-cell subtypes compared with the patients with CRSsNP and the controls. These results implied a potential role for mucosal B cells in the ongoing inflammation in patients with CRSwNP.

The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.

PubMed

Kruschke, John K; Liddell, Torrin M

2018-02-01

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

AAVrh.10-mediated expression of an anti-cocaine antibody mediates persistent passive immunization that suppresses cocaine-induced behavior.

PubMed

Rosenberg, Jonathan B; Hicks, Martin J; De, Bishnu P; Pagovich, Odelya; Frenk, Esther; Janda, Kim D; Wee, Sunmee; Koob, George F; Hackett, Neil R; Kaminsky, Stephen M; Worgall, Stefan; Tignor, Nicole; Mezey, Jason G; Crystal, Ronald G

2012-05-01

Cocaine addiction is a major problem affecting all societal and economic classes for which there is no effective therapy. We hypothesized an effective anti-cocaine vaccine could be developed by using an adeno-associated virus (AAV) gene transfer vector as the delivery vehicle to persistently express an anti-cocaine monoclonal antibody in vivo, which would sequester cocaine in the blood, preventing access to cognate receptors in the brain. To accomplish this, we constructed AAVrh.10antiCoc.Mab, an AAVrh.10 gene transfer vector expressing the heavy and light chains of the high affinity anti-cocaine monoclonal antibody GNC92H2. Intravenous administration of AAVrh.10antiCoc.Mab to mice mediated high, persistent serum levels of high-affinity, cocaine-specific antibodies that sequestered intravenously administered cocaine in the blood. With repeated intravenous cocaine challenge, naive mice exhibited hyperactivity, while the AAVrh.10antiCoc.Mab-vaccinated mice were completely resistant to the cocaine. These observations demonstrate a novel strategy for cocaine addiction by requiring only a single administration of an AAV vector mediating persistent, systemic anti-cocaine passive immunity.

UNIFORMLY MOST POWERFUL BAYESIAN TESTS

PubMed Central

Johnson, Valen E.

2014-01-01

Uniformly most powerful tests are statistical hypothesis tests that provide the greatest power against a fixed null hypothesis among all tests of a given size. In this article, the notion of uniformly most powerful tests is extended to the Bayesian setting by defining uniformly most powerful Bayesian tests to be tests that maximize the probability that the Bayes factor, in favor of the alternative hypothesis, exceeds a specified threshold. Like their classical counterpart, uniformly most powerful Bayesian tests are most easily defined in one-parameter exponential family models, although extensions outside of this class are possible. The connection between uniformly most powerful tests and uniformly most powerful Bayesian tests can be used to provide an approximate calibration between p-values and Bayes factors. Finally, issues regarding the strong dependence of resulting Bayes factors and p-values on sample size are discussed. PMID:24659829

75 FR 32692 - Schools and Libraries Universal Service Support Mechanism

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-09

..., wireless Internet access applications, and web hosting. We propose to revise the Commission's rules to.../anti-spam software, scheduling services, wireless Internet access applications, and web hosting should... schools and libraries may receive discounts for eligible telecommunications services, Internet access, and...

Sending Large Files without Mucking up the Works

ERIC Educational Resources Information Center

Goldborough, Reid

2005-01-01

E-mail has never been a foolproof way of sending information, and lately it has gotten even hairier, with well-meaning but overzealous anti-spam filters often blocking even legitimate messages. This document discusses different options available for sending files.

A Workshop for High School Students on Naive Set Theory

ERIC Educational Resources Information Center

Wegner, Sven-Ake

2014-01-01

In this article we present the prototype of a workshop on naive set theory designed for high school students in or around the seventh year of primary education. Our concept is based on two events which the author organized in 2006 and 2010 for students of elementary school and high school, respectively. The article also includes a practice report…

Bayesian methods in reliability

NASA Astrophysics Data System (ADS)

Sander, P.; Badoux, R.

1991-11-01

The present proceedings from a course on Bayesian methods in reliability encompasses Bayesian statistical methods and their computational implementation, models for analyzing censored data from nonrepairable systems, the traits of repairable systems and growth models, the use of expert judgment, and a review of the problem of forecasting software reliability. Specific issues addressed include the use of Bayesian methods to estimate the leak rate of a gas pipeline, approximate analyses under great prior uncertainty, reliability estimation techniques, and a nonhomogeneous Poisson process. Also addressed are the calibration sets and seed variables of expert judgment systems for risk assessment, experimental illustrations of the use of expert judgment for reliability testing, and analyses of the predictive quality of software-reliability growth models such as the Weibull order statistics.

Is probabilistic bias analysis approximately Bayesian?

PubMed Central

MacLehose, Richard F.; Gustafson, Paul

2011-01-01

Case-control studies are particularly susceptible to differential exposure misclassification when exposure status is determined following incident case status. Probabilistic bias analysis methods have been developed as ways to adjust standard effect estimates based on the sensitivity and specificity of exposure misclassification. The iterative sampling method advocated in probabilistic bias analysis bears a distinct resemblance to a Bayesian adjustment; however, it is not identical. Furthermore, without a formal theoretical framework (Bayesian or frequentist), the results of a probabilistic bias analysis remain somewhat difficult to interpret. We describe, both theoretically and empirically, the extent to which probabilistic bias analysis can be viewed as approximately Bayesian. While the differences between probabilistic bias analysis and Bayesian approaches to misclassification can be substantial, these situations often involve unrealistic prior specifications and are relatively easy to detect. Outside of these special cases, probabilistic bias analysis and Bayesian approaches to exposure misclassification in case-control studies appear to perform equally well. PMID:22157311

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

Robust Bayesian clustering.

PubMed

Archambeau, Cédric; Verleysen, Michel

2007-01-01

A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algorithm leads to (i) robust density estimation, (ii) robust clustering and (iii) robust automatic model selection. Gaussian mixture models are learning machines which are based on a divide-and-conquer approach. They are commonly used for density estimation and clustering tasks, but are sensitive to outliers. The Student-t distribution has heavier tails than the Gaussian distribution and is therefore less sensitive to any departure of the empirical distribution from Gaussianity. As a consequence, the Student-t distribution is suitable for constructing robust mixture models. In this work, we formalize the Bayesian Student-t mixture model as a latent variable model in a different way from Svensén and Bishop [Svensén, M., & Bishop, C. M. (2005). Robust Bayesian mixture modelling. Neurocomputing, 64, 235-252]. The main difference resides in the fact that it is not necessary to assume a factorized approximation of the posterior distribution on the latent indicator variables and the latent scale variables in order to obtain a tractable solution. Not neglecting the correlations between these unobserved random variables leads to a Bayesian model having an increased robustness. Furthermore, it is expected that the lower bound on the log-evidence is tighter. Based on this bound, the model complexity, i.e. the number of components in the mixture, can be inferred with a higher confidence.

Bayesian networks improve causal environmental ...

EPA Pesticide Factsheets

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

Bayesian Latent Class Analysis Tutorial.

PubMed

Li, Yuelin; Lord-Bessen, Jennifer; Shiyko, Mariya; Loeb, Rebecca

2018-01-01

This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.

HIV-1 drug resistance mutations emerging on darunavir therapy in PI-naive and -experienced patients in the UK.

PubMed

El Bouzidi, Kate; White, Ellen; Mbisa, Jean L; Sabin, Caroline A; Phillips, Andrew N; Mackie, Nicola; Pozniak, Anton L; Tostevin, Anna; Pillay, Deenan; Dunn, David T

2016-12-01

Darunavir is considered to have a high genetic barrier to resistance. Most darunavir-associated drug resistance mutations (DRMs) have been identified through correlation of baseline genotype with virological response in clinical trials. However, there is little information on DRMs that are directly selected by darunavir in clinical settings. We examined darunavir DRMs emerging in clinical practice in the UK. Baseline and post-exposure protease genotypes were compared for individuals in the UK Collaborative HIV Cohort Study who had received darunavir; analyses were stratified for PI history. A selection analysis was used to compare the evolution of subtype B proteases in darunavir recipients and matched PI-naive controls. Of 6918 people who had received darunavir, 386 had resistance tests pre- and post-exposure. Overall, 2.8% (11/386) of these participants developed emergent darunavir DRMs. The prevalence of baseline DRMs was 1.0% (2/198) among PI-naive participants and 13.8% (26/188) among PI-experienced participants. Emergent DRMs developed in 2.0% of the PI-naive group (4 mutations) and 3.7% of the PI-experienced group (12 mutations). Codon 77 was positively selected in the PI-naive darunavir cases, but not in the control group. Our findings suggest that although emergent darunavir resistance is rare, it may be more common among PI-experienced patients than those who are PI-naive. Further investigation is required to explore whether codon 77 is a novel site involved in darunavir susceptibility. © The Author 2016. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.

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…

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…

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

Edoxaban vs. warfarin in vitamin K antagonist experienced and naive patients with atrial fibrillation†.

PubMed

O'Donoghue, Michelle L; Ruff, Christian T; Giugliano, Robert P; Murphy, Sabina A; Grip, Laura T; Mercuri, Michele F; Rutman, Howard; Shi, Minggao; Kania, Grzegorz; Cermak, Ondrej; Braunwald, Eugene; Antman, Elliott M

2015-06-14

Edoxaban is an oral, once-daily factor Xa inhibitor that is non-inferior to well-managed warfarin in patients with atrial fibrillation (AF) for the prevention of stroke and systemic embolic events (SEEs). We examined the efficacy and safety of edoxaban vs. warfarin in patients who were vitamin K antagonist (VKA) naive or experienced. ENGAGE AF-TIMI 48 randomized 21 105 patients with AF at moderate-to-high risk of stroke to once-daily edoxaban vs. warfarin. Subjects were followed for a median of 2.8 years. The primary efficacy endpoint was stroke or SEE. As a pre-specified subgroup, we analysed outcomes for those with or without prior VKA experience (>60 consecutive days). Higher-dose edoxaban significantly reduced the risk of stroke or SEE in patients who were VKA naive [hazard ratio (HR) 0.71, 95% confidence interval (CI) 0.56-0.90] and was similar to warfarin in the VKA experienced (HR 1.01, 95% CI 0.82-1.24; P interaction = 0.028). Lower-dose edoxaban was similar to warfarin for stroke or SEE prevention in patients who were VKA naive (HR 0.92, 95% CI 0.73-1.15), but was inferior to warfarin in those who were VKA experienced (HR 1.31, 95% 1.08-1.60; P interaction = 0.019). Both higher-dose and lower-dose edoxaban regimens significantly reduced the risk of major bleeding regardless of prior VKA experience (P interaction = 0.90 and 0.71, respectively). In patients with AF, edoxaban appeared to demonstrate greater efficacy compared with warfarin in patients who were VKA naive than VKA experienced. Edoxaban significantly reduced major bleeding compared with warfarin regardless of prior VKA exposure. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2015. For permissions please email: journals.permissions@oup.com.

The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group.

PubMed

Natanegara, Fanni; Neuenschwander, Beat; Seaman, John W; Kinnersley, Nelson; Heilmann, Cory R; Ohlssen, David; Rochester, George

2014-01-01

Bayesian applications in medical product development have recently gained popularity. Despite many advances in Bayesian methodology and computations, increase in application across the various areas of medical product development has been modest. The DIA Bayesian Scientific Working Group (BSWG), which includes representatives from industry, regulatory agencies, and academia, has adopted the vision to ensure Bayesian methods are well understood, accepted more broadly, and appropriately utilized to improve decision making and enhance patient outcomes. As Bayesian applications in medical product development are wide ranging, several sub-teams were formed to focus on various topics such as patient safety, non-inferiority, prior specification, comparative effectiveness, joint modeling, program-wide decision making, analytical tools, and education. The focus of this paper is on the recent effort of the BSWG Education sub-team to administer a Bayesian survey to statisticians across 17 organizations involved in medical product development. We summarize results of this survey, from which we provide recommendations on how to accelerate progress in Bayesian applications throughout medical product development. The survey results support findings from the literature and provide additional insight on regulatory acceptance of Bayesian methods and information on the need for a Bayesian infrastructure within an organization. The survey findings support the claim that only modest progress in areas of education and implementation has been made recently, despite substantial progress in Bayesian statistical research and software availability. Copyright © 2013 John Wiley & Sons, Ltd.

Naive and effector B-cell subtypes are increased in chronic rhinosinusitis with polyps

PubMed Central

Miljkovic, Dijana; Psaltis, Alkis; Wormald, Peter-John

2018-01-01

Background: Recent studies demonstrated that B cells and their chemoattractants are elevated in the nasal mucosa of patients with chronic rhinosinusitis (CRS) with nasal polyposis (CRSwNP). However, the presence of naive B cells and of plasmablasts and memory B-cell subsets in the mucosa and periphery of the same patient with CRS is yet to be characterized. Objective: Here we sought to quantify naive, plasmablasts, and memory B cells in mucosal tissue and peripheral blood of patients with CRSwNP, patients with CRS without nasal polyps (CRSsNP), and control patients. Methods: Polyps, mucosa, and peripheral blood samples were prospectively collected from the patients with CRS and from the non-CRS controls. We used flow cytometry to distinguish among naive, plasmablast, and memory B cells in sinus tissue and peripheral blood. Results: A total of 45 patients were recruited for the study. The patients with CRSwNP had significantly increased mucosal B-cell numbers versus the controls (3.39 ± 4.05% versus 0.39 ± 1.05% of live cells; p < 0.01, Kruskal-Wallis test), which included naive B cells (0.61 ± 0.94 versus 0.11 ± 0.24% of live cells; p < 0.03, Kruskal-Wallis test), plasmablasts (0.06 ± 0.26 versus 0.00 ± 0.00% of live cells; p < 0.055, Kruskal-Wallis test), and memory B cells (0.62 ± 1.26 versus 0.05 ± 0.15% of live cells; p < 0.02, Kruskal-Wallis test). Conclusion: Our study identified increased frequencies of different B-cell subtypes in the mucosa of patients with CRSwNP but not in the peripheral blood. We also found that patients with CRSwNP had significantly increased B-cell subtypes compared with the patients with CRSsNP and the controls. These results implied a potential role for mucosal B cells in the ongoing inflammation in patients with CRSwNP. PMID:29336281

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.

Anti-Obesity Agents and the US Food and Drug Administration.

PubMed

Casey, Martin F; Mechanick, Jeffrey I

2014-09-01

Despite the growing market for obesity care, the US Food and Drug Administration (FDA) has approved only two new pharmaceutical agents-lorcaserin and combination phentermine/topiramate-for weight reduction since 2000, while removing three agents from the market in the same time period. This article explores the FDA's history and role in the approval of anti-obesity medications within the context of a public health model of obesity. Through the review of obesity literature and FDA approval documents, we identified two major barriers preventing fair evaluation of anti-obesity agents including: (1) methodological pitfalls in clinical trials and (2) misaligned values in the assessment of anti-obesity agents. Specific recommendations include the use of adaptive (Bayesian) design protocols, value-based analyses of risks and benefits, and regulatory guidance based on a comprehensive, multi-platform obesity disease model. Positively addressing barriers in the FDA approval process of anti-obesity agents may have many beneficial effects within an obesity disease model.

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 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

The Effect of Naive Ideas on Students' Reasoning about Electricity and Magnetism

ERIC Educational Resources Information Center

Leppavirta, Johanna

2012-01-01

Traditional multiple-choice concept inventories measure students' critical conceptual understanding and are designed to reveal students' naive or alternate ideas. The overall scores, however, give little information about the state of students' knowledge and the consistency of reasoning. This study investigates whether students have consistent…

Bayesian Decision Support

NASA Astrophysics Data System (ADS)

Berliner, M.

2017-12-01

Bayesian statistical decision theory offers a natural framework for decision-policy making in the presence of uncertainty. Key advantages of the approach include efficient incorporation of information and observations. However, in complicated settings it is very difficult, perhaps essentially impossible, to formalize the mathematical inputs needed in the approach. Nevertheless, using the approach as a template is useful for decision support; that is, organizing and communicating our analyses. Bayesian hierarchical modeling is valuable in quantifying and managing uncertainty such cases. I review some aspects of the idea emphasizing statistical model development and use in the context of sea-level rise.

CD72 ligation regulates defective naive newborn B cell responses.

PubMed

Howard, L M; Reen, D J

1997-02-01

The biological basis for reduced Ig production by naive newborn B cells compared to adult peripheral blood B cells is not fully understood. In a Con A + IL-2 T cell-dependent system using "competent" adult T cells, adult B cells produced large amounts of IgM, IgG, and IgA, while cord B cells were restricted to low levels of only IgM production. Cord B cell activation was also diminished. The contribution of specific B-T cell contact-mediated events to the diminished cord B cell response in this system, using mAbs to CD40, CD28, CD80, and CD72, were investigated, as well as regulation of B cell Ig production by cytokines. alphaCD72 ligation increased cord B cell activation and IgM production, but did not affect adult B cells. Blocking alphaCD40 mAb inhibited cord B cell Ig production completely, but only partly inhibited adult B cell Ig production even at high concentration, suggesting a greater sensitivity of cord B cells to disruption of the CD40-CD40L interaction. Addition of IL-10 did not increase cord B cell Ig production, while adult B cell Ig production was increased. However, combined addition of IL-10 and alphaCD72 significantly increased cord B cell Ig production over that in the presence of either alphaCD72 or IL-10 alone, but had no effect on adult B cells over that of IL-10 alone. These data suggest that the diminished T cell-dependent response of cord B cells is due to reduced or absent CD72 ligation. CD72 ligation plays an important role in the induction of primary responses by naive B cells. CD72 modulation of naive B cell sensitivity to IL-10 stimulation may have implications in the induction of class switch, which is deficient in newborn B cells. Since all T cells express CD5 constitutively, these data also suggest the existence of another ligand for CD72.

Children's Naive Theories of Intelligence Influence Their Metacognitive Judgments

ERIC Educational Resources Information Center

Miele, David B.; Son, Lisa K.; Metcalfe, Janet

2013-01-01

Recent studies have shown that the metacognitive judgments adults infer from their experiences of encoding effort vary in accordance with their naive theories of intelligence. To determine whether this finding extends to elementary schoolchildren, a study was conducted in which 27 third graders (M[subscript age] = 8.27) and 24 fifth graders…

Disrupted adenovirus-based vaccines against small addictive molecules circumvent anti-adenovirus immunity.

PubMed

De, Bishnu P; Pagovich, Odelya E; Hicks, Martin J; Rosenberg, Jonathan B; Moreno, Amira Y; Janda, Kim D; Koob, George F; Worgall, Stefan; Kaminsky, Stephen M; Sondhi, Dolan; Crystal, Ronald G

2013-01-01

Adenovirus (Ad) vaccine vectors have been used for many applications due to the capacity of the Ad capsid proteins to evoke potent immune responses, but these vectors are often ineffective in the context of pre-existing anti-Ad immunity. Leveraging the knowledge that E1(-)E3(-) Ad gene transfer vectors are potent immunogens, we have developed a vaccine platform against small molecules by covalently coupling analogs of small molecules to the capsid proteins of disrupted Ad (dAd5). We hypothesized that the dAd5 platform would maintain immunopotency even in the context of anti-Ad neutralizing antibodies. To test this hypothesis, we coupled cocaine and nicotine analogs, GNE and AM1, to dAd5 capsid proteins to generate dAd5GNE and dAd5AM1, respectively. Mice were pre-immunized with Ad5Null, resulting in high titer anti-Ad5 neutralizing antibodies comparable to those observed in the human population. The dAd5GNE and dAd5AM1 vaccines elicited high anti-cocaine and anti-nicotine antibody titers, respectively, in both naive and Ad5-immune mice, and both functioned to prevent cocaine or nicotine from reaching the brain of anti-Ad immune mice. Thus, disrupted Ad5 evokes potent humoral immunity that is effective in the context of pre-existing neutralizing anti-Ad immunity, overcoming a major limitation for current Ad-based vaccines.

Disrupted Adenovirus-Based Vaccines Against Small Addictive Molecules Circumvent Anti-Adenovirus Immunity

PubMed Central

De, Bishnu P.; Pagovich, Odelya E.; Hicks, Martin J.; Rosenberg, Jonathan B.; Moreno, Amira Y.; Janda, Kim D.; Koob, George F.; Worgall, Stefan; Kaminsky, Stephen M.; Sondhi, Dolan

2013-01-01

Abstract Adenovirus (Ad) vaccine vectors have been used for many applications due to the capacity of the Ad capsid proteins to evoke potent immune responses, but these vectors are often ineffective in the context of pre-existing anti-Ad immunity. Leveraging the knowledge that E1−E3− Ad gene transfer vectors are potent immunogens, we have developed a vaccine platform against small molecules by covalently coupling analogs of small molecules to the capsid proteins of disrupted Ad (dAd5). We hypothesized that the dAd5 platform would maintain immunopotency even in the context of anti-Ad neutralizing antibodies. To test this hypothesis, we coupled cocaine and nicotine analogs, GNE and AM1, to dAd5 capsid proteins to generate dAd5GNE and dAd5AM1, respectively. Mice were pre-immunized with Ad5Null, resulting in high titer anti-Ad5 neutralizing antibodies comparable to those observed in the human population. The dAd5GNE and dAd5AM1 vaccines elicited high anti-cocaine and anti-nicotine antibody titers, respectively, in both naive and Ad5-immune mice, and both functioned to prevent cocaine or nicotine from reaching the brain of anti-Ad immune mice. Thus, disrupted Ad5 evokes potent humoral immunity that is effective in the context of pre-existing neutralizing anti-Ad immunity, overcoming a major limitation for current Ad-based vaccines. PMID:23140508

CD4:8 ratio >5 is associated with a dominant naive T-cell phenotype and impaired physical functioning in CMV-seropositive very elderly people: results from the BELFRAIL study.

PubMed

Adriaensen, Wim; Derhovanessian, Evelyna; Vaes, Bert; Van Pottelbergh, Gijs; Degryse, Jean-Marie; Pawelec, Graham; Hamprecht, Klaus; Theeten, Heidi; Matheï, Catharina

2015-02-01

A subset of older people is at increased risk of hospitalization and dependency. Emerging evidence suggests that immunosenescence reflected by an inverted CD4:8 ratio and cytomegalovirus (CMV) seropositivity plays an important role in the pathophysiology of functional decline. Nevertheless, the relation between CD4:8 ratio and functional outcome has rarely been investigated. Here, CD4:8 ratio and T-cell phenotypes of 235 community-dwelling persons aged ≥81.5 years in the BELFRAIL study and 25 younger persons (mean age 28.5 years) were analyzed using polychromatic flow cytometry. In the elderly persons, 7.2% had an inverted CD4:8 ratio, which was associated with CMV seropositivity, less naive, and more late-differentiated CD4+ and CD8+ T cells. However, 32.8% had a CD4:8 ratio >5, a phenotype associated with a higher proportion of naive T cells and absent in young donors. In CMV seropositives, this subgroup had lower proportions of late-differentiated CD4+ and CD8+ T cells and weaker anti-CMV immunoglobulin G reactivity. This novel naive T-cell-dominated phenotype was counterintuitively associated with a higher proportion of those with impaired physical functioning in the very elderly people infected with CMV. This underscores the notion that in very elderly people, not merely CMV infection but also the state of its accompanying immune dysregulation is of crucial importance with regard to physical impairment. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Recent thymic emigrants and mature naive T cells exhibit differential DNA methylation at key cytokine loci.

PubMed

Berkley, Amy M; Hendricks, Deborah W; Simmons, Kalynn B; Fink, Pamela J

2013-06-15

Recent thymic emigrants (RTEs) are the youngest T cells in the lymphoid periphery and exhibit phenotypic and functional characteristics distinct from those of their more mature counterparts in the naive peripheral T cell pool. We show in this study that the Il2 and Il4 promoter regions of naive CD4(+) RTEs are characterized by site-specific hypermethylation compared with those of both mature naive (MN) T cells and the thymocyte precursors of RTEs. Thus, RTEs do not merely occupy a midpoint between the thymus and the mature T cell pool, but represent a distinct transitional T cell population. Furthermore, RTEs and MN T cells exhibit distinct CpG DNA methylation patterns both before and after activation. Compared with MN T cells, RTEs express higher levels of several enzymes that modify DNA methylation, and inhibiting methylation during culture allows RTEs to reach MN T cell levels of cytokine production. Collectively, these data suggest that the functional differences that distinguish RTEs from MN T cells are influenced by epigenetic mechanisms and provide clues to a mechanistic basis for postthymic maturation.

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.

Bayesian learning

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1989-01-01

In 1983 and 1984, the Infrared Astronomical Satellite (IRAS) detected 5,425 stellar objects and measured their infrared spectra. In 1987 a program called AUTOCLASS used Bayesian inference methods to discover the classes present in these data and determine the most probable class of each object, revealing unknown phenomena in astronomy. AUTOCLASS has rekindled the old debate on the suitability of Bayesian methods, which are computationally intensive, interpret probabilities as plausibility measures rather than frequencies, and appear to depend on a subjective assessment of the probability of a hypothesis before the data were collected. Modern statistical methods have, however, recently been shown to also depend on subjective elements. These debates bring into question the whole tradition of scientific objectivity and offer scientists a new way to take responsibility for their findings and conclusions.

Searching Algorithm Using Bayesian Updates

ERIC Educational Resources Information Center

Caudle, Kyle

2010-01-01

In late October 1967, the USS Scorpion was lost at sea, somewhere between the Azores and Norfolk Virginia. Dr. Craven of the U.S. Navy's Special Projects Division is credited with using Bayesian Search Theory to locate the submarine. Bayesian Search Theory is a straightforward and interesting application of Bayes' theorem which involves searching…

AAVrh.10-Mediated Expression of an Anti-Cocaine Antibody Mediates Persistent Passive Immunization That Suppresses Cocaine-Induced Behavior

PubMed Central

Rosenberg, Jonathan B.; Hicks, Martin J.; De, Bishnu P.; Pagovich, Odelya; Frenk, Esther; Janda, Kim D.; Wee, Sunmee; Koob, George F.; Hackett, Neil R.; Kaminsky, Stephen M.; Worgall, Stefan; Tignor, Nicole; Mezey, Jason G.

2012-01-01

Abstract Cocaine addiction is a major problem affecting all societal and economic classes for which there is no effective therapy. We hypothesized an effective anti-cocaine vaccine could be developed by using an adeno-associated virus (AAV) gene transfer vector as the delivery vehicle to persistently express an anti-cocaine monoclonal antibody in vivo, which would sequester cocaine in the blood, preventing access to cognate receptors in the brain. To accomplish this, we constructed AAVrh.10antiCoc.Mab, an AAVrh.10 gene transfer vector expressing the heavy and light chains of the high affinity anti-cocaine monoclonal antibody GNC92H2. Intravenous administration of AAVrh.10antiCoc.Mab to mice mediated high, persistent serum levels of high-affinity, cocaine-specific antibodies that sequestered intravenously administered cocaine in the blood. With repeated intravenous cocaine challenge, naive mice exhibited hyperactivity, while the AAVrh.10antiCoc.Mab-vaccinated mice were completely resistant to the cocaine. These observations demonstrate a novel strategy for cocaine addiction by requiring only a single administration of an AAV vector mediating persistent, systemic anti-cocaine passive immunity. PMID:22486244

Bayesian adjustment for measurement error in continuous exposures in an individually matched case-control study.

PubMed

Espino-Hernandez, Gabriela; Gustafson, Paul; Burstyn, Igor

2011-05-14

In epidemiological studies explanatory variables are frequently subject to measurement error. The aim of this paper is to develop a Bayesian method to correct for measurement error in multiple continuous exposures in individually matched case-control studies. This is a topic that has not been widely investigated. The new method is illustrated using data from an individually matched case-control study of the association between thyroid hormone levels during pregnancy and exposure to perfluorinated acids. The objective of the motivating study was to examine the risk of maternal hypothyroxinemia due to exposure to three perfluorinated acids measured on a continuous scale. Results from the proposed method are compared with those obtained from a naive analysis. Using a Bayesian approach, the developed method considers a classical measurement error model for the exposures, as well as the conditional logistic regression likelihood as the disease model, together with a random-effect exposure model. Proper and diffuse prior distributions are assigned, and results from a quality control experiment are used to estimate the perfluorinated acids' measurement error variability. As a result, posterior distributions and 95% credible intervals of the odds ratios are computed. A sensitivity analysis of method's performance in this particular application with different measurement error variability was performed. The proposed Bayesian method to correct for measurement error is feasible and can be implemented using statistical software. For the study on perfluorinated acids, a comparison of the inferences which are corrected for measurement error to those which ignore it indicates that little adjustment is manifested for the level of measurement error actually exhibited in the exposures. Nevertheless, a sensitivity analysis shows that more substantial adjustments arise if larger measurement errors are assumed. In individually matched case-control studies, the use of conditional

Bayesian just-so stories in psychology and neuroscience.

PubMed

Bowers, Jeffrey S; Davis, Colin J

2012-05-01

According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak. This weakness relates to the many arbitrary ways that priors, likelihoods, and utility functions can be altered in order to account for the data that are obtained, making the models unfalsifiable. It further relates to the fact that Bayesian theories are rarely better at predicting data compared with alternative (and simpler) non-Bayesian theories. Second, we show that the empirical evidence for Bayesian theories in neuroscience is weaker still. There are impressive mathematical analyses showing how populations of neurons could compute in a Bayesian manner but little or no evidence that they do. Third, we challenge the general scientific approach that characterizes Bayesian theorizing in cognitive science. A common premise is that theories in psychology should largely be constrained by a rational analysis of what the mind ought to do. We question this claim and argue that many of the important constraints come from biological, evolutionary, and processing (algorithmic) considerations that have no adaptive relevance to the problem per se. In our view, these factors have contributed to the development of many Bayesian "just so" stories in psychology and neuroscience; that is, mathematical analyses of cognition that can be used to explain almost any behavior as optimal. 2012 APA, 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.

Nicotinic Acid Adenine Dinucleotide Phosphate Plays a Critical Role in Naive and Effector Murine T Cells but Not Natural Regulatory T Cells*

PubMed Central

Ali, Ramadan A.; Camick, Christina; Wiles, Katherine; Walseth, Timothy F.; Slama, James T.; Bhattacharya, Sumit; Giovannucci, David R.; Wall, Katherine A.

2016-01-01

Nicotinic acid adenine dinucleotide phosphate (NAADP), the most potent Ca2+ mobilizing second messenger discovered to date, has been implicated in Ca2+ signaling in some lymphomas and T cell clones. In contrast, the role of NAADP in Ca2+ signaling or the identity of the Ca2+ stores targeted by NAADP in conventional naive T cells is less clear. In the current study, we demonstrate the importance of NAADP in the generation of Ca2+ signals in murine naive T cells. Combining live-cell imaging methods and a pharmacological approach using the NAADP antagonist Ned-19, we addressed the involvement of NAADP in the generation of Ca2+ signals evoked by TCR stimulation and the role of this signal in downstream physiological end points such as proliferation, cytokine production, and other responses to stimulation. We demonstrated that acidic compartments in addition to the endoplasmic reticulum were the Ca2+ stores that were sensitive to NAADP in naive T cells. NAADP was shown to evoke functionally relevant Ca2+ signals in both naive CD4 and naive CD8 T cells. Furthermore, we examined the role of this signal in the activation, proliferation, and secretion of effector cytokines by Th1, Th2, Th17, and CD8 effector T cells. Overall, NAADP exhibited a similar profile in mediating Ca2+ release in effector T cells as in their counterpart naive T cells and seemed to be equally important for the function of these different subsets of effector T cells. This profile was not observed for natural T regulatory cells. PMID:26728458

A default Bayesian hypothesis test for mediation.

PubMed

Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan

2015-03-01

In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).

Free will in Bayesian and inverse Bayesian inference-driven endo-consciousness.

PubMed

Gunji, Yukio-Pegio; Minoura, Mai; Kojima, Kei; Horry, Yoichi

2017-12-01

How can we link challenging issues related to consciousness and/or qualia with natural science? The introduction of endo-perspective, instead of exo-perspective, as proposed by Matsuno, Rössler, and Gunji, is considered one of the most promising candidate approaches. Here, we distinguish the endo-from the exo-perspective in terms of whether the external is or is not directly operated. In the endo-perspective, the external can be neither perceived nor recognized directly; rather, one can only indirectly summon something outside of the perspective, which can be illustrated by a causation-reversal pair. On one hand, causation logically proceeds from the cause to the effect. On the other hand, a reversal from the effect to the cause is non-logical and is equipped with a metaphorical structure. We argue that the differences in exo- and endo-perspectives result not from the difference between Western and Eastern cultures, but from differences between modernism and animism. Here, a causation-reversal pair described using a pair of upward (from premise to consequence) and downward (from consequence to premise) causation and a pair of Bayesian and inverse Bayesian inference (BIB inference). Accordingly, the notion of endo-consciousness is proposed as an agent equipped with BIB inference. We also argue that BIB inference can yield both highly efficient computations through Bayesian interference and robust computations through inverse Bayesian inference. By adapting a logical model of the free will theorem to the BIB inference, we show that endo-consciousness can explain free will as a regression of the controllability of voluntary action. Copyright © 2017. Published by Elsevier Ltd.

Application of Bayesian Approach in Cancer Clinical Trial

PubMed Central

Bhattacharjee, Atanu

2014-01-01

The application of Bayesian approach in clinical trials becomes more useful over classical method. It is beneficial from design to analysis phase. The straight forward statement is possible to obtain through Bayesian about the drug treatment effect. Complex computational problems are simple to handle with Bayesian techniques. The technique is only feasible to performing presence of prior information of the data. The inference is possible to establish through posterior estimates. However, some limitations are present in this method. The objective of this work was to explore the several merits and demerits of Bayesian approach in cancer research. The review of the technique will be helpful for the clinical researcher involved in the oncology to explore the limitation and power of Bayesian techniques. PMID:29147387

Bayesian least squares deconvolution

NASA Astrophysics Data System (ADS)

Asensio Ramos, A.; Petit, P.

2015-11-01

Aims: We develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods: We consider LSD under the Bayesian framework and we introduce a flexible Gaussian process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results: We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.

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.

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.

Universal Darwinism As a Process of Bayesian Inference.

PubMed

Campbell, John O

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

Homeostasis of naive and memory CD4+ T cells: IL-2 and IL-7 differentially regulate the balance between proliferation and Fas-mediated apoptosis.

PubMed

Jaleco, Sara; Swainson, Louise; Dardalhon, Valérie; Burjanadze, Maryam; Kinet, Sandrina; Taylor, Naomi

2003-07-01

Cytokines play a crucial role in the maintenance of polyclonal naive and memory T cell populations. It has previously been shown that ex vivo, the IL-7 cytokine induces the proliferation of naive recent thymic emigrants (RTE) isolated from umbilical cord blood but not mature adult-derived naive and memory human CD4(+) T cells. We find that the combination of IL-2 and IL-7 strongly promotes the proliferation of RTE, whereas adult CD4(+) T cells remain relatively unresponsive. Immunological activity is controlled by a balance between proliferation and apoptotic cell death. However, the relative contributions of IL-2 and IL-7 in regulating these processes in the absence of MHC/peptide signals are not known. Following exposure to either IL-2 or IL-7 alone, RTE, as well as mature naive and memory CD4(+) T cells, are rendered only minimally sensitive to Fas-mediated cell death. However, in the presence of the two cytokines, Fas engagement results in a high level of caspase-dependent apoptosis in both RTE as well as naive adult CD4(+) T cells. In contrast, equivalently treated memory CD4(+) T cells are significantly less sensitive to Fas-induced cell death. The increased susceptibility of RTE and naive CD4(+) T cells to Fas-induced apoptosis correlates with a significantly higher IL-2/IL-7-induced Fas expression on these T cell subsets than on memory CD4(+) T cells. Thus, IL-2 and IL-7 regulate homeostasis by modulating the equilibrium between proliferation and apoptotic cell death in RTE and mature naive and memory T cell subsets.

Daniel Goodman’s empirical approach to Bayesian statistics

USGS Publications Warehouse

Gerrodette, Tim; Ward, Eric; Taylor, Rebecca L.; Schwarz, Lisa K.; Eguchi, Tomoharu; Wade, Paul; Himes Boor, Gina

2016-01-01

Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

PubMed Central

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B; Neyer, Franz J; van Aken, Marcel AG

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided. PMID:24116396

Bayesian Inference: with ecological applications

USGS Publications Warehouse

Link, William A.; Barker, Richard J.

2010-01-01

This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.

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.

Impairment of DNA Methylation Maintenance Is the Main Cause of Global Demethylation in Naive Embryonic Stem Cells.

PubMed

von Meyenn, Ferdinand; Iurlaro, Mario; Habibi, Ehsan; Liu, Ning Qing; Salehzadeh-Yazdi, Ali; Santos, Fátima; Petrini, Edoardo; Milagre, Inês; Yu, Miao; Xie, Zhenqing; Kroeze, Leonie I; Nesterova, Tatyana B; Jansen, Joop H; Xie, Hehuang; He, Chuan; Reik, Wolf; Stunnenberg, Hendrik G

2016-06-16

Global demethylation is part of a conserved program of epigenetic reprogramming to naive pluripotency. The transition from primed hypermethylated embryonic stem cells (ESCs) to naive hypomethylated ones (serum-to-2i) is a valuable model system for epigenetic reprogramming. We present a mathematical model, which accurately predicts global DNA demethylation kinetics. Experimentally, we show that the main drivers of global demethylation are neither active mechanisms (Aicda, Tdg, and Tet1-3) nor the reduction of de novo methylation. UHRF1 protein, the essential targeting factor for DNMT1, is reduced upon transition to 2i, and so is recruitment of the maintenance methylation machinery to replication foci. Concurrently, there is global loss of H3K9me2, which is needed for chromatin binding of UHRF1. These mechanisms synergistically enforce global DNA hypomethylation in a replication-coupled fashion. Our observations establish the molecular mechanism for global demethylation in naive ESCs, which has key parallels with those operating in primordial germ cells and early embryos. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Impaired P600 in neuroleptic naive patients with first-episode schizophrenia.

PubMed

Papageorgiou, C; Kontaxakis, V P; Havaki-Kontaxaki, B J; Stamouli, S; Vasios, C; Asvestas, P; Matsopoulos, G K; Kontopantelis, E; Rabavilas, A; Uzunoglu, N; Christodoulou, G N

2001-09-17

Deficits of working memory (WM) are recognized as an important pathological feature in schizophrenia. Since the P600 component of event related potentials has been hypothesized that represents aspects of second-pass parsing processes of information processing, and is related to WM, the present study focuses on P600 elicited during a WM test in drug-naive first-episode schizophrenics (FES) compared to healthy controls. We examined 16 drug-naive first-episode schizophrenic patients and 23 healthy controls matched for age and sex. Compared with controls schizophrenic patients showed reduced P600 amplitude on left temporoparietal region and increased P600 amplitude on left occipital region. With regard to the latency, the patients exhibited significantly prolongation on right temporoparietal region. The obtained pattern of differences classified correctly 89.20% of patients. Memory performance of patients was also significantly impaired relative to controls. Our results suggest that second-pass parsing process of information processing, as indexed by P600, elicited during a WM test, is impaired in FES. Moreover, these findings lend support to the view that the auditory WM in schizophrenia involves or affects a circuitry including temporoparietal and occipital brain areas.

Nicotinic Acid Adenine Dinucleotide Phosphate Plays a Critical Role in Naive and Effector Murine T Cells but Not Natural Regulatory T Cells.

PubMed

Ali, Ramadan A; Camick, Christina; Wiles, Katherine; Walseth, Timothy F; Slama, James T; Bhattacharya, Sumit; Giovannucci, David R; Wall, Katherine A

2016-02-26

Nicotinic acid adenine dinucleotide phosphate (NAADP), the most potent Ca(2+) mobilizing second messenger discovered to date, has been implicated in Ca(2+) signaling in some lymphomas and T cell clones. In contrast, the role of NAADP in Ca(2+) signaling or the identity of the Ca(2+) stores targeted by NAADP in conventional naive T cells is less clear. In the current study, we demonstrate the importance of NAADP in the generation of Ca(2+) signals in murine naive T cells. Combining live-cell imaging methods and a pharmacological approach using the NAADP antagonist Ned-19, we addressed the involvement of NAADP in the generation of Ca(2+) signals evoked by TCR stimulation and the role of this signal in downstream physiological end points such as proliferation, cytokine production, and other responses to stimulation. We demonstrated that acidic compartments in addition to the endoplasmic reticulum were the Ca(2+) stores that were sensitive to NAADP in naive T cells. NAADP was shown to evoke functionally relevant Ca(2+) signals in both naive CD4 and naive CD8 T cells. Furthermore, we examined the role of this signal in the activation, proliferation, and secretion of effector cytokines by Th1, Th2, Th17, and CD8 effector T cells. Overall, NAADP exhibited a similar profile in mediating Ca(2+) release in effector T cells as in their counterpart naive T cells and seemed to be equally important for the function of these different subsets of effector T cells. This profile was not observed for natural T regulatory cells. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Bayesian Just-So Stories in Psychology and Neuroscience

ERIC Educational Resources Information Center

Bowers, Jeffrey S.; Davis, Colin J.

2012-01-01

According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak.…

Universal Darwinism As a Process of Bayesian Inference

PubMed Central

Campbell, John O.

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an “experiment” in the external world environment, and the results of that “experiment” or the “surprise” entailed by predicted and actual outcomes of the “experiment.” Minimization of free energy implies that the implicit measure of “surprise” experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature. PMID:27375438

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…

Interleukin-7 induces HIV replication in primary naive T cells through a nuclear factor of activated T cell (NFAT)-dependent pathway

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

Managlia, Elizabeth Z.; Landay, Alan; Al-Harthi, Lena

2006-07-05

Interleukin (IL)-7 plays several roles critical to T cell maturation, survival, and homeostasis. Because of these functions, IL-7 is under investigation as an immune-modulator for therapeutic use in lymphopenic clinical conditions, including HIV. We reported that naive T cells, typically not permissive to HIV, can be productively infected when pre-treated with IL-7. We evaluated the mechanism by which IL-7-mediates this effect. IL-7 potently up-regulated the transcriptional factor NFAT, but had no effect on NF{kappa}B. Blocking NFAT activity using a number of reagents, such as Cyclosporin A, FK-506, or the NFAT-specific inhibitor known as VIVIT peptide, all markedly reduced IL-7-mediated inductionmore » of HIV replication in naive T cells. Additional neutralization of cytokines present in IL-7-treated cultures and/or those that have NFAT-binding sequences within their promotors indicated that IL-10, IL-4, and most significantly IFN{gamma}, all contribute to IL-7-induction of HIV productive replication in naive T cells. These data clarify the mechanism by which IL-7 can overcome the block to HIV productive infection in naive T cells, despite their quiescent cell status. These findings are relevant to the treatment of HIV disease and understanding HIV pathogenesis in the naive CD4+ T cell compartment, especially in light of the vigorous pursuit of IL-7 as an in vivo immune modulator.« less

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

ERIC Educational Resources Information Center

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A. G.

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are…

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.

Clinical and Mucosal Immune Correlates of HIV-1 Semen Levels in Antiretroviral-Naive Men

PubMed Central

Marsh, Angie K.; Huibner, Sanja; Shahabi, Kamnoosh; Liu, Cindy; Contente, Tania; Nagelkerke, Nico J. D.; Kovacs, Colin; Benko, Erika; Price, Lance; MacDonald, Kelly S.; Kaul, Rupert

2017-01-01

Abstract Background. This study was done to characterize parameters associated with semen human immunodeficiency virus (HIV)-1 ribonucleic acid (RNA) viral load (VL) variability in HIV-infected, therapy-naive men. Methods. Paired blood and semen samples were collected from 30 HIV-infected, therapy-naive men who have sex with men, and 13 participants were observed longitudinally for up to 1 year. Human immunodeficiency virus RNA, bacterial load by 16S RNA, herpesvirus (Epstein-Barr virus and cytomegalovirus [CMV]) shedding, and semen cytokines/chemokines were quantified, and semen T-cell subsets were assessed by multiparameter flow cytometry. Results. Semen HIV RNA was detected at 93% of visits, with >50% of men shedding high levels of virus (defined as >5000 copies/mL). In the baseline cross-sectional analysis, an increased semen HIV VL correlated with local CMV reactivation, the semen bacterial load, and semen inflammatory cytokines, particularly interleukin (IL)-8. T cells in semen were more activated than blood, and there was an increased frequency of Th17 cells and γδ-T-cells. Subsequent prospective analysis demonstrated striking interindividual variability in HIV and CMV shedding patterns, and only semen IL-8 levels and the blood VL were independently associated with semen HIV levels. Conclusions. Several clinical and immune parameters were associated with increased HIV semen levels in antiretroviral therapy-naive men, with induction of local proinflammatory cytokines potentially acting as a common pathway. PMID:28534034

Bayesian Decision Theoretical Framework for Clustering

ERIC Educational Resources Information Center

Chen, Mo

2011-01-01

In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…

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…

Quantum-Like Representation of Non-Bayesian Inference

NASA Astrophysics Data System (ADS)

Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.

2013-01-01

This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.

Brentuximab vedotin (SGN-35) in patients with transplant-naive relapsed/refractory Hodgkin lymphoma.

PubMed

Sasse, Stephanie; Rothe, Achim; Goergen, Helen; Eichenauer, Dennis A; Lohri, Andreas; Kreher, Stephan; Jäger, Ulrich; Bangard, Christopher; Kuhnert, Georg; Böll, Boris; von Tresckow, Bastian; Engert, Andreas

2013-10-01

Only limited data are available on the role of brentuximab vedotin (SGN-35) in transplant-naive relapsed or refractory patients with Hodgkin lymphoma (HL). We thus retrospectively analyzed 14 patients with primary refractory or relapsed HL who were treated with brentuximab vedotin as single agent in a named patient program, who had not received prior high-dose chemotherapy (HDCT) and autologous stem cell transplant (ASCT) due to refractory disease (n = 9), comorbidity (n = 4) and unknown reasons (n = 1). Brentuximab vedotin resulted in an overall response rate of 71% (10/14) with five complete responses (CRs). Five of those patients with refractory disease and four patients with relevant comorbidity responded. Consolidating ASCT (n = 4) or allogeneic SCT (n = 1) was performed in five patients. Median progression-free survival (PFS) was 9 months and the median overall survival (OS) was not reached. These data indicate the therapeutic efficacy of brentuximab vedotin in chemotherapy-refractory transplant-naive patients with HL.

Bayesian analysis of rare events

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

Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into themore » probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.« less

Bayesian analysis of rare events

NASA Astrophysics Data System (ADS)

Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

Differences in Expansion Potential of Naive Chimeric Antigen Receptor T Cells from Healthy Donors and Untreated Chronic Lymphocytic Leukemia Patients.

PubMed

Hoffmann, Jean-Marc; Schubert, Maria-Luisa; Wang, Lei; Hückelhoven, Angela; Sellner, Leopold; Stock, Sophia; Schmitt, Anita; Kleist, Christian; Gern, Ulrike; Loskog, Angelica; Wuchter, Patrick; Hofmann, Susanne; Ho, Anthony D; Müller-Tidow, Carsten; Dreger, Peter; Schmitt, Michael

2017-01-01

Therapy with chimeric antigen receptor T (CART) cells for hematological malignancies has shown promising results. Effectiveness of CART cells may depend on the ratio of naive (T N ) vs. effector (T E ) T cells, T N cells being responsible for an enduring antitumor activity through maturation. Therefore, we investigated factors influencing the T N /T E ratio of CART cells. CART cells were generated upon transduction of peripheral blood mononuclear cells with a CD19.CAR-CD28-CD137zeta third generation retroviral vector under two different stimulating culture conditions: anti-CD3/anti-CD28 antibodies adding either interleukin (IL)-7/IL-15 or IL-2. CART cells were maintained in culture for 20 days. We evaluated 24 healthy donors (HDs) and 11 patients with chronic lymphocytic leukemia (CLL) for the composition of cell subsets and produced CART cells. Phenotype and functionality were tested using flow cytometry and chromium release assays. IL-7/IL-15 preferentially induced differentiation into T N , stem cell memory (T SCM : naive CD27+ CD95+), CD4+ and CXCR3+ CART cells, while IL-2 increased effector memory (T EM ), CD56+ and CD4+ T regulatory (T Reg ) CART cells. The net amplification of different CART subpopulations derived from HDs and untreated CLL patients was compared. Particularly the expansion of CD4+ CART N cells differed significantly between the two groups. For HDs, this subtype expanded >60-fold, whereas CD4+ CART N cells of untreated CLL patients expanded less than 10-fold. Expression of exhaustion marker programmed cell death 1 on CART N cells on day 10 of culture was significantly higher in patient samples compared to HD samples. As the percentage of malignant B cells was expectedly higher within patient samples, an excessive amount of B cells during culture could account for the reduced expansion potential of CART N cells in untreated CLL patients. Final T N /T E ratio stayed <0.3 despite stimulation condition for patients, whereas this ratio was >2 in

Semisupervised learning using Bayesian interpretation: application to LS-SVM.

PubMed

Adankon, Mathias M; Cheriet, Mohamed; Biem, Alain

2011-04-01

Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.

Non-Bayesian Optical Inference Machines

NASA Astrophysics Data System (ADS)

Kadar, Ivan; Eichmann, George

1987-01-01

In a recent paper, Eichmann and Caulfield) presented a preliminary exposition of optical learning machines suited for use in expert systems. In this paper, we extend the previous ideas by introducing learning as a means of reinforcement by information gathering and reasoning with uncertainty in a non-Bayesian framework2. More specifically, the non-Bayesian approach allows the representation of total ignorance (not knowing) as opposed to assuming equally likely prior distributions.

Genetic characterization and antiretroviral resistance mutations among treatment-naive HIV-infected individuals in Jiaxing, China.

PubMed

Guo, Jinlei; Yan, Yong; Zhang, Jiafeng; Ji, Jimei; Ge, Zhijian; Ge, Rui; Zhang, Xiaofei; Wang, Henghui; Chen, Zhongwen; Luo, Jianyong

2017-03-14

The aim of this study was to characterize HIV-1 genotypes and antiretroviral resistance mutations among treatment-naive HIV-infected individuals in Jiaxing, China. The HIV-1 partial polymerase (pol) genes in 93 of the 99 plasma samples were successfully amplified and analyzed. Phylogenetic analysis revealed the existence of five HIV-1 genotypes, of which the most prevalent genotype was CRF01_AE (38.7%), followed by CRF07_BC (34.4%), CRF08_BC (16.1%), subtype B/B' (5.4%), and CRF55_01B (2.1%). Besides, three types of unique recombination forms (URFs) were also observed, including C/F2/A1, CRF01_AE/B, and CRF08_BC/CRF07_BC. Among 93 amplicons, 46.2% had drug resistance-associated mutations, including 23.7% for protease inhibitors (PIs) mutations, 1.1% for nucleoside reverse transcriptase inhibitors (NRTIs) mutations, and 20.4% for non-nucleoside reverse transcriptase inhibitors (NNRTIs) mutations. Six (6.5%) out of 93 treatment-naive subjects were identified to be resistant to one or more NNRTIs, while resistance to NRTIs or PIs was not observed. Our study showed the genetic diversity of HIV-1 strains circulating in Jiaxing and a relative high proportion of antiretroviral resistance mutations among treatment-naive patients, indicating a serious challenge for HIV prevention and treatment program.

A Combined Omics Approach to Generate the Surface Atlas of Human Naive CD4+ T Cells during Early T-Cell Receptor Activation*

PubMed Central

Graessel, Anke; Hauck, Stefanie M.; von Toerne, Christine; Kloppmann, Edda; Goldberg, Tatyana; Koppensteiner, Herwig; Schindler, Michael; Knapp, Bettina; Krause, Linda; Dietz, Katharina; Schmidt-Weber, Carsten B.; Suttner, Kathrin

2015-01-01

Naive CD4+ T cells are the common precursors of multiple effector and memory T-cell subsets and possess a high plasticity in terms of differentiation potential. This stem-cell-like character is important for cell therapies aiming at regeneration of specific immunity. Cell surface proteins are crucial for recognition and response to signals mediated by other cells or environmental changes. Knowledge of cell surface proteins of human naive CD4+ T cells and their changes during the early phase of T-cell activation is urgently needed for a guided differentiation of naive T cells and may support the selection of pluripotent cells for cell therapy. Periodate oxidation and aniline-catalyzed oxime ligation technology was applied with subsequent quantitative liquid chromatography-tandem MS to generate a data set describing the surface proteome of primary human naive CD4+ T cells and to monitor dynamic changes during the early phase of activation. This led to the identification of 173 N-glycosylated surface proteins. To independently confirm the proteomic data set and to analyze the cell surface by an alternative technique a systematic phenotypic expression analysis of surface antigens via flow cytometry was performed. This screening expanded the previous data set, resulting in 229 surface proteins, which were expressed on naive unstimulated and activated CD4+ T cells. Furthermore, we generated a surface expression atlas based on transcriptome data, experimental annotation, and predicted subcellular localization, and correlated the proteomics result with this transcriptional data set. This extensive surface atlas provides an overall naive CD4+ T cell surface resource and will enable future studies aiming at a deeper understanding of mechanisms of T-cell biology allowing the identification of novel immune targets usable for the development of therapeutic treatments. PMID:25991687

BAYESIAN ESTIMATION OF THERMONUCLEAR REACTION RATES

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

Iliadis, C.; Anderson, K. S.; Coc, A.

The problem of estimating non-resonant astrophysical S -factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We presentmore » astrophysical S -factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p, γ ){sup 3}He, {sup 3}He({sup 3}He,2p){sup 4}He, and {sup 3}He( α , γ ){sup 7}Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.« less

Bayesian Exploratory Factor Analysis

PubMed Central

Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi

2014-01-01

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. PMID:25431517

Prior approval: the growth of Bayesian methods in psychology.

PubMed

Andrews, Mark; Baguley, Thom

2013-02-01

Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.

A Primer on Bayesian Analysis for Experimental Psychopathologists

PubMed Central

Krypotos, Angelos-Miltiadis; Blanken, Tessa F.; Arnaudova, Inna; Matzke, Dora; Beckers, Tom

2016-01-01

The principal goals of experimental psychopathology (EPP) research are to offer insights into the pathogenic mechanisms of mental disorders and to provide a stable ground for the development of clinical interventions. The main message of the present article is that those goals are better served by the adoption of Bayesian statistics than by the continued use of null-hypothesis significance testing (NHST). In the first part of the article we list the main disadvantages of NHST and explain why those disadvantages limit the conclusions that can be drawn from EPP research. Next, we highlight the advantages of Bayesian statistics. To illustrate, we then pit NHST and Bayesian analysis against each other using an experimental data set from our lab. Finally, we discuss some challenges when adopting Bayesian statistics. We hope that the present article will encourage experimental psychopathologists to embrace Bayesian statistics, which could strengthen the conclusions drawn from EPP research. PMID:28748068

Bayesian analysis of CCDM models

NASA Astrophysics Data System (ADS)

Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.

2017-09-01

Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3αH0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.

Enhancements to the Bayesian Infrasound Source Location Method

DTIC Science & Technology

2012-09-01

ENHANCEMENTS TO THE BAYESIAN INFRASOUND SOURCE LOCATION METHOD Omar E. Marcillo, Stephen J. Arrowsmith, Rod W. Whitaker, and Dale N. Anderson Los...ABSTRACT We report on R&D that is enabling enhancements to the Bayesian Infrasound Source Location (BISL) method for infrasound event location...the Bayesian Infrasound Source Location Method 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER

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.

Exploring the Autonomous Economic World of Children: A Mixed Methods Study of Kids' Naive Economic Theories Incorporating Ethnographic and Behavioral Economics Methodologies

ERIC Educational Resources Information Center

Jennings, Amanda Brooke

2017-01-01

Children construct meaning from their economic experiences in the form of naive theories and use these theories to explain the relationships between their actions and the outcomes. Inevitably, due to their lack of economic literacy, these theories will be incomplete. Through curriculum design that acknowledges and addresses these naive theories,…

In Vitro Measles Virus Infection of Human Lymphocyte Subsets Demonstrates High Susceptibility and Permissiveness of both Naive and Memory B Cells

PubMed Central

Laksono, Brigitta M.; Grosserichter-Wagener, Christina; de Vries, Rory D.; Langeveld, Simone A. G.; Brem, Maarten D.; van Dongen, Jacques J. M.; Koopmans, Marion P. G.

2018-01-01

ABSTRACT Measles is characterized by a transient immune suppression, leading to an increased risk of opportunistic infections. Measles virus (MV) infection of immune cells is mediated by the cellular receptor CD150, expressed by subsets of lymphocytes, dendritic cells, macrophages, and thymocytes. Previous studies showed that human and nonhuman primate memory T cells express higher levels of CD150 than naive cells and are more susceptible to MV infection. However, limited information is available about the CD150 expression and relative susceptibility to MV infection of B-cell subsets. In this study, we assessed the susceptibility and permissiveness of naive and memory T- and B-cell subsets from human peripheral blood or tonsils to in vitro MV infection. Our study demonstrates that naive and memory B cells express CD150, but at lower frequencies than memory T cells. Nevertheless, both naive and memory B cells proved to be highly permissive to MV infection. Furthermore, we assessed the susceptibility and permissiveness of various functionally distinct T and B cells, such as helper T (TH) cell subsets and IgG- and IgA-positive memory B cells, in peripheral blood and tonsils. We demonstrated that TH1TH17 cells and plasma and germinal center B cells were the subsets most susceptible and permissive to MV infection. Our study suggests that both naive and memory B cells, along with several other antigen-experienced lymphocytes, are important target cells of MV infection. Depletion of these cells potentially contributes to the pathogenesis of measles immune suppression. IMPORTANCE Measles is associated with immune suppression and is often complicated by bacterial pneumonia, otitis media, or gastroenteritis. Measles virus infects antigen-presenting cells and T and B cells, and depletion of these cells may contribute to lymphopenia and immune suppression. Measles has been associated with follicular exhaustion in lymphoid tissues in humans and nonhuman primates, emphasizing

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

Forecasting of Information Security Related Incidents: Amount of Spam Messages as a Case Study

NASA Astrophysics Data System (ADS)

Romanov, Anton; Okamoto, Eiji

With the increasing demand for services provided by communication networks, quality and reliability of such services as well as confidentiality of data transfer are becoming ones of the highest concerns. At the same time, because of growing hacker's activities, quality of provided content and reliability of its continuous delivery strongly depend on integrity of data transmission and availability of communication infrastructure, thus on information security of a given IT landscape. But, the amount of resources allocated to provide information security (like security staff, technical countermeasures and etc.) must be reasonable from the economic point of view. This fact, in turn, leads to the need to employ a forecasting technique in order to make planning of IT budget and short-term planning of potential bottlenecks. In this paper we present an approach to make such a forecasting for a wide class of information security related incidents (ISRI) — unambiguously detectable ISRI. This approach is based on different auto regression models which are widely used in financial time series analysis but can not be directly applied to ISRI time series due to specifics related to information security. We investigate and address this specifics by proposing rules (special conditions) of collection and storage of ISRI time series, adherence to which improves forecasting in this subject field. We present an application of our approach to one type of unambiguously detectable ISRI — amount of spam messages which, if not mitigated properly, could create additional load on communication infrastructure and consume significant amounts of network capacity. Finally we evaluate our approach by simulation and actual measurement.

Bayesian coronal seismology

NASA Astrophysics Data System (ADS)

Arregui, Iñigo

2018-01-01

In contrast to the situation in a laboratory, the study of the solar atmosphere has to be pursued without direct access to the physical conditions of interest. Information is therefore incomplete and uncertain and inference methods need to be employed to diagnose the physical conditions and processes. One of such methods, solar atmospheric seismology, makes use of observed and theoretically predicted properties of waves to infer plasma and magnetic field properties. A recent development in solar atmospheric seismology consists in the use of inversion and model comparison methods based on Bayesian analysis. In this paper, the philosophy and methodology of Bayesian analysis are first explained. Then, we provide an account of what has been achieved so far from the application of these techniques to solar atmospheric seismology and a prospect of possible future extensions.

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.

The Bayesian boom: good thing or bad?

PubMed Central

Hahn, Ulrike

2014-01-01

A series of high-profile critiques of Bayesian models of cognition have recently sparked controversy. These critiques question the contribution of rational, normative considerations in the study of cognition. The present article takes central claims from these critiques and evaluates them in light of specific models. Closer consideration of actual examples of Bayesian treatments of different cognitive phenomena allows one to defuse these critiques showing that they cannot be sustained across the diversity of applications of the Bayesian framework for cognitive modeling. More generally, there is nothing in the Bayesian framework that would inherently give rise to the deficits that these critiques perceive, suggesting they have been framed at the wrong level of generality. At the same time, the examples are used to demonstrate the different ways in which consideration of rationality uniquely benefits both theory and practice in the study of cognition. PMID:25152738

Achieving ventricular rate control using metoprolol in β-blocker-naive patients vs patients on chronic β-blocker therapy.

PubMed

Kuang, Patricia; Mah, Nathan D; Barton, Cassie A; Miura, Andrea J; Tanas, Laura R; Ran, Ran

2016-03-01

The objective of the study is to evaluate the difference in ventricular rate control using an intravenous (IV) metoprolol regimen commonly used in clinical practice in patients receiving chronic β-blocker therapy compared to patients considered β-blocker naive admitted to the emergency department (ED) for atrial fibrillation (AF) with rapid ventricular rate. A single-center retrospective cohort study of adult ED patients who were admitted with a rapid ventricular rate of 120 beats per minute (bpm) or greater and treated with IV metoprolol was performed. Rate control was defined as either a decrease in ventricular rate to less than 100 bpm or a 20% decrease in heart rate to less than 120 bpm after metoprolol administration. Patient demographics, differences in length of stay, and adverse events were recorded. A total of 398 patients were included in the study, with 79.4% (n=316) receiving chronic β-blocker therapy. Patients considered to be β-blocker naive were more likely to achieve successful rate control with IV metoprolol compared to patients on chronic β-blocker therapy (56.1% vs 42.4%; P=.03). β-Blocker-naive status was associated with a shorter length of stay in comparison to patients receiving chronic β-blocker therapy (1.79 vs 2.64 days; P<.01). Intravenous metoprolol for the treatment of atrial fibrillation with rapid ventricular rate was associated with a higher treatment response in patients considered β-blocker naive compared to patients receiving chronic β-blocker therapy. Copyright © 2015 Elsevier Inc. All rights reserved.

A SAS Interface for Bayesian Analysis with WinBUGS

ERIC Educational Resources Information Center

Zhang, Zhiyong; McArdle, John J.; Wang, Lijuan; Hamagami, Fumiaki

2008-01-01

Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model…

The Development of Bayesian Theory and Its Applications in Business and Bioinformatics

NASA Astrophysics Data System (ADS)

Zhang, Yifei

2018-03-01

Bayesian Theory originated from an Essay of a British mathematician named Thomas Bayes in 1763, and after its development in 20th century, Bayesian Statistics has been taking a significant part in statistical study of all fields. Due to the recent breakthrough of high-dimensional integral, Bayesian Statistics has been improved and perfected, and now it can be used to solve problems that Classical Statistics failed to solve. This paper summarizes Bayesian Statistics’ history, concepts and applications, which are illustrated in five parts: the history of Bayesian Statistics, the weakness of Classical Statistics, Bayesian Theory and its development and applications. The first two parts make a comparison between Bayesian Statistics and Classical Statistics in a macroscopic aspect. And the last three parts focus on Bayesian Theory in specific -- from introducing some particular Bayesian Statistics’ concepts to listing their development and finally their applications.

Anterior Cingulate Volumetric Alterations in Treatment-Naive Adults with ADHD: A Pilot Study

ERIC Educational Resources Information Center

Makris, Nikos; Seidman, Larry J.; Valera, Eve M.; Biederman, Joseph; Monuteaux, Michael C.; Kennedy, David N.; Caviness, Verne S., Jr.; Bush, George; Crum, Katherine; Brown, Ariel B.; Faraone, Stephen V.

2010-01-01

Objective: We sought to examine preliminary results of brain alterations in anterior cingulate cortex (ACC) in treatment-naive adults with ADHD. The ACC is a central brain node for the integration of cognitive control and allocation of attention, affect and drive. Thus its anatomical alteration may give rise to impulsivity, hyperactivity and…

Scanning probe acceleration microscopy (SPAM) in fluids: Mapping mechanical properties of surfaces at the nanoscale

NASA Astrophysics Data System (ADS)

Legleiter, Justin; Park, Matthew; Cusick, Brian; Kowalewski, Tomasz

2006-03-01

One of the major thrusts in proximal probe techniques is combination of imaging capabilities with simultaneous measurements of physical properties. In tapping mode atomic force microscopy (TMAFM), the most straightforward way to accomplish this goal is to reconstruct the time-resolved force interaction between the tip and surface. These tip-sample forces can be used to detect interactions (e.g., binding sites) and map material properties with nanoscale spatial resolution. Here, we describe a previously unreported approach, which we refer to as scanning probe acceleration microscopy (SPAM), in which the TMAFM cantilever acts as an accelerometer to extract tip-sample forces during imaging. This method utilizes the second derivative of the deflection signal to recover the tip acceleration trajectory. The challenge in such an approach is that with real, noisy data, the second derivative of the signal is strongly dominated by the noise. This problem is solved by taking advantage of the fact that most of the information about the deflection trajectory is contained in the higher harmonics, making it possible to filter the signal by “comb” filtering, i.e., by taking its Fourier transform and inverting it while selectively retaining only the intensities at integer harmonic frequencies. Such a comb filtering method works particularly well in fluid TMAFM because of the highly distorted character of the deflection signal. Numerical simulations and in situ TMAFM experiments on supported lipid bilayer patches on mica are reported to demonstrate the validity of this approach.

Advances in Bayesian Modeling in Educational Research

ERIC Educational Resources Information Center

Levy, Roy

2016-01-01

In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…

Bayesian Statistics for Biological Data: Pedigree Analysis

ERIC Educational Resources Information Center

Stanfield, William D.; Carlton, Matthew A.

2004-01-01

The use of Bayes' formula is applied to the biological problem of pedigree analysis to show that the Bayes' formula and non-Bayesian or "classical" methods of probability calculation give different answers. First year college students of biology can be introduced to the Bayesian statistics.

Bayesian networks in neuroscience: a survey.

PubMed

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.

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.

Teaching Bayesian Statistics in a Health Research Methodology Program

ERIC Educational Resources Information Center

Pullenayegum, Eleanor M.; Thabane, Lehana

2009-01-01

Despite the appeal of Bayesian methods in health research, they are not widely used. This is partly due to a lack of courses in Bayesian methods at an appropriate level for non-statisticians in health research. Teaching such a course can be challenging because most statisticians have been taught Bayesian methods using a mathematical approach, and…

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.

An Intuitive Dashboard for Bayesian Network Inference

NASA Astrophysics Data System (ADS)

Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.

2014-03-01

Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.

KLF4 Nuclear Export Requires ERK Activation and Initiates Exit from Naive Pluripotency.

PubMed

Dhaliwal, Navroop K; Miri, Kamelia; Davidson, Scott; Tamim El Jarkass, Hala; Mitchell, Jennifer A

2018-04-10

Cooperative action of a transcription factor complex containing OCT4, SOX2, NANOG, and KLF4 maintains the naive pluripotent state; however, less is known about the mechanisms that disrupt this complex, initiating exit from pluripotency. We show that, as embryonic stem cells (ESCs) exit pluripotency, KLF4 protein is exported from the nucleus causing rapid decline in Nanog and Klf4 transcription; as a result, KLF4 is the first pluripotency transcription factor removed from transcription-associated complexes during differentiation. KLF4 nuclear export requires ERK activation, and phosphorylation of KLF4 by ERK initiates interaction of KLF4 with nuclear export factor XPO1, leading to KLF4 export. Mutation of the ERK phosphorylation site in KLF4 (S132) blocks KLF4 nuclear export, the decline in Nanog, Klf4, and Sox2 mRNA, and differentiation. These findings demonstrate that relocalization of KLF4 to the cytoplasm is a critical first step in exit from the naive pluripotent state and initiation of ESC differentiation. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Power in Bayesian Mediation Analysis for Small Sample Research.

PubMed

Miočević, Milica; MacKinnon, David P; Levy, Roy

2017-01-01

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results.

A Bayesian model of stereopsis depth and motion direction discrimination.

PubMed

Read, J C A

2002-02-01

The extraction of stereoscopic depth from retinal disparity, and motion direction from two-frame kinematograms, requires the solution of a correspondence problem. In previous psychophysical work [Read and Eagle (2000) Vision Res 40: 3345-3358], we compared the performance of the human stereopsis and motion systems with correlated and anti-correlated stimuli. We found that, although the two systems performed similarly for narrow-band stimuli, broadband anti-correlated kinematograms produced a strong perception of reversed motion, whereas the stereograms appeared merely rivalrous. I now model these psychophysical data with a computational model of the correspondence problem based on the known properties of visual cortical cells. Noisy retinal images are filtered through a set of Fourier channels tuned to different spatial frequencies and orientations. Within each channel, a Bayesian analysis incorporating a prior preference for small disparities is used to assess the probability of each possible match. Finally, information from the different channels is combined to arrive at a judgement of stimulus disparity. Each model system--stereopsis and motion--has two free parameters: the amount of noise they are subject to, and the strength of their preference for small disparities. By adjusting these parameters independently for each system, qualitative matches are produced to psychophysical data, for both correlated and anti-correlated stimuli, across a range of spatial frequency and orientation bandwidths. The motion model is found to require much higher noise levels and a weaker preference for small disparities. This makes the motion model more tolerant of poor-quality reverse-direction false matches encountered with anti-correlated stimuli, matching the strong perception of reversed motion that humans experience with these stimuli. In contrast, the lower noise level and tighter prior preference used with the stereopsis model means that it performs close to chance with

Bayesian B-spline mapping for dynamic quantitative traits.

PubMed

Xing, Jun; Li, Jiahan; Yang, Runqing; Zhou, Xiaojing; Xu, Shizhong

2012-04-01

Owing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.

Prevalence of Dyslipidemia Among Antiretroviral-Naive HIV-Infected Individuals in China

PubMed Central

Shen, Yinzhong; Wang, Jiangrong; Wang, Zhenyan; Qi, Tangkai; Song, Wei; Tang, Yang; Liu, Li; Zhang, Renfang; Lu, Hongzhou

2015-01-01

Abstract Little is known about the epidemiological features of dyslipidemia among antiretroviral-naive HIV-infected individuals in China. We used a cross-sectional study design to estimate the prevalence of dyslipidemia in this population, and to identify risk factors associated with the presence of dyslipidemia. One thousand five hundred and eighteen antiretroviral-naive HIV-infected individuals and 347 HIV-negative subjects in China were enrolled during 2009 to 2010. Demographics and medical histories were recorded. After an overnight fast, serum samples were collected to measure lipid levels. Factors associated with the presence of dyslipidemia were analyzed by logistic regression. Mean total cholesterol (TC), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL) levels were lower in HIV-positive than HIV-negative subjects, but mean triglyceride (TG) was higher in HIV-positive subjects. The overall prevalence of dyslipidemia in HIV-positive and HIV-negative groups did not differ (75.6% vs. 73.7%, P = 0.580). However, the prevalence of high TC (8.4% vs. 28.2%, P < 0.001) and high LDL (8.5% vs. 62.6%, P < 0.001) was lower in HIV-positive than HIV-negative subjects, and the prevalence of high TG (33.9% vs. 17.0%, P < 0.001) and low HDL (59.6% vs. 11.2%, P < 0.001) was higher in HIV-positive than HIV-negative subjects. Logistic analysis showed that HIV positivity was significantly associated with both an increased risk of high TG and low HDL and a decreased risk of high TC and high LDL. The mean levels of TC, of LDL and of HDL showed an increasing trend with increasing CD4 count in HIV-positive subjects. Multivariable logistic regression found that lower CD4 count was significantly associated with both an increased risk of high TG and low HDL and a decreased risk of high TC in HIV-positive subjects. Among antiretroviral-naive HIV-infected Chinese adults, there was a high prevalence of dyslipidemia characterized by

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…

Particle identification in ALICE: a Bayesian approach

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, S.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Benacek, P.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Dénes, E.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, A.; Gheata, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, A. S.; Gonzalez, V.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Haake, R.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, H.; Kim, J. S.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kostarakis, P.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; León Vargas, H.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martin Blanco, J.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Montes, E.; Moreira De Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paić, G.; Pal, S. K.; Pan, J.; Pandey, A. K.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Pereira Da Costa, H.; Peresunko, D.; Pérez Lara, C. E.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shahzad, M. I.; Shangaraev, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Souza, R. D. de; Sozzi, F.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Valencia Palomo, L.; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yang, H.; Yang, P.; Yano, S.; Yasin, Z.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.

2016-05-01

We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian PID approach for charged pions, kaons and protons in the central barrel of ALICE is studied. PID is performed via measurements of specific energy loss ( d E/d x) and time of flight. PID efficiencies and misidentification probabilities are extracted and compared with Monte Carlo simulations using high-purity samples of identified particles in the decay channels K0S → π-π+, φ→ K-K+, and Λ→ p π- in p-Pb collisions at √{s_{NN}}=5.02 TeV. In order to thoroughly assess the validity of the Bayesian approach, this methodology was used to obtain corrected pT spectra of pions, kaons, protons, and D0 mesons in pp collisions at √{s}=7 TeV. In all cases, the results using Bayesian PID were found to be consistent with previous measurements performed by ALICE using a standard PID approach. For the measurement of D0 → K-π+, it was found that a Bayesian PID approach gave a higher signal-to-background ratio and a similar or larger statistical significance when compared with standard PID selections, despite a reduced identification efficiency. Finally, we present an exploratory study of the measurement of Λc+ → p K-π+ in pp collisions at √{s}=7 TeV, using the Bayesian approach for the identification of its decay products.

Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media

NASA Astrophysics Data System (ADS)

Candra Permana, Fahmi; Rosmansyah, Yusep; Setiawan Abdullah, Atje

2017-10-01

Students activity on social media can provide implicit knowledge and new perspectives for an educational system. Sentiment analysis is a part of text mining that can help to analyze and classify the opinion data. This research uses text mining and naive Bayes method as opinion classifier, to be used as an alternative methods in the process of evaluating studentss satisfaction for educational institution. Based on test results, this system can determine the opinion classification in Bahasa Indonesia using naive Bayes as opinion classifier with accuracy level of 84% correct, and the comparison between the existing system and the proposed system to evaluate students satisfaction in learning process, there is only a difference of 16.49%.

Phage-Mediated Competitive Chemiluminescent Immunoassay for Detecting Cry1Ab Toxin by Using an Anti-Idiotypic Camel Nanobody.

PubMed

Qiu, Yulou; Li, Pan; Dong, Sa; Zhang, Xiaoshuai; Yang, Qianru; Wang, Yulong; Ge, Jing; Hammock, Bruce D; Zhang, Cunzheng; Liu, Xianjin

2018-01-31

Cry toxins have been widely used in genetically modified organisms for pest control, raising public concern regarding their effects on the natural environment and food safety. In this work, a phage-mediated competitive chemiluminescent immunoassay (c-CLIA) was developed for determination of Cry1Ab toxin using anti-idiotypic camel nanobodies. By extracting RNA from camels' peripheral blood lymphocytes, a naive phage-displayed nanobody library was established. Using anti-Cry1Ab toxin monoclonal antibodies (mAbs) against the library for anti-idiotypic antibody screening, four anti-idiotypic nanobodies were selected and confirmed to be specific for anti-Cry1Ab mAb binding. Thereafter, a c-CLIA was developed for detection of Cry1Ab toxin based on anti-idiotypic camel nanobodies and employed for sample testing. The results revealed a half-inhibition concentration of developed assay to be 42.68 ± 2.54 ng/mL, in the linear range of 10.49-307.1 ng/mL. The established method is highly specific for Cry1Ab recognition, with negligible cross-reactivity for other Cry toxins. For spiked cereal samples, the recoveries of Cry1Ab toxin ranged from 77.4% to 127%, with coefficient of variation of less than 9%. This study demonstrated that the competitive format based on phage-displayed anti-idiotypic nanobodies can provide an alternative strategy for Cry toxin detection.

Bayesian analyses of time-interval data for environmental radiation monitoring.

PubMed

Luo, Peng; Sharp, Julia L; DeVol, Timothy A

2013-01-01

Time-interval (time difference between two consecutive pulses) analysis based on the principles of Bayesian inference was investigated for online radiation monitoring. Using experimental and simulated data, Bayesian analysis of time-interval data [Bayesian (ti)] was compared with Bayesian and a conventional frequentist analysis of counts in a fixed count time [Bayesian (cnt) and single interval test (SIT), respectively]. The performances of the three methods were compared in terms of average run length (ARL) and detection probability for several simulated detection scenarios. Experimental data were acquired with a DGF-4C system in list mode. Simulated data were obtained using Monte Carlo techniques to obtain a random sampling of the Poisson distribution. All statistical algorithms were developed using the R Project for statistical computing. Bayesian analysis of time-interval information provided a similar detection probability as Bayesian analysis of count information, but the authors were able to make a decision with fewer pulses at relatively higher radiation levels. In addition, for the cases with very short presence of the source (< count time), time-interval information is more sensitive to detect a change than count information since the source data is averaged by the background data over the entire count time. The relationships of the source time, change points, and modifications to the Bayesian approach for increasing detection probability are presented.

Variations on Bayesian Prediction and Inference

DTIC Science & Technology

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

Power in Bayesian Mediation Analysis for Small Sample Research

PubMed Central

Miočević, Milica; MacKinnon, David P.; Levy, Roy

2018-01-01

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results. PMID:29662296

In Vitro Measles Virus Infection of Human Lymphocyte Subsets Demonstrates High Susceptibility and Permissiveness of both Naive and Memory B Cells.

PubMed

Laksono, Brigitta M; Grosserichter-Wagener, Christina; de Vries, Rory D; Langeveld, Simone A G; Brem, Maarten D; van Dongen, Jacques J M; Katsikis, Peter D; Koopmans, Marion P G; van Zelm, Menno C; de Swart, Rik L

2018-04-15

Measles is characterized by a transient immune suppression, leading to an increased risk of opportunistic infections. Measles virus (MV) infection of immune cells is mediated by the cellular receptor CD150, expressed by subsets of lymphocytes, dendritic cells, macrophages, and thymocytes. Previous studies showed that human and nonhuman primate memory T cells express higher levels of CD150 than naive cells and are more susceptible to MV infection. However, limited information is available about the CD150 expression and relative susceptibility to MV infection of B-cell subsets. In this study, we assessed the susceptibility and permissiveness of naive and memory T- and B-cell subsets from human peripheral blood or tonsils to in vitro MV infection. Our study demonstrates that naive and memory B cells express CD150, but at lower frequencies than memory T cells. Nevertheless, both naive and memory B cells proved to be highly permissive to MV infection. Furthermore, we assessed the susceptibility and permissiveness of various functionally distinct T and B cells, such as helper T (T H ) cell subsets and IgG- and IgA-positive memory B cells, in peripheral blood and tonsils. We demonstrated that T H 1T H 17 cells and plasma and germinal center B cells were the subsets most susceptible and permissive to MV infection. Our study suggests that both naive and memory B cells, along with several other antigen-experienced lymphocytes, are important target cells of MV infection. Depletion of these cells potentially contributes to the pathogenesis of measles immune suppression. IMPORTANCE Measles is associated with immune suppression and is often complicated by bacterial pneumonia, otitis media, or gastroenteritis. Measles virus infects antigen-presenting cells and T and B cells, and depletion of these cells may contribute to lymphopenia and immune suppression. Measles has been associated with follicular exhaustion in lymphoid tissues in humans and nonhuman primates, emphasizing the

Bayesian inference for psychology. Part II: Example applications with JASP.

PubMed

Wagenmakers, Eric-Jan; Love, Jonathon; Marsman, Maarten; Jamil, Tahira; Ly, Alexander; Verhagen, Josine; Selker, Ravi; Gronau, Quentin F; Dropmann, Damian; Boutin, Bruno; Meerhoff, Frans; Knight, Patrick; Raj, Akash; van Kesteren, Erik-Jan; van Doorn, Johnny; Šmíra, Martin; Epskamp, Sacha; Etz, Alexander; Matzke, Dora; de Jong, Tim; van den Bergh, Don; Sarafoglou, Alexandra; Steingroever, Helen; Derks, Koen; Rouder, Jeffrey N; Morey, Richard D

2018-02-01

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

Artificial and Bayesian Neural Networks

PubMed

Korhani Kangi, Azam; Bahrampour, Abbas

2018-02-26

Introduction and purpose: In recent years the use of neural networks without any premises for investigation of prognosis in analyzing survival data has increased. Artificial neural networks (ANN) use small processors with a continuous network to solve problems inspired by the human brain. Bayesian neural networks (BNN) constitute a neural-based approach to modeling and non-linearization of complex issues using special algorithms and statistical methods. Gastric cancer incidence is the first and third ranking for men and women in Iran, respectively. The aim of the present study was to assess the value of an artificial neural network and a Bayesian neural network for modeling and predicting of probability of gastric cancer patient death. Materials and Methods: In this study, we used information on 339 patients aged from 20 to 90 years old with positive gastric cancer, referred to Afzalipoor and Shahid Bahonar Hospitals in Kerman City from 2001 to 2015. The three layers perceptron neural network (ANN) and the Bayesian neural network (BNN) were used for predicting the probability of mortality using the available data. To investigate differences between the models, sensitivity, specificity, accuracy and the area under receiver operating characteristic curves (AUROCs) were generated. Results: In this study, the sensitivity and specificity of the artificial neural network and Bayesian neural network models were 0.882, 0.903 and 0.954, 0.909, respectively. Prediction accuracy and the area under curve ROC for the two models were 0.891, 0.944 and 0.935, 0.961. The age at diagnosis of gastric cancer was most important for predicting survival, followed by tumor grade, morphology, gender, smoking history, opium consumption, receiving chemotherapy, presence of metastasis, tumor stage, receiving radiotherapy, and being resident in a village. Conclusion: The findings of the present study indicated that the Bayesian neural network is preferable to an artificial neural network for

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.

Using Bayesian belief networks in adaptive management.

Treesearch

J.B. Nyberg; B.G. Marcot; R. Sulyma

2006-01-01

Bayesian belief and decision networks are relatively new modeling methods that are especially well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes for practioners of adaptive management, from illustrating system relations conceptually to...

Maraviroc: perspectives for use in antiretroviral-naive HIV-1-infected patients.

PubMed

Vandekerckhove, Linos; Verhofstede, Chris; Vogelaers, Dirk

2009-06-01

Maraviroc (Pfizer's UK-427857, Selzentry or Celsentri outside the USA) is the first agent in the new class of oral HIV-1 entry inhibitors to acquire approval by the US Food and Drug Administration and the European Medicine Agency. Considering the mechanism of action, it is expected that this drug will be effective only in a subpopulation of HIV-1-infected people, namely those harbouring the R5 virus. The favourable toxicity profile of the drug has been demonstrated in Phase III clinical trials in treatment-naive (MERIT) and treatment-experienced (MOTIVATE) patients. In the latter population, maraviroc showed a superior antiviral efficacy and immunological activity compared with optimized backbone therapy + placebo. However, in MERIT, a prospective double-blind, randomized trial in treatment-naive patients, maraviroc + zidovudine/lamivudine failed to prove non-inferiority to efavirenz + zidovudine/lamivudine as standard of care regimen in the 48 week intention-to-treat analysis. Using an assay with higher sensitivity for minority CXCR4-using (X4) HIV variants (the enhanced Trofile assay-Monogram), non-inferiority was reached for the maraviroc- versus efavirenz-based combination. These data indicate the important impact of the sensitivity of tropism testing on treatment outcome of maraviroc-containing regimens. This paper discusses both the prospective and retrospective analyses of the MERIT data and highlights the impact of these results on daily practice in HIV care.

Bayesian calibration for forensic age estimation.

PubMed

Ferrante, Luigi; Skrami, Edlira; Gesuita, Rosaria; Cameriere, Roberto

2015-05-10

Forensic medicine is increasingly called upon to assess the age of individuals. Forensic age estimation is mostly required in relation to illegal immigration and identification of bodies or skeletal remains. A variety of age estimation methods are based on dental samples and use of regression models, where the age of an individual is predicted by morphological tooth changes that take place over time. From the medico-legal point of view, regression models, with age as the dependent random variable entail that age tends to be overestimated in the young and underestimated in the old. To overcome this bias, we describe a new full Bayesian calibration method (asymmetric Laplace Bayesian calibration) for forensic age estimation that uses asymmetric Laplace distribution as the probability model. The method was compared with three existing approaches (two Bayesian and a classical method) using simulated data. Although its accuracy was comparable with that of the other methods, the asymmetric Laplace Bayesian calibration appears to be significantly more reliable and robust in case of misspecification of the probability model. The proposed method was also applied to a real dataset of values of the pulp chamber of the right lower premolar measured on x-ray scans of individuals of known age. Copyright © 2015 John Wiley & Sons, Ltd.

Bayesian networks in neuroscience: a survey

PubMed Central

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind–morphological, electrophysiological, -omics and neuroimaging–, thereby broadening the scope–molecular, cellular, structural, functional, cognitive and medical– of the brain aspects to be studied. PMID:25360109

Bayesian randomized clinical trials: From fixed to adaptive design.

PubMed

Yin, Guosheng; Lam, Chi Kin; Shi, Haolun

2017-08-01

Randomized controlled studies are the gold standard for phase III clinical trials. Using α-spending functions to control the overall type I error rate, group sequential methods are well established and have been dominating phase III studies. Bayesian randomized design, on the other hand, can be viewed as a complement instead of competitive approach to the frequentist methods. For the fixed Bayesian design, the hypothesis testing can be cast in the posterior probability or Bayes factor framework, which has a direct link to the frequentist type I error rate. Bayesian group sequential design relies upon Bayesian decision-theoretic approaches based on backward induction, which is often computationally intensive. Compared with the frequentist approaches, Bayesian methods have several advantages. The posterior predictive probability serves as a useful and convenient tool for trial monitoring, and can be updated at any time as the data accrue during the trial. The Bayesian decision-theoretic framework possesses a direct link to the decision making in the practical setting, and can be modeled more realistically to reflect the actual cost-benefit analysis during the drug development process. Other merits include the possibility of hierarchical modeling and the use of informative priors, which would lead to a more comprehensive utilization of information from both historical and longitudinal data. From fixed to adaptive design, we focus on Bayesian randomized controlled clinical trials and make extensive comparisons with frequentist counterparts through numerical studies. Copyright © 2017 Elsevier Inc. All rights reserved.

BATSE gamma-ray burst line search. 2: Bayesian consistency methodology

NASA Technical Reports Server (NTRS)

Band, D. L.; Ford, L. A.; Matteson, J. L.; Briggs, M.; Paciesas, W.; Pendleton, G.; Preece, R.; Palmer, D.; Teegarden, B.; Schaefer, B.

1994-01-01

We describe a Bayesian methodology to evaluate the consistency between the reported Ginga and Burst and Transient Source Experiment (BATSE) detections of absorption features in gamma-ray burst spectra. Currently no features have been detected by BATSE, but this methodology will still be applicable if and when such features are discovered. The Bayesian methodology permits the comparison of hypotheses regarding the two detectors' observations and makes explicit the subjective aspects of our analysis (e.g., the quantification of our confidence in detector performance). We also present non-Bayesian consistency statistics. Based on preliminary calculations of line detectability, we find that both the Bayesian and non-Bayesian techniques show that the BATSE and Ginga observations are consistent given our understanding of these detectors.

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…

Additive Genetic Variability and the Bayesian Alphabet

PubMed Central

Gianola, Daniel; de los Campos, Gustavo; Hill, William G.; Manfredi, Eduardo; Fernando, Rohan

2009-01-01

The use of all available molecular markers in statistical models for prediction of quantitative traits has led to what could be termed a genomic-assisted selection paradigm in animal and plant breeding. This article provides a critical review of some theoretical and statistical concepts in the context of genomic-assisted genetic evaluation of animals and crops. First, relationships between the (Bayesian) variance of marker effects in some regression models and additive genetic variance are examined under standard assumptions. Second, the connection between marker genotypes and resemblance between relatives is explored, and linkages between a marker-based model and the infinitesimal model are reviewed. Third, issues associated with the use of Bayesian models for marker-assisted selection, with a focus on the role of the priors, are examined from a theoretical angle. The sensitivity of a Bayesian specification that has been proposed (called “Bayes A”) with respect to priors is illustrated with a simulation. Methods that can solve potential shortcomings of some of these Bayesian regression procedures are discussed briefly. PMID:19620397

Postsurgical prescriptions for opioid naive patients and association with overdose and misuse: retrospective cohort study

PubMed Central

Agniel, Denis; Beam, Andrew; Yorkgitis, Brian; Bicket, Mark; Homer, Mark; Fox, Kathe P; Knecht, Daniel B; McMahill-Walraven, Cheryl N; Palmer, Nathan; Kohane, Isaac

2018-01-01

Abstract Objective To quantify the effects of varying opioid prescribing patterns after surgery on dependence, overdose, or abuse in an opioid naive population. Design Retrospective cohort study. Setting Surgical claims from a linked medical and pharmacy administrative database of 37 651 619 commercially insured patients between 2008 and 2016. Participants 1 015 116 opioid naive patients undergoing surgery. Main outcome measures Use of oral opioids after discharge as defined by refills and total dosage and duration of use. The primary outcome was a composite of misuse identified by a diagnostic code for opioid dependence, abuse, or overdose. Results 568 612 (56.0%) patients received postoperative opioids, and a code for abuse was identified for 5906 patients (0.6%, 183 per 100 000 person years). Total duration of opioid use was the strongest predictor of misuse, with each refill and additional week of opioid use associated with an adjusted increase in the rate of misuse of 44.0% (95% confidence interval 40.8% to 47.2%, P<0.001), and 19.9% increase in hazard (18.5% to 21.4%, P<0.001), respectively. Conclusions Each refill and week of opioid prescription is associated with a large increase in opioid misuse among opioid naive patients. The data from this study suggest that duration of the prescription rather than dosage is more strongly associated with ultimate misuse in the early postsurgical period. The analysis quantifies the association of prescribing choices on opioid misuse and identifies levers for possible impact. PMID:29343479

Virological Failure and HIV-1 Drug Resistance Mutations among Naive and Antiretroviral Pre-Treated Patients Entering the ESTHER Program of Calmette Hospital in Cambodia

PubMed Central

Limsreng, Setha; Him, Sovanvatey; Nouhin, Janin; Hak, Chanroeurn; Srun, Chanvatey; Viretto, Gerald; Ouk, Vara; Delfraissy, Jean Francois; Ségéral, Olivier

2014-01-01

Introduction In resource limited settings, patients entering an antiretroviral therapy (ART) program comprise ART naive and ART pre-treated patients who may show differential virological outcomes. Methods This retrospective study, conducted in 2010–2012 in the HIV clinic of Calmette Hospital located in Phnom Penh (Cambodia) assessed virological failure (VF) rates and patterns of drug resistance of naive and pre-treated patients. Naive and ART pre-treated patients were included when a Viral Load (VL) was performed during the first year of ART for naive subjects or at the first consultation for pre-treated individuals. Patients showing Virological failure (VF) (>1,000 copies/ml) underwent HIV DR genotyping testing. Interpretation of drug resistance mutations was done according to 2013 version 23 ANRS algorithms. Results On a total of 209 patients, 164 (78.4%) were naive and 45 (21.5%) were ART pre-treated. Their median initial CD4 counts were 74 cells/mm3 (IQR: 30–194) and 279 cells/mm3 (IQR: 103–455) (p<0.001), respectively. Twenty seven patients (12.9%) exhibited VF (95% CI: 8.6–18.2%), including 10 naive (10/164, 6.0%) and 17 pre-treated (17/45, 37.8%) patients (p<0.001). Among these viremic patients, twenty-two (81.4%) were sequenced in reverse transcriptase and protease coding regions. Overall, 19 (86.3%) harbored ≥1 drug resistance mutations (DRMs) whereas 3 (all belonging to pre-treated patients) harbored wild-types viruses. The most frequent DRMs were M184V (86.3%), K103N (45.5%) and thymidine analog mutations (TAMs) (40.9%). Two (13.3%) pre-treated patients harbored viruses that showed a multi-nucleos(t)ide resistance including Q151M, K65R, E33A/D, E44A/D mutations. Conclusion In Cambodia, VF rates were low for naive patients but the emergence of DRMs to NNRTI and 3TC occurred relatively quickly in this subgroup. In pre-treated patients, VF rates were much higher and TAMs were relatively common. HIV genotypic assays before ART initiation and for

Induction of cross-priming of naive CD8+ T lymphocytes by recombinant bacillus Calmette-Guerin that secretes heat shock protein 70-major membrane protein-II fusion protein.

PubMed

Mukai, Tetsu; Maeda, Yumi; Tamura, Toshiki; Matsuoka, Masanori; Tsukamoto, Yumiko; Makino, Masahiko

2009-11-15

Because Mycobacterium bovis bacillus Calmette-Guérin (BCG) unconvincingly activates human naive CD8(+) T cells, a rBCG (BCG-70M) that secretes a fusion protein comprising BCG-derived heat shock protein (HSP)70 and Mycobacterium leprae-derived major membrane protein (MMP)-II, one of the immunodominant Ags of M. leprae, was newly constructed to potentiate the ability of activating naive CD8(+) T cells through dendritic cells (DC). BCG-70M secreted HSP70-MMP-II fusion protein in vitro, which stimulated DC to produce IL-12p70 through TLR2. BCG-70M-infected DC activated not only memory and naive CD8(+) T cells, but also CD4(+) T cells of both types to produce IFN-gamma. The activation of these naive T cells by BCG-70M was dependent on the MHC and CD86 molecules on BCG-70M-infected DC, and was significantly inhibited by pretreatment of DC with chloroquine. Both brefeldin A and lactacystin significantly inhibited the activation of naive CD8(+) T cells by BCG-70M through DC. Thus, the CD8(+) T cell activation may be induced by cross-presentation of Ags through a TAP- and proteosome-dependent cytosolic pathway. When naive CD8(+) T cells were stimulated by BCG-70M-infected DC in the presence of naive CD4(+) T cells, CD62L(low)CD8(+) T cells and perforin-producing CD8(+) T cells were efficiently produced. MMP-II-reactive CD4(+) and CD8(+) memory T cells were efficiently produced in C57BL/6 mice by infection with BCG-70M. These results indicate that BCG-70M activated DC, CD4(+) T cells, and CD8(+) T cells, and the combination of HSP70 and MMP-II may be useful for inducing better T cell activation.

Computational Neuropsychology and Bayesian Inference.

PubMed

Parr, Thomas; Rees, Geraint; Friston, Karl J

2018-01-01

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine 'prior' beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology - optimal inference with suboptimal priors - and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient's behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.

Computational Neuropsychology and Bayesian Inference

PubMed Central

Parr, Thomas; Rees, Geraint; Friston, Karl J.

2018-01-01

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology. PMID:29527157

BMDS: A Collection of R Functions for Bayesian Multidimensional Scaling

ERIC Educational Resources Information Center

Okada, Kensuke; Shigemasu, Kazuo

2009-01-01

Bayesian multidimensional scaling (MDS) has attracted a great deal of attention because: (1) it provides a better fit than do classical MDS and ALSCAL; (2) it provides estimation errors of the distances; and (3) the Bayesian dimension selection criterion, MDSIC, provides a direct indication of optimal dimensionality. However, Bayesian MDS is not…

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...

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.

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

A Tutorial in Bayesian Potential Outcomes Mediation Analysis.

PubMed

Miočević, Milica; Gonzalez, Oscar; Valente, Matthew J; MacKinnon, David P

2018-01-01

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

Cognitive Performance Under Electroconvulsive Therapy (ECT) in ECT-Naive Treatment-Resistant Patients With Major Depressive Disorder.

PubMed

Ziegelmayer, Christoph; Hajak, Göran; Bauer, Anne; Held, Marion; Rupprecht, Rainer; Trapp, Wolfgang

2017-06-01

Although electroconvulsive therapy (ECT) is considered a safe and highly effective treatment option for major depressive disorder, there are still some reservations with regard to possible adverse cognitive adverse effects. This is the case despite a large body of evidence showing that these deficits are transient and that there even seems to be a long-term improvement of cognitive functioning level. However, most data concerning cognitive adverse effects stem from studies using mixed samples of treatment-resistant and non-treatment-resistant as well as ECT-naive and non-ECT-naive subjects. Furthermore, neurocognitive measures might partly be sensitive to practice effects and improvements in depressive symptom level. We examined neurocognitive performance in a sample of 20 treatment-resistant and ECT-naive subjects using repeatable neurocognitive tests, whereas changes in depressive symptom level were controlled. Cognitive functioning level was assessed before (baseline), 1 week, and 6 months (follow-up 1 and 2) after (12 to) 15 sessions of unilateral ECT treatment. No adverse cognitive effects were observed in any of the cognitive domains examined. Instead, a significant improvement in verbal working memory performance was found from baseline to follow-up 2. When changes in depressive symptom levels were controlled statistically, this improvement was no longer seen. Although findings that ECT does not lead to longer lasting cognitive deficits caused by ECT were confirmed, our study adds evidence that previous results of a beneficial effect of ECT on cognition might be questioned.

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

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.

Bayesian markets to elicit private information.

PubMed

Baillon, Aurélien

2017-07-25

Financial markets reveal what investors think about the future, and prediction markets are used to forecast election results. Could markets also encourage people to reveal private information, such as subjective judgments (e.g., "Are you satisfied with your life?") or unverifiable facts? This paper shows how to design such markets, called Bayesian markets. People trade an asset whose value represents the proportion of affirmative answers to a question. Their trading position then reveals their own answer to the question. The results of this paper are based on a Bayesian setup in which people use their private information (their "type") as a signal. Hence, beliefs about others' types are correlated with one's own type. Bayesian markets transform this correlation into a mechanism that rewards truth telling. These markets avoid two complications of alternative methods: they need no knowledge of prior information and no elicitation of metabeliefs regarding others' signals.

CCR6+ Th cell distribution differentiates systemic lupus erythematosus patients based on anti-dsDNA antibody status.

PubMed

Zhong, Wei; Jiang, Zhenyu; Wu, Jiang; Jiang, Yanfang; Zhao, Ling

2018-01-01

Systemic lupus erythematosus (SLE) disease has been shown to be associated with the generation of multiple auto-antibodies. Among these, anti-dsDNA antibodies (anti-DNAs) are specific and play a pathogenic role in SLE. Indeed, anti-DNA + SLE patients display a worse disease course. The generation of these pathogenic anti-DNAs has been attributed to the interaction between aberrant T helper (Th) cells and autoimmune B cells. Thus, in this study we have investigated whether CCR6 + Th cells have the ability to differentiate SLE patients based on anti-DNA status, and if their distribution has any correlation with disease activity. We recruited 25 anti-DNA + and 25 anti-DNA - treatment-naive onset SLE patients, matched for various clinical characteristics in our nested matched case-control study. CCR6 + Th cells and their additional subsets were analyzed in each patient by flow cytometry. Anti-DNA + SLE patients specifically had a higher percentage of Th cells expressing CCR6 and CXCR3. Further analysis of CCR6 + Th cell subsets showed that anti-DNA + SLE patients had elevated proportions of Th9, Th17, Th17.1 and CCR4/CXCR3 double-negative (DN) cells. However, the proportions of CCR6 - Th subsets, including Th1 and Th2 cells, did not show any association with anti-DNA status. Finally, we identified a correlation between CCR6 + Th subsets and clinical indicators, specifically in anti-DNA + SLE patients. Our data indicated that CCR6 + Th cells and their subsets were elevated and correlated with disease activity in anti-DNA + SLE patients. We speculated that CCR6 + Th cells may contribute to distinct disease severity in anti-DNA + SLE patients.

Naive-like Conversion Overcomes the Limited Differentiation Capacity of Induced Pluripotent Stem Cells*

PubMed Central

Honda, Arata; Hatori, Masanori; Hirose, Michiko; Honda, Chizumi; Izu, Haruna; Inoue, Kimiko; Hirasawa, Ryutaro; Matoba, Shogo; Togayachi, Sumie; Miyoshi, Hiroyuki; Ogura, Atsuo

2013-01-01

Although induced pluripotent stem (iPS) cells are indistinguishable from ES cells in their expression of pluripotent markers, their differentiation into targeted cells is often limited. Here, we examined whether the limited capacity of iPS cells to differentiate into neural lineage cells could be mitigated by improving their base-line level of pluripotency, i.e. by converting them into the so-called “naive” state. In this study, we used rabbit iPS and ES cells because of the easy availability of both cell types and their typical primed state characters. Repeated passages of the iPS cells permitted their differentiation into early neural cell types (neural stem cells, neurons, and glial astrocytes) with efficiencies similar to ES cells. However, unlike ES cells, their ability to differentiate later into neural cells (oligodendrocytes) was severely compromised. In contrast, after these iPS cells had been converted to a naive-like state, they readily differentiated into mature oligodendrocytes developing characteristic ramified branches, which could not be attained even with ES cells. These results suggest that the naive-like conversion of iPS cells might endow them with a higher differentiation capacity. PMID:23880763

Personality matters: individual variation in reactions of naive bird predators to aposematic prey.

PubMed

Exnerová, Alice; Svádová, Katerina Hotová; Fucíková, Eva; Drent, Pieter; Stys, Pavel

2010-03-07

Variation in reactions to aposematic prey is common among conspecific individuals of bird predators. It may result from different individual experience but it also exists among naive birds. This variation may possibly be explained by the effect of personality--a complex of correlated, heritable behavioural traits consistent across contexts. In the great tit (Parus major), two extreme personality types have been defined. 'Fast' explorers are bold, aggressive and routine-forming; 'slow' explorers are shy, non-aggressive and innovative. Influence of personality type on unlearned reaction to aposematic prey, rate of avoidance learning and memory were tested in naive, hand-reared great tits from two opposite lines selected for exploration (slow against fast). The birds were subjected to a sequence of trials in which they were offered aposematic adult firebugs (Pyrrhocoris apterus). Slow birds showed a greater degree of unlearned wariness and learned to avoid the firebugs faster than fast birds. Although birds of both personality types remembered their experience, slow birds were more cautious in the memory test. We conclude that not only different species but also populations of predators that differ in proportions of personality types may have different impacts on survival of aposematic insects under natural conditions.

Approximate Bayesian evaluations of measurement uncertainty

NASA Astrophysics Data System (ADS)

Possolo, Antonio; Bodnar, Olha

2018-04-01

The Guide to the Expression of Uncertainty in Measurement (GUM) includes formulas that produce an estimate of a scalar output quantity that is a function of several input quantities, and an approximate evaluation of the associated standard uncertainty. This contribution presents approximate, Bayesian counterparts of those formulas for the case where the output quantity is a parameter of the joint probability distribution of the input quantities, also taking into account any information about the value of the output quantity available prior to measurement expressed in the form of a probability distribution on the set of possible values for the measurand. The approximate Bayesian estimates and uncertainty evaluations that we present have a long history and illustrious pedigree, and provide sufficiently accurate approximations in many applications, yet are very easy to implement in practice. Differently from exact Bayesian estimates, which involve either (analytical or numerical) integrations, or Markov Chain Monte Carlo sampling, the approximations that we describe involve only numerical optimization and simple algebra. Therefore, they make Bayesian methods widely accessible to metrologists. We illustrate the application of the proposed techniques in several instances of measurement: isotopic ratio of silver in a commercial silver nitrate; odds of cryptosporidiosis in AIDS patients; height of a manometer column; mass fraction of chromium in a reference material; and potential-difference in a Zener voltage standard.

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.

Variation in the vitreoretinal configuration of Stage 4 retinopathy of prematurity in photocoagulated and treatment naive eyes undergoing vitrectomy

PubMed Central

Gadkari, Salil Sharad; Deshpande, Madan

2017-01-01

Purpose: We sought to document the difference in the vitreoretinal configuration of Stage 4 retinopathy of prematurity (ROP) in photocoagulated and treatment naive eyes undergoing vitrectomy and to correlate it with surgical complexity. Methods: Consecutive eyes posted for vitrectomy with Stage 4 ROP were documented preoperatively using a RetCam for the presence of peripheral traction (PT), presence of central traction just outside the arcades, and presence of traction extending to the lens. A note was made of the following intraoperative events: lensectomy, intraoperative bleeding, and iatrogenic breaks. Wilcoxon rank-sum test was used for analysis. Results: From a total of 46 eyes, 16 and 30 eyes were from the treated and treatment naive group, respectively. More eyes in the treated group had central (P < 0.0001) and lenticular traction (P = 0.022). More eyes in the untreated group had PT (P < 0.0001). A significant number of eyes without photocoagulation needed lensectomy (P = 0.042), and no difference in intraoperative bleeding (P = 0.94) was demonstrable. Iatrogenic retinotomy occurred in three eyes, all naive. Notably, age at surgery was more in the untreated group (P = 0.00008). Conclusion: Vasoproliferative activity in all retinopathies occurs at the junction of the ischemic and nonischemic retina. In the natural course of ROP, this takes place peripherally, at the ridge. In photocoagulated eyes, this junction is displaced posteriorly due to peripheral ablation. Treated eyes manifested with posterior proliferative changes and were more amenable to lens-sparing vitrectomy. Naive eyes were older when they underwent surgery to relieve PT with greater chances of lensectomy and iatrogenic breaks. PMID:28905829

Plasma homovanillic acid levels and therapeutic outcome in schizophrenics: comparisons of neuroleptic-naive first-episode patients and patients with disease exacerbation due to neuroleptic discontinuance.

PubMed

Akiyama, K; Tsuchida, K; Kanzaki, A; Ujike, H; Hamamura, T; Kondo, K; Mutoh, S; Miyanagi, K; Kuroda, S; Otsuki, S

1995-11-15

Plasma homovanillic acid (pHVA) levels were measured and the Brief Psychiatric Rating Scale (BPRS) scores were evaluated in 26 schizophrenic patients who had either never been medicated (neuroleptic-naive, first-episode subjects) or whose condition had become exacerbated following neuroleptic discontinuance (exacerbated subjects). All the subjects received medication with a fixed dose of a neuroleptic (haloperidol or fluphenazine, both 9 mg/day) for the first week and variable doses for the subsequent 4 weeks. In the neuroleptic-naive subjects, pHVA levels increased significantly 1 week after starting the protocol; this increase correlated significantly with clinical improvement of the BPRS positive symptom scores at week 5. In the neuroleptic-naive subjects, pHVA levels had declined to the baseline level by week 5. In the exacerbated subjects, there were no significant correlations between pHVA level changes at week 1 and later improvements of the BPRS positive symptom scores. These results suggest that the rise in pHVA levels occurring within 1 week after starting a fixed neuroleptic dose may predict a favorable clinical response in neuroleptic-naive schizophrenic patients.

Comparative analysis of drug resistance mutations in the human immunodeficiency virus reverse transcriptase gene in patients who are non-responsive, responsive and naive to antiretroviral therapy.

PubMed

Misbah, Mohammad; Roy, Gaurav; Shahid, Mudassar; Nag, Nalin; Kumar, Suresh; Husain, Mohammad

2016-05-01

Drug resistance mutations in the Pol gene of human immunodeficiency virus 1 (HIV-1) are one of the critical factors associated with antiretroviral therapy (ART) failure in HIV-1 patients. The issue of resistance to reverse transcriptase inhibitors (RTIs) in HIV infection has not been adequately addressed in the Indian subcontinent. We compared HIV-1 reverse transcriptase (RT) gene sequences to identify mutations present in HIV-1 patients who were ART non-responders, ART responders and drug naive. Genotypic drug resistance testing was performed by sequencing a 655-bp region of the RT gene from 102 HIV-1 patients, consisting of 30 ART-non-responding, 35 ART-responding and 37 drug-naive patients. The Stanford HIV Resistance Database (HIVDBv 6.2), IAS-USA mutation list, ANRS_09/2012 algorithm, and Rega v8.02 algorithm were used to interpret the pattern of drug resistance. The majority of the sequences (96 %) belonged to subtype C, and a few of them (3.9 %) to subtype A1. The frequency of drug resistance mutations observed in ART-non-responding, ART-responding and drug-naive patients was 40.1 %, 10.7 % and 20.58 %, respectively. It was observed that in non-responders, multiple mutations were present in the same patient, while in responders, a single mutation was found. Some of the drug-naive patients had more than one mutation. Thymidine analogue mutations (TAMs), however, were found in non-responders and naive patients but not in responders. Although drug resistance mutations were widely distributed among ART non-responders, the presence of resistance mutations in the viruses of drug-naive patients poses a big concern in the absence of a genotyping resistance test.

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.

Acute cognitive impact of antiseizure drugs in naive rodents and corneal-kindled mice.

PubMed

Barker-Haliski, Melissa L; Vanegas, Fabiola; Mau, Matthew J; Underwood, Tristan K; White, H Steve

2016-09-01

Some antiseizure drugs (ASDs) are associated with cognitive liability in patients with epilepsy, thus ASDs without this risk would be preferred. Little comparative pharmacology exists with ASDs in preclinical models of cognition. Few pharmacologic studies exist on the acute effects in rodents with chronic seizures. Predicting risk for cognitive impact with preclinical models may supply valuable ASD differentiation data. ASDs (phenytoin [PHT]; carbamazepine [CBZ]; valproic acid [VPA]; lamotrigine [LTG]; phenobarbital [PB]; tiagabine [TGB]; retigabine [RTG]; topiramate [TPM]; and levetiracetam [LEV]) were administered equivalent to maximal electroshock median effective dose ([ED50]; mice, rats), or median dose necessary to elicit minimal motor impairment (median toxic dose [TD50]; rats). Cognition models with naive adult rodents were novel object/place recognition (NOPR) task with CF-1 mice, and Morris water maze (MWM) with Sprague-Dawley rats. Selected ASDs were also administered to rats prior to testing in an open field. The effect of chronic seizures and ASD administration on cognitive performance in NOPR was also determined with corneal-kindled mice. Mice that did not achieve kindling criterion (partially kindled) were included to examine the effect of electrical stimulation on cognitive performance. Sham-kindled and age-matched mice were also tested. No ASD (ED50) affected latency to locate the MWM platform; TD50 of PB, RTG, TPM, and VPA reduced this latency. In naive mice, CBZ and VPA (ED50) reduced time with the novel object. Of interest, no ASD (ED50) affected performance of fully kindled mice in NOPR, whereas CBZ and LEV improved cognitive performance of partially kindled mice. Standardized approaches to the preclinical evaluation of an ASD's potential cognitive impact are needed to inform drug development. This study demonstrated acute, dose- and model-dependent effects of therapeutically relevant doses of ASDs on cognitive performance of naive mice and

Learning Bayesian Networks from Correlated Data

NASA Astrophysics Data System (ADS)

Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

2016-05-01

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

Bayesian markets to elicit private information

PubMed Central

2017-01-01

Financial markets reveal what investors think about the future, and prediction markets are used to forecast election results. Could markets also encourage people to reveal private information, such as subjective judgments (e.g., “Are you satisfied with your life?”) or unverifiable facts? This paper shows how to design such markets, called Bayesian markets. People trade an asset whose value represents the proportion of affirmative answers to a question. Their trading position then reveals their own answer to the question. The results of this paper are based on a Bayesian setup in which people use their private information (their “type”) as a signal. Hence, beliefs about others’ types are correlated with one’s own type. Bayesian markets transform this correlation into a mechanism that rewards truth telling. These markets avoid two complications of alternative methods: they need no knowledge of prior information and no elicitation of metabeliefs regarding others’ signals. PMID:28696293

Quantum Inference on Bayesian Networks

NASA Astrophysics Data System (ADS)

Yoder, Theodore; Low, Guang Hao; Chuang, Isaac

2014-03-01

Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.

Is Children's Naive Knowledge Consistent?: A Comparison of the Concepts of Sound and Heat

ERIC Educational Resources Information Center

Lautrey, Jacques; Mazens, Karine

2004-01-01

The aim of this study was to shed some light on the organization of naive knowledge, and on the process of conceptual change in everyday physics, more specifically regarding the concepts of sound and heat. Eighty-three 8-year-old children were interviewed individually in order to see if they attributed the properties of objects (such as…

The reproducibility of adenosine monophosphate bronchial challenges in mild, steroid-naive asthmatics

PubMed Central

Singh, Dave; Fairwood, Jennifer; Murdoch, Robert; Weeks, Amanda; Russell, Paul; Roy, Kay; Langley, Steve; Woodcock, Ashley

2008-01-01

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT Repeated adenosine monophosphate (AMP) challenges are used to assess drug effects in asthma clinical trials, but may be prone to tachyphylaxis when repeated at short intervals. Possible tachyphylaxis at 12- and 24-h intervals has not been studied. WHAT THIS STUDY ADDS Clinically relevant tachyphylaxis after repeated AMP challenges does not occur when repeated at 12- and 24-h intervals. AMP challenges at these intervals can be used to assess drug effects in clinical trials. AIMS Repeated adenosine monophosphate (AMP) challenges are used to assess drug efficacy in clinical trials of mild, steroid-naive asthmatics. Refractoriness has been reported after repeated challenges over short intervals. This study evaluated possible tachyphylaxis after repeated AMP challenges at 12 and 24 h in mild, steroid-naive asthmatics. METHODS This was an open, three-way crossover study. Twenty-six steroid-naive asthmatic subjects were randomized to the following AMP challenge regimens separated by 7–14 days: (A) challenge at 08.00 h, repeated 24 h later; (B) challenge at 08.00 h, repeated 12 and 24 h later; (C) challenge at 20.00 h, repeated 12 h later. Comparisons within day were assessed using 90% confidence intervals (CIs). Non-inferiority approach taken with 1 doubling concentration (DC) as a clinically relevant difference. RESULTS Regimen A: Significant increase in AMP reactivity at 24 h. Mean DC difference was 0.6 (90% CI 0.24, 0.96). Regimen B: No evidence of difference between AMP reactivity at 08.00 h and a repeated challenge 12 h later. Repeated challenge at 24 h caused a significant increase in provocation concentration (PC)20 compared with 12 h (mean DC difference 0.48, 90% CI 0.02, 0.95) and 0 h (mean DC difference 0.82, 90% CI 0.49, 1.14 – the upper CI exceeds the criteria of 1 DC). Challenge regimen C: No difference between challenges; mean DC difference of 0.28 (90% CI −0.2, 0.76). CONCLUSION The small decline in AMP

Experience of dolutegravir in HIV-infected treatment-naive patients from a tertiary care University Hospital in Ireland

PubMed Central

Waqas, Sarmad; O’Connor, Mairead; Levey, Ciara; Mallon, Paddy; Sheehan, Gerard; Patel, Anjali; Avramovic, Gordana; Lambert, John S

2016-01-01

Objective: Dolutegravir, an HIV integrase inhibitor, is a relatively new treatment option. To assess the tolerability, side effects, and time to viral decline to non-detectable in patients newly started on dolutegravir. Methods: Retrospective health care record of 61 consecutive HIV treatment-naive patients started on dolutegravir was reviewed and analysed on SPSS. Results: The mean initial viral load was 160826.05 copies/mL (range, 79–1,126,617 copies/mL). HIV viral load became non-detectable in 63.9% of patients on dolutegravir within 3 months. In all, 60.7% of patients reported no side effects on dolutegravir; 98.4% of the patients claimed full compliance to their antiretrovirals. Conclusion: Dolutegravir was found to be efficacious and well tolerated in HIV-infected treatment-naive patients. PMID:27826447

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.

A Bayesian approach to meta-analysis of plant pathology studies.

PubMed

Mila, A L; Ngugi, H K

2011-01-01

Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection products that induce systemic acquired resistance for the management of fire blight of apple. In a simple random-effects model assuming a normal distribution of effect sizes and no prior information (i.e., a noninformative prior), the results of the Bayesian meta-analysis are similar to those obtained with classical methods. Implementing the same model with a Student's t distribution and a noninformative prior for the effect sizes, instead of a normal distribution, yields similar results for all but acibenzolar-S-methyl (Actigard) which was evaluated only in seven studies in this example. Whereas both the classical (P = 0.28) and the Bayesian analysis with a noninformative prior (95% credibility interval [CRI] for the log response ratio: -0.63 to 0.08) indicate a nonsignificant effect for Actigard, specifying a t distribution resulted in a significant, albeit variable, effect for this product (CRI: -0.73 to -0.10). These results confirm the sensitivity of the analytical outcome (i.e., the posterior distribution) to the choice of prior in Bayesian meta-analyses involving a limited number of studies. We review some pertinent literature on more advanced topics, including modeling of among-study heterogeneity, publication bias, analyses involving a limited number of studies, and methods for dealing with missing data, and show how these issues can be approached in a Bayesian framework

What Is the Probability You Are a Bayesian?

ERIC Educational Resources Information Center

Wulff, Shaun S.; Robinson, Timothy J.

2014-01-01

Bayesian methodology continues to be widely used in statistical applications. As a result, it is increasingly important to introduce students to Bayesian thinking at early stages in their mathematics and statistics education. While many students in upper level probability courses can recite the differences in the Frequentist and Bayesian…

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.

The image recognition based on neural network and Bayesian decision

NASA Astrophysics Data System (ADS)

Wang, Chugege

2018-04-01

The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.

Editorial: Bayesian benefits for child psychology and psychiatry researchers.

PubMed

Oldehinkel, Albertine J

2016-09-01

For many scientists, performing statistical tests has become an almost automated routine. However, p-values are frequently used and interpreted incorrectly; and even when used appropriately, p-values tend to provide answers that do not match researchers' questions and hypotheses well. Bayesian statistics present an elegant and often more suitable alternative. The Bayesian approach has rarely been applied in child psychology and psychiatry research so far, but the development of user-friendly software packages and tutorials has placed it well within reach now. Because Bayesian analyses require a more refined definition of hypothesized probabilities of possible outcomes than the classical approach, going Bayesian may offer the additional benefit of sparkling the development and refinement of theoretical models in our field. © 2016 Association for Child and Adolescent Mental Health.

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.

Strain differences in the neural, behavioral, and molecular correlates of sweet and salty taste in naive, ethanol- and sucrose-exposed P and NP rats

PubMed Central

Coleman, Jamison; Williams, Ashley; Phan, Tam-Hao T.; Mummalaneni, Shobha; Melone, Pamela; Ren, ZuoJun; Zhou, Huiping; Mahavadi, Sunila; Murthy, Karnam S.; Katsumata, Tadayoshi; DeSimone, John A.

2011-01-01

Strain differences between naive, sucrose- and ethanol-exposed alcohol-preferring (P) and alcohol-nonpreferring (NP) rats were investigated in their consumption of ethanol, sucrose, and NaCl; chorda tympani (CT) nerve responses to sweet and salty stimuli; and gene expression in the anterior tongue of T1R3 and TRPV1/TRPV1t. Preference for 5% ethanol and 10% sucrose, CT responses to sweet stimuli, and T1R3 expression were greater in naive P rats than NP rats. The enhancement of the CT response to 0.5 M sucrose in the presence of varying ethanol concentrations (0.5–40%) in naive P rats was higher and shifted to lower ethanol concentrations than NP rats. Chronic ingestion of 5% sucrose or 5% ethanol decreased T1R3 mRNA in NP and P rats. Naive P rats also demonstrated bigger CT responses to NaCl+benzamil and greater TRPV1/TRPV1t expression. TRPV1t agonists produced biphasic effects on NaCl+benzamil CT responses, enhancing the response at low concentrations and inhibiting it at high concentrations. The concentration of a TRPV1/TRPV1t agonist (Maillard reacted peptides conjugated with galacturonic acid) that produced a maximum enhancement in the NaCl+benzamil CT response induced a decrease in NaCl intake and preference in P rats. In naive P rats and NP rats exposed to 5% ethanol in a no-choice paradigm, the biphasic TRPV1t agonist vs. NaCl+benzamil CT response profiles were higher and shifted to lower agonist concentrations than in naive NP rats. TRPV1/TRPV1t mRNA expression increased in NP rats but not in P rats exposed to 5% ethanol in a no-choice paradigm. We conclude that P and NP rats differ in T1R3 and TRPV1/TRPV1t expression and neural and behavioral responses to sweet and salty stimuli and to chronic sucrose and ethanol exposure. PMID:21849614

Strain differences in the neural, behavioral, and molecular correlates of sweet and salty taste in naive, ethanol- and sucrose-exposed P and NP rats.

PubMed

Coleman, Jamison; Williams, Ashley; Phan, Tam-Hao T; Mummalaneni, Shobha; Melone, Pamela; Ren, Zuojun; Zhou, Huiping; Mahavadi, Sunila; Murthy, Karnam S; Katsumata, Tadayoshi; DeSimone, John A; Lyall, Vijay

2011-11-01

Strain differences between naive, sucrose- and ethanol-exposed alcohol-preferring (P) and alcohol-nonpreferring (NP) rats were investigated in their consumption of ethanol, sucrose, and NaCl; chorda tympani (CT) nerve responses to sweet and salty stimuli; and gene expression in the anterior tongue of T1R3 and TRPV1/TRPV1t. Preference for 5% ethanol and 10% sucrose, CT responses to sweet stimuli, and T1R3 expression were greater in naive P rats than NP rats. The enhancement of the CT response to 0.5 M sucrose in the presence of varying ethanol concentrations (0.5-40%) in naive P rats was higher and shifted to lower ethanol concentrations than NP rats. Chronic ingestion of 5% sucrose or 5% ethanol decreased T1R3 mRNA in NP and P rats. Naive P rats also demonstrated bigger CT responses to NaCl+benzamil and greater TRPV1/TRPV1t expression. TRPV1t agonists produced biphasic effects on NaCl+benzamil CT responses, enhancing the response at low concentrations and inhibiting it at high concentrations. The concentration of a TRPV1/TRPV1t agonist (Maillard reacted peptides conjugated with galacturonic acid) that produced a maximum enhancement in the NaCl+benzamil CT response induced a decrease in NaCl intake and preference in P rats. In naive P rats and NP rats exposed to 5% ethanol in a no-choice paradigm, the biphasic TRPV1t agonist vs. NaCl+benzamil CT response profiles were higher and shifted to lower agonist concentrations than in naive NP rats. TRPV1/TRPV1t mRNA expression increased in NP rats but not in P rats exposed to 5% ethanol in a no-choice paradigm. We conclude that P and NP rats differ in T1R3 and TRPV1/TRPV1t expression and neural and behavioral responses to sweet and salty stimuli and to chronic sucrose and ethanol exposure.

A comment on priors for Bayesian occupancy models

PubMed Central

Gerber, Brian D.

2018-01-01

Understanding patterns of species occurrence and the processes underlying these patterns is fundamental to the study of ecology. One of the more commonly used approaches to investigate species occurrence patterns is occupancy modeling, which can account for imperfect detection of a species during surveys. In recent years, there has been a proliferation of Bayesian modeling in ecology, which includes fitting Bayesian occupancy models. The Bayesian framework is appealing to ecologists for many reasons, including the ability to incorporate prior information through the specification of prior distributions on parameters. While ecologists almost exclusively intend to choose priors so that they are “uninformative” or “vague”, such priors can easily be unintentionally highly informative. Here we report on how the specification of a “vague” normally distributed (i.e., Gaussian) prior on coefficients in Bayesian occupancy models can unintentionally influence parameter estimation. Using both simulated data and empirical examples, we illustrate how this issue likely compromises inference about species-habitat relationships. While the extent to which these informative priors influence inference depends on the data set, researchers fitting Bayesian occupancy models should conduct sensitivity analyses to ensure intended inference, or employ less commonly used priors that are less informative (e.g., logistic or t prior distributions). We provide suggestions for addressing this issue in occupancy studies, and an online tool for exploring this issue under different contexts. PMID:29481554

A comment on priors for Bayesian occupancy models.

PubMed

Northrup, Joseph M; Gerber, Brian D

2018-01-01

Understanding patterns of species occurrence and the processes underlying these patterns is fundamental to the study of ecology. One of the more commonly used approaches to investigate species occurrence patterns is occupancy modeling, which can account for imperfect detection of a species during surveys. In recent years, there has been a proliferation of Bayesian modeling in ecology, which includes fitting Bayesian occupancy models. The Bayesian framework is appealing to ecologists for many reasons, including the ability to incorporate prior information through the specification of prior distributions on parameters. While ecologists almost exclusively intend to choose priors so that they are "uninformative" or "vague", such priors can easily be unintentionally highly informative. Here we report on how the specification of a "vague" normally distributed (i.e., Gaussian) prior on coefficients in Bayesian occupancy models can unintentionally influence parameter estimation. Using both simulated data and empirical examples, we illustrate how this issue likely compromises inference about species-habitat relationships. While the extent to which these informative priors influence inference depends on the data set, researchers fitting Bayesian occupancy models should conduct sensitivity analyses to ensure intended inference, or employ less commonly used priors that are less informative (e.g., logistic or t prior distributions). We provide suggestions for addressing this issue in occupancy studies, and an online tool for exploring this issue under different contexts.

Bayesian networks in overlay recipe optimization

NASA Astrophysics Data System (ADS)

Binns, Lewis A.; Reynolds, Greg; Rigden, Timothy C.; Watkins, Stephen; Soroka, Andrew

2005-05-01

Currently, overlay measurements are characterized by "recipe", which defines both physical parameters such as focus, illumination et cetera, and also the software parameters such as algorithm to be used and regions of interest. Setting up these recipes requires both engineering time and wafer availability on an overlay tool, so reducing these requirements will result in higher tool productivity. One of the significant challenges to automating this process is that the parameters are highly and complexly correlated. At the same time, a high level of traceability and transparency is required in the recipe creation process, so a technique that maintains its decisions in terms of well defined physical parameters is desirable. Running time should be short, given the system (automatic recipe creation) is being implemented to reduce overheads. Finally, a failure of the system to determine acceptable parameters should be obvious, so a certainty metric is also desirable. The complex, nonlinear interactions make solution by an expert system difficult at best, especially in the verification of the resulting decision network. The transparency requirements tend to preclude classical neural networks and similar techniques. Genetic algorithms and other "global minimization" techniques require too much computational power (given system footprint and cost requirements). A Bayesian network, however, provides a solution to these requirements. Such a network, with appropriate priors, can be used during recipe creation / optimization not just to select a good set of parameters, but also to guide the direction of search, by evaluating the network state while only incomplete information is available. As a Bayesian network maintains an estimate of the probability distribution of nodal values, a maximum-entropy approach can be utilized to obtain a working recipe in a minimum or near-minimum number of steps. In this paper we discuss the potential use of a Bayesian network in such a capacity

Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a

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…

Teaching Bayesian Statistics to Undergraduate Students through Debates

ERIC Educational Resources Information Center

Stewart, Sepideh; Stewart, Wayne

2014-01-01

This paper describes a lecturer's approach to teaching Bayesian statistics to students who were only exposed to the classical paradigm. The study shows how the lecturer extended himself by making use of ventriloquist dolls to grab hold of students' attention and embed important ideas in revealing the differences between the Bayesian and classical…

Bayesian estimation inherent in a Mexican-hat-type neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2016-05-01

Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.

Global Symmetries of Naive and Staggered Fermions in Arbitrary Dimensions

NASA Astrophysics Data System (ADS)

Kieburg, Mario; Würfel, Tim R.

2018-03-01

It is well-known that staggered fermions do not necessarily satisfy the same global symmetries as the continuum theory. We analyze the mechanism behind this phenomenon for arbitrary dimension and gauge group representation. For this purpose we vary the number of lattice sites between even and odd parity in each single direction. Since the global symmetries are manifest in the lowest eigenvalues of the Dirac operator, the spectral statistics and also the symmetry breaking pattern will be affected. We analyze these effects and compare our predictions with Monte-Carlo simulations of naive Dirac operators in the strong coupling limit. This proceeding is a summary of our work [1].

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.

Support vector machines

NASA Technical Reports Server (NTRS)

Garay, Michael J.; Mazzoni, Dominic; Davies, Roger; Wagstaff, Kiri

2004-01-01

Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

Classical Swine Fever Outbreak after Modified Live LOM Strain Vaccination in Naive Pigs, South Korea

PubMed Central

Je, Sang H.; Kwon, Taeyong; Yoo, Sung J.; Lee, Dong-Uk; Lee, SeungYoon; Richt, Juergen A.

2018-01-01

We report classical swine fever outbreaks occurring in naive pig herds on Jeju Island, South Korea, after the introduction of the LOM vaccine strain. Two isolates from sick pigs had >99% identity with the vaccine stain. LOM strain does not appear safe; its use in the vaccine should be reconsidered. PMID:29553332

Comprehension and computation in Bayesian problem solving

PubMed Central

Johnson, Eric D.; Tubau, Elisabet

2015-01-01

Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian inferences relative to normalized formats (e.g., probabilities, percentages), both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on “transparent” Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e., transparent problem structures) at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct vs. incorrect reasoners depart, and how individual differences might influence this time point. PMID:26283976

Quantum-Bayesian coherence

NASA Astrophysics Data System (ADS)

Fuchs, Christopher A.; Schack, Rüdiger

2013-10-01

In the quantum-Bayesian interpretation of quantum theory (or QBism), the Born rule cannot be interpreted as a rule for setting measurement-outcome probabilities from an objective quantum state. But if not, what is the role of the rule? In this paper, the argument is given that it should be seen as an empirical addition to Bayesian reasoning itself. Particularly, it is shown how to view the Born rule as a normative rule in addition to usual Dutch-book coherence. It is a rule that takes into account how one should assign probabilities to the consequences of various intended measurements on a physical system, but explicitly in terms of prior probabilities for and conditional probabilities consequent upon the imagined outcomes of a special counterfactual reference measurement. This interpretation is exemplified by representing quantum states in terms of probabilities for the outcomes of a fixed, fiducial symmetric informationally complete measurement. The extent to which the general form of the new normative rule implies the full state-space structure of quantum mechanics is explored.

A Bayesian Assessment of Seismic Semi-Periodicity Forecasts

NASA Astrophysics Data System (ADS)

Nava, F.; Quinteros, C.; Glowacka, E.; Frez, J.

2016-01-01

Among the schemes for earthquake forecasting, the search for semi-periodicity during large earthquakes in a given seismogenic region plays an important role. When considering earthquake forecasts based on semi-periodic sequence identification, the Bayesian formalism is a useful tool for: (1) assessing how well a given earthquake satisfies a previously made forecast; (2) re-evaluating the semi-periodic sequence probability; and (3) testing other prior estimations of the sequence probability. A comparison of Bayesian estimates with updated estimates of semi-periodic sequences that incorporate new data not used in the original estimates shows extremely good agreement, indicating that: (1) the probability that a semi-periodic sequence is not due to chance is an appropriate estimate for the prior sequence probability estimate; and (2) the Bayesian formalism does a very good job of estimating corrected semi-periodicity probabilities, using slightly less data than that used for updated estimates. The Bayesian approach is exemplified explicitly by its application to the Parkfield semi-periodic forecast, and results are given for its application to other forecasts in Japan and Venezuela.

Zeb1-Hdac2-eNOS circuitry identifies early cardiovascular precursors in naive mouse embryonic stem cells.

PubMed

Cencioni, Chiara; Spallotta, Francesco; Savoia, Matteo; Kuenne, Carsten; Guenther, Stefan; Re, Agnese; Wingert, Susanne; Rehage, Maike; Sürün, Duran; Siragusa, Mauro; Smith, Jacob G; Schnütgen, Frank; von Melchner, Harald; Rieger, Michael A; Martelli, Fabio; Riccio, Antonella; Fleming, Ingrid; Braun, Thomas; Zeiher, Andreas M; Farsetti, Antonella; Gaetano, Carlo

2018-03-29

Nitric oxide (NO) synthesis is a late event during differentiation of mouse embryonic stem cells (mESC) and occurs after release from serum and leukemia inhibitory factor (LIF). Here we show that after release from pluripotency, a subpopulation of mESC, kept in the naive state by 2i/LIF, expresses endothelial nitric oxide synthase (eNOS) and endogenously synthesizes NO. This eNOS/NO-positive subpopulation (ESNO+) expresses mesendodermal markers and is more efficient in the generation of cardiovascular precursors than eNOS/NO-negative cells. Mechanistically, production of endogenous NO triggers rapid Hdac2 S-nitrosylation, which reduces association of Hdac2 with the transcriptional repression factor Zeb1, allowing mesendodermal gene expression. In conclusion, our results suggest that the interaction between Zeb1, Hdac2, and eNOS is required for early mesendodermal differentiation of naive mESC.

Calibrating Bayesian Network Representations of Social-Behavioral Models

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

Whitney, Paul D.; Walsh, Stephen J.

2010-04-08

While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empiricalmore » comparison with data taken from the Minorities at Risk Organizational Behaviors database.« less

Naive helper T cells from BCG-vaccinated volunteers produce IFN-gamma and IL-5 to mycobacterial antigen-pulsed dendritic cells.

PubMed

Kowalewicz-Kulbat, Magdalena; Kaźmierczak, Dominik; Donevski, Stefan; Biet, Franck; Pestel, Joël; Rudnicka, Wiesława

2008-01-01

Mycobacterium bovis bacillus Calmette-Guérin (BCG) is a live vaccine that has been used in routine vaccination against tuberculosis for nearly 80 years. However, its efficacy is controversial. The failure of BCG vaccination may be at least partially explained by the induction of poor or inappropriate host responses. Dendritic cells (DCs) are likely to play a key role in the induction of immune response to mycobacteria by polarizing the reactivity of T lymphocytes toward a Th1 profile, contributing to the generation of protective cellular immunity against mycobacteria. In this study we aimed to investigate the production of Th1 and Th2 cytokines by naive CD4+ T cells to mycobacterial antigen-pulsed DCs in the group of young, healthy BCG vaccinated volunteers. The response of naive helper T cells was compared with the response of total blood lymphocytes. Our present results clearly showed that circulating naive CD45RA+CD4+ lymphocytes from BCG-vaccinated subjects can become effector helper cells producing IFN-gamma and IL-5 under the stimulation by autologous dendritic cells presenting mycobacterial protein antigen-PPD or infected with live M. bovis BCG bacilli.

Characteristic appearances of fundus autofluorescence in treatment-naive and active polypoidal choroidal vasculopathy: a retrospective study of 170 patients.

PubMed

Zhao, Xinyu; Xia, Song; Chen, Youxin

2018-06-01

To investigate the characteristic appearances of fundus autofluorescence (FAF) in patients with treatment-naive and active polypoidal choroidal vasculopathy (PCV). Cases with the diagnosis of treatment-naive and active PCV from November 2012 to May 2017 at Peking Union Medical College Hospital were retrospectively reviewed. All patients underwent comprehensive ophthalmologic examination. Autofluorescence (AF) findings were described at the retinal sites of the corresponding lesions identified and diagnosed using indocyanine green angiography and spectral-domain optical coherence tomography. One hundred seventy patients with 192 affected eyes were included. The logMAR BCVA of the patients were 0.53 ± 0.28. The six AF patterns of 243 polypoidal lesions were confluent hypo-AF with hyper-AF ring (49.8%), confluent hypo-AF (22.6%), hyper-AF with hypo-AF ring (3.7%), granular hypo-AF (7.0%), blocked hypo-AF due to hemorrhage (8.6%), and polyps without apparent AF changes (8.2%). For 146 branching vascular networks (BVNs), 97.3% were granular hypo-AF, and others were blocked hypo-AF due to hemorrhage. In eyes with treatment-naive and active PCV, the polypoidal lesions and BVNs induce characteristic FAF changes. FAF images provide reliable adjunct reference for the diagnosis of PCV.

Bayesian Threshold Estimation

ERIC Educational Resources Information Center

Gustafson, S. C.; Costello, C. S.; Like, E. C.; Pierce, S. J.; Shenoy, K. N.

2009-01-01

Bayesian estimation of a threshold time (hereafter simply threshold) for the receipt of impulse signals is accomplished given the following: 1) data, consisting of the number of impulses received in a time interval from zero to one and the time of the largest time impulse; 2) a model, consisting of a uniform probability density of impulse time…

Bayesian analyses of seasonal runoff forecasts

NASA Astrophysics Data System (ADS)

Krzysztofowicz, R.; Reese, S.

1991-12-01

Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.

Bayesian survival analysis in clinical trials: What methods are used in practice?

PubMed

Brard, Caroline; Le Teuff, Gwénaël; Le Deley, Marie-Cécile; Hampson, Lisa V

2017-02-01

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment

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

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

A bayesian approach to classification criteria for spectacled eiders

USGS Publications Warehouse

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

Bayesian Modeling of a Human MMORPG Player

NASA Astrophysics Data System (ADS)

Synnaeve, Gabriel; Bessière, Pierre

2011-03-01

This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes are present. We explain the model in Bayesian programming and show how we could learn the conditional probabilities from data gathered during human-played sessions.

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 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

Phenotypic and Genotypic Shifts in Hepatitis B Virus in Treatment-Naive Patients, Taiwan, 2008-2012.

PubMed

Yeh, Chau-Ting; Liang, Kung-Hao; Chang, Ming-Ling; Hsu, Chao-Wei; Chen, Yi-Cheng; Lin, Chih-Lang; Lin, Wey-Ran; Lai, Ming-Wei

2017-05-01

We examined the characteristic changes of hepatitis B virus (HBV) in antiviral drug treatment-naive patients referred for pretreatment evaluation in Taiwan during 2008-2012. Over time, we observed substantial decreases in the prevalence of HBV e antigen (HBeAg) and increasing prevalence of the precore G1899A mutation and HBV-DNA levels in HBeAg-positive patients.

Bayesian Networks Improve Causal Environmental Assessments for Evidence-Based Policy.

PubMed

Carriger, John F; Barron, Mace G; Newman, Michael C

2016-12-20

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on valued ecological resources. These aspects are demonstrated through hypothetical problem scenarios that explore some major benefits of using Bayesian networks for reasoning and making inferences in evidence-based policy.

Insulin sensitivity and beta-cell function in protease inhibitor-treated and -naive human immunodeficiency virus-infected children.

PubMed

Bitnun, Ari; Sochett, Etienne; Dick, Paul T; To, Teresa; Jefferies, Craig; Babyn, Paul; Forbes, Jack; Read, Stanley; King, Susan M

2005-01-01

Previous pediatric studies have failed to demonstrate a clear association between protease inhibitor (PI) therapy and abnormal glucose homeostasis in HIV-infected children. To define more precisely the impact of PI therapy on glucose homeostasis in this population, we performed the insulin-modified frequent-sampling iv glucose tolerance test on 33 PI-treated and 15 PI-naive HIV-infected children. Other investigations included fasting serum lipids; glucose, insulin, and C-peptide; single-slice abdominal computed tomography; and, in a subset of PI-treated children, an oral glucose tolerance test. There were no differences between the two groups with respect to fasting serum insulin or C-peptide, homeostatic model assessment insulin resistance, or quantitative insulin sensitivity check index. The mean insulin sensitivity index of PI-treated and PI-naive children was 6.93 +/- 6.37 and 10.58 +/- 12.93 x 10(-4)min(-1) [microU/ml](-1), respectively (P = 0.17). The mean disposition index for the two groups was 1840 +/- 1575 and 3708 +/- 3005 x 10(-4)min(-1) (P = 0.013), respectively. After adjusting for potential confounding variables using multiple regression analysis, the insulin sensitivity index and disposition index of PI-treated children were significantly lower than that of PI-naive children (P = 0.01 for both). In PI-treated but not PI-naive children, insulin sensitivity correlated inversely with visceral adipose tissue area (r = -0.43, P = 0.01) and visceral to sc adipose tissue ratio (r = -0.49, P = 0.004). Mildly impaired glucose tolerance was noted in four of 21 PI-treated subjects tested. Our results demonstrate not only that PI therapy reduces insulin sensitivity in HIV-infected children but also that it impairs the beta-cell response to this reduction in insulin sensitivity and, in a subset of children, leads to the development of impaired glucose tolerance. The presence of insulin resistance, dyslipidemia, and the significant correlation of reduced insulin

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…

Bayesian estimates of the incidence of rare cancers in Europe.

PubMed

Botta, Laura; Capocaccia, Riccardo; Trama, Annalisa; Herrmann, Christian; Salmerón, Diego; De Angelis, Roberta; Mallone, Sandra; Bidoli, Ettore; Marcos-Gragera, Rafael; Dudek-Godeau, Dorota; Gatta, Gemma; Cleries, Ramon

2018-04-21

The RARECAREnet project has updated the estimates of the burden of the 198 rare cancers in each European country. Suspecting that scant data could affect the reliability of statistical analysis, we employed a Bayesian approach to estimate the incidence of these cancers. We analyzed about 2,000,000 rare cancers diagnosed in 2000-2007 provided by 83 population-based cancer registries from 27 European countries. We considered European incidence rates (IRs), calculated over all the data available in RARECAREnet, as a valid a priori to merge with country-specific observed data. Therefore we provided (1) Bayesian estimates of IRs and the yearly numbers of cases of rare cancers in each country; (2) the expected time (T) in years needed to observe one new case; and (3) practical criteria to decide when to use the Bayesian approach. Bayesian and classical estimates did not differ much; substantial differences (>10%) ranged from 77 rare cancers in Iceland to 14 in England. The smaller the population the larger the number of rare cancers needing a Bayesian approach. Bayesian estimates were useful for cancers with fewer than 150 observed cases in a country during the study period; this occurred mostly when the population of the country is small. For the first time the Bayesian estimates of IRs and the yearly expected numbers of cases for each rare cancer in each individual European country were calculated. Moreover, the indicator T is useful to convey incidence estimates for exceptionally rare cancers and in small countries; it far exceeds the professional lifespan of a medical doctor. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

Rediscovery of Good-Turing estimators via Bayesian nonparametrics.

PubMed

Favaro, Stefano; Nipoti, Bernardo; Teh, Yee Whye

2016-03-01

The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bayesian, has been proposed for estimating discovery probabilities. In this article, we investigate the relationships between the celebrated Good-Turing approach, which is a frequentist nonparametric approach developed in the 1940s, and a Bayesian nonparametric approach recently introduced in the literature. Specifically, under the assumption of a two parameter Poisson-Dirichlet prior, we show that Bayesian nonparametric estimators of discovery probabilities are asymptotically equivalent, for a large sample size, to suitably smoothed Good-Turing estimators. As a by-product of this result, we introduce and investigate a methodology for deriving exact and asymptotic credible intervals to be associated with the Bayesian nonparametric estimators of discovery probabilities. The proposed methodology is illustrated through a comprehensive simulation study and the analysis of Expressed Sequence Tags data generated by sequencing a benchmark complementary DNA library. © 2015, The International Biometric Society.

With or without you: predictive coding and Bayesian inference in the brain

PubMed Central

Aitchison, Laurence; Lengyel, Máté

2018-01-01

Two theoretical ideas have emerged recently with the ambition to provide a unifying functional explanation of neural population coding and dynamics: predictive coding and Bayesian inference. Here, we describe the two theories and their combination into a single framework: Bayesian predictive coding. We clarify how the two theories can be distinguished, despite sharing core computational concepts and addressing an overlapping set of empirical phenomena. We argue that predictive coding is an algorithmic / representational motif that can serve several different computational goals of which Bayesian inference is but one. Conversely, while Bayesian inference can utilize predictive coding, it can also be realized by a variety of other representations. We critically evaluate the experimental evidence supporting Bayesian predictive coding and discuss how to test it more directly. PMID:28942084

Bayesian correction for covariate measurement error: A frequentist evaluation and comparison with regression calibration.

PubMed

Bartlett, Jonathan W; Keogh, Ruth H

2018-06-01

Bayesian approaches for handling covariate measurement error are well established and yet arguably are still relatively little used by researchers. For some this is likely due to unfamiliarity or disagreement with the Bayesian inferential paradigm. For others a contributory factor is the inability of standard statistical packages to perform such Bayesian analyses. In this paper, we first give an overview of the Bayesian approach to handling covariate measurement error, and contrast it with regression calibration, arguably the most commonly adopted approach. We then argue why the Bayesian approach has a number of statistical advantages compared to regression calibration and demonstrate that implementing the Bayesian approach is usually quite feasible for the analyst. Next, we describe the closely related maximum likelihood and multiple imputation approaches and explain why we believe the Bayesian approach to generally be preferable. We then empirically compare the frequentist properties of regression calibration and the Bayesian approach through simulation studies. The flexibility of the Bayesian approach to handle both measurement error and missing data is then illustrated through an analysis of data from the Third National Health and Nutrition Examination Survey.

Content Abstract Classification Using Naive Bayes

NASA Astrophysics Data System (ADS)

Latif, Syukriyanto; Suwardoyo, Untung; Aldrin Wihelmus Sanadi, Edwin

2018-03-01

This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at www.computer.org. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (Multimedia). Systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.

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

Bayesian characterization of uncertainty in species interaction strengths.

PubMed

Wolf, Christopher; Novak, Mark; Gitelman, Alix I

2017-06-01

Considerable effort has been devoted to the estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and obtaining point estimates of parameters that contribute to interaction strength magnitudes, leaving the characterization of uncertainty associated with those estimates unconsidered. We consider a means of characterizing the uncertainty of a generalist predator's interaction strengths by formulating an observational method for estimating a predator's prey-specific per capita attack rates as a Bayesian statistical model. This formulation permits the explicit incorporation of multiple sources of uncertainty. A key insight is the informative nature of several so-called non-informative priors that have been used in modeling the sparse data typical of predator feeding surveys. We introduce to ecology a new neutral prior and provide evidence for its superior performance. We use a case study to consider the attack rates in a New Zealand intertidal whelk predator, and we illustrate not only that Bayesian point estimates can be made to correspond with those obtained by frequentist approaches, but also that estimation uncertainty as described by 95% intervals is more useful and biologically realistic using the Bayesian method. In particular, unlike in bootstrap confidence intervals, the lower bounds of the Bayesian posterior intervals for attack rates do not include zero when a predator-prey interaction is in fact observed. We conclude that the Bayesian framework provides a straightforward, probabilistic characterization of interaction strength uncertainty, enabling future considerations of both the deterministic and stochastic drivers of interaction strength and their impact on food webs.

Bayesian molecular dating: opening up the black box.

PubMed

Bromham, Lindell; Duchêne, Sebastián; Hua, Xia; Ritchie, Andrew M; Duchêne, David A; Ho, Simon Y W

2018-05-01

Molecular dating analyses allow evolutionary timescales to be estimated from genetic data, offering an unprecedented capacity for investigating the evolutionary past of all species. These methods require us to make assumptions about the relationship between genetic change and evolutionary time, often referred to as a 'molecular clock'. Although initially regarded with scepticism, molecular dating has now been adopted in many areas of biology. This broad uptake has been due partly to the development of Bayesian methods that allow complex aspects of molecular evolution, such as variation in rates of change across lineages, to be taken into account. But in order to do this, Bayesian dating methods rely on a range of assumptions about the evolutionary process, which vary in their degree of biological realism and empirical support. These assumptions can have substantial impacts on the estimates produced by molecular dating analyses. The aim of this review is to open the 'black box' of Bayesian molecular dating and have a look at the machinery inside. We explain the components of these dating methods, the important decisions that researchers must make in their analyses, and the factors that need to be considered when interpreting results. We illustrate the effects that the choices of different models and priors can have on the outcome of the analysis, and suggest ways to explore these impacts. We describe some major research directions that may improve the reliability of Bayesian dating. The goal of our review is to help researchers to make informed choices when using Bayesian phylogenetic methods to estimate evolutionary rates and timescales. © 2017 Cambridge Philosophical Society.

Variable Discretisation for Anomaly Detection using Bayesian Networks

DTIC Science & Technology

2017-01-01

UNCLASSIFIED DST- Group –TR–3328 1 Introduction Bayesian network implementations usually require each variable to take on a finite number of mutually...UNCLASSIFIED Variable Discretisation for Anomaly Detection using Bayesian Networks Jonathan Legg National Security and ISR Division Defence Science...and Technology Group DST- Group –TR–3328 ABSTRACT Anomaly detection is the process by which low probability events are automatically found against a

Bayesian truthing as experimental verification of C4ISR sensors

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Forrester, Thomas; Romanov, Volodymyr; Wang, Wenjian; Nielsen, Thomas; Kostrzewski, Andrew

2015-05-01

In this paper, the general methodology for experimental verification/validation of C4ISR and other sensors' performance, is presented, based on Bayesian inference, in general, and binary sensors, in particular. This methodology, called Bayesian Truthing, defines Performance Metrics for binary sensors in: physics, optics, electronics, medicine, law enforcement, C3ISR, QC, ATR (Automatic Target Recognition), terrorism related events, and many others. For Bayesian Truthing, the sensing medium itself is not what is truly important; it is how the decision process is affected.

Bayesian outcome-based strategy classification.

PubMed

Lee, Michael D

2016-03-01

Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014) recently developed a method for making inferences about the decision processes people use in multi-attribute forced choice tasks. Their paper makes a number of worthwhile theoretical and methodological contributions. Theoretically, they provide an insightful psychological motivation for a probabilistic extension of the widely-used "weighted additive" (WADD) model, and show how this model, as well as other important models like "take-the-best" (TTB), can and should be expressed in terms of meaningful priors. Methodologically, they develop an inference approach based on the Minimum Description Length (MDL) principles that balances both the goodness-of-fit and complexity of the decision models they consider. This paper aims to preserve these useful contributions, but provide a complementary Bayesian approach with some theoretical and methodological advantages. We develop a simple graphical model, implemented in JAGS, that allows for fully Bayesian inferences about which models people use to make decisions. To demonstrate the Bayesian approach, we apply it to the models and data considered by Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014), showing how a prior predictive analysis of the models, and posterior inferences about which models people use and the parameter settings at which they use them, can contribute to our understanding of human decision making.

Extending R packages to support 64-bit compiled code: An illustration with spam64 and GIMMS NDVI3g data

NASA Astrophysics Data System (ADS)

Gerber, Florian; Mösinger, Kaspar; Furrer, Reinhard

2017-07-01

Software packages for spatial data often implement a hybrid approach of interpreted and compiled programming languages. The compiled parts are usually written in C, C++, or Fortran, and are efficient in terms of computational speed and memory usage. Conversely, the interpreted part serves as a convenient user-interface and calls the compiled code for computationally demanding operations. The price paid for the user friendliness of the interpreted component is-besides performance-the limited access to low level and optimized code. An example of such a restriction is the 64-bit vector support of the widely used statistical language R. On the R side, users do not need to change existing code and may not even notice the extension. On the other hand, interfacing 64-bit compiled code efficiently is challenging. Since many R packages for spatial data could benefit from 64-bit vectors, we investigate strategies to efficiently pass 64-bit vectors to compiled languages. More precisely, we show how to simply extend existing R packages using the foreign function interface to seamlessly support 64-bit vectors. This extension is shown with the sparse matrix algebra R package spam. The new capabilities are illustrated with an example of GIMMS NDVI3g data featuring a parametric modeling approach for a non-stationary covariance matrix.

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…

Bayesian sparse channel estimation

NASA Astrophysics Data System (ADS)

Chen, Chulong; Zoltowski, Michael D.

2012-05-01

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to estimate and track the time-varying multipath channel is critical to ensure reliable, high data rate communications. It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure to reduce the number of pilot tones and increase the channel estimation quality, the application of compressed sensing to channel estimation is proposed. In this article, to make the compressed channel estimation more feasible for practical applications, it is investigated from a perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. Simulation studies show a significant improvement in channel estimation MSE and less computing time compared to the conventional compressed channel estimation techniques.

Mean Field Variational Bayesian Data Assimilation

NASA Astrophysics Data System (ADS)

Vrettas, M.; Cornford, D.; Opper, M.

2012-04-01

Current data assimilation schemes propose a range of approximate solutions to the classical data assimilation problem, particularly state estimation. Broadly there are three main active research areas: ensemble Kalman filter methods which rely on statistical linearization of the model evolution equations, particle filters which provide a discrete point representation of the posterior filtering or smoothing distribution and 4DVAR methods which seek the most likely posterior smoothing solution. In this paper we present a recent extension to our variational Bayesian algorithm which seeks the most probably posterior distribution over the states, within the family of non-stationary Gaussian processes. Our original work on variational Bayesian approaches to data assimilation sought the best approximating time varying Gaussian process to the posterior smoothing distribution for stochastic dynamical systems. This approach was based on minimising the Kullback-Leibler divergence between the true posterior over paths, and our Gaussian process approximation. So long as the observation density was sufficiently high to bring the posterior smoothing density close to Gaussian the algorithm proved very effective, on lower dimensional systems. However for higher dimensional systems, the algorithm was computationally very demanding. We have been developing a mean field version of the algorithm which treats the state variables at a given time as being independent in the posterior approximation, but still accounts for their relationships between each other in the mean solution arising from the original dynamical system. In this work we present the new mean field variational Bayesian approach, illustrating its performance on a range of classical data assimilation problems. We discuss the potential and limitations of the new approach. We emphasise that the variational Bayesian approach we adopt, in contrast to other variational approaches, provides a bound on the marginal likelihood of

Population forecasts for Bangladesh, using a Bayesian methodology.

PubMed

Mahsin, Md; Hossain, Syed Shahadat

2012-12-01

Population projection for many developing countries could be quite a challenging task for the demographers mostly due to lack of availability of enough reliable data. The objective of this paper is to present an overview of the existing methods for population forecasting and to propose an alternative based on the Bayesian statistics, combining the formality of inference. The analysis has been made using Markov Chain Monte Carlo (MCMC) technique for Bayesian methodology available with the software WinBUGS. Convergence diagnostic techniques available with the WinBUGS software have been applied to ensure the convergence of the chains necessary for the implementation of MCMC. The Bayesian approach allows for the use of observed data and expert judgements by means of appropriate priors, and a more realistic population forecasts, along with associated uncertainty, has been possible.

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

Posterior Predictive Bayesian Phylogenetic Model Selection

PubMed Central

Lewis, Paul O.; Xie, Wangang; Chen, Ming-Hui; Fan, Yu; Kuo, Lynn

2014-01-01

We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand–Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (GGp) and goodness-of-fit (GGg), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML) which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data. [Bayesian; conditional predictive ordinate; CPO; L-measure; LPML; model selection; phylogenetics; posterior predictive.] PMID:24193892

Discriminative Bayesian Dictionary Learning for Classification.

PubMed

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations

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

Chen, Peng, E-mail: peng@ices.utexas.edu; Schwab, Christoph, E-mail: christoph.schwab@sam.math.ethz.ch

2016-07-01

We extend the reduced basis (RB) accelerated Bayesian inversion methods for affine-parametric, linear operator equations which are considered in [16,17] to non-affine, nonlinear parametric operator equations. We generalize the analysis of sparsity of parametric forward solution maps in [20] and of Bayesian inversion in [48,49] to the fully discrete setting, including Petrov–Galerkin high-fidelity (“HiFi”) discretization of the forward maps. We develop adaptive, stochastic collocation based reduction methods for the efficient computation of reduced bases on the parametric solution manifold. The nonaffinity and nonlinearity with respect to (w.r.t.) the distributed, uncertain parameters and the unknown solution is collocated; specifically, by themore » so-called Empirical Interpolation Method (EIM). For the corresponding Bayesian inversion problems, computational efficiency is enhanced in two ways: first, expectations w.r.t. the posterior are computed by adaptive quadratures with dimension-independent convergence rates proposed in [49]; the present work generalizes [49] to account for the impact of the PG discretization in the forward maps on the convergence rates of the Quantities of Interest (QoI for short). Second, we propose to perform the Bayesian estimation only w.r.t. a parsimonious, RB approximation of the posterior density. Based on the approximation results in [49], the infinite-dimensional parametric, deterministic forward map and operator admit N-term RB and EIM approximations which converge at rates which depend only on the sparsity of the parametric forward map. In several numerical experiments, the proposed algorithms exhibit dimension-independent convergence rates which equal, at least, the currently known rate estimates for N-term approximation. We propose to accelerate Bayesian estimation by first offline construction of reduced basis surrogates of the Bayesian posterior density. The parsimonious surrogates can then be employed for online

Space Shuttle RTOS Bayesian Network

NASA Technical Reports Server (NTRS)

Morris, A. Terry; Beling, Peter A.

2001-01-01

With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores

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 Approaches to Imputation, Hypothesis Testing, and Parameter Estimation

ERIC Educational Resources Information Center

Ross, Steven J.; Mackey, Beth

2015-01-01

This chapter introduces three applications of Bayesian inference to common and novel issues in second language research. After a review of the critiques of conventional hypothesis testing, our focus centers on ways Bayesian inference can be used for dealing with missing data, for testing theory-driven substantive hypotheses without a default null…

Ockham's razor and Bayesian analysis. [statistical theory for systems evaluation

NASA Technical Reports Server (NTRS)

Jefferys, William H.; Berger, James O.

1992-01-01

'Ockham's razor', the ad hoc principle enjoining the greatest possible simplicity in theoretical explanations, is presently shown to be justifiable as a consequence of Bayesian inference; Bayesian analysis can, moreover, clarify the nature of the 'simplest' hypothesis consistent with the given data. By choosing the prior probabilities of hypotheses, it becomes possible to quantify the scientific judgment that simpler hypotheses are more likely to be correct. Bayesian analysis also shows that a hypothesis with fewer adjustable parameters intrinsically possesses an enhanced posterior probability, due to the clarity of its predictions.

Bayesian network learning for natural hazard assessments

NASA Astrophysics Data System (ADS)

Vogel, Kristin

2016-04-01

Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables

A Bayesian approach to reliability and confidence

NASA Technical Reports Server (NTRS)

Barnes, Ron

1989-01-01

The historical evolution of NASA's interest in quantitative measures of reliability assessment is outlined. The introduction of some quantitative methodologies into the Vehicle Reliability Branch of the Safety, Reliability and Quality Assurance (SR and QA) Division at Johnson Space Center (JSC) was noted along with the development of the Extended Orbiter Duration--Weakest Link study which will utilize quantitative tools for a Bayesian statistical analysis. Extending the earlier work of NASA sponsor, Richard Heydorn, researchers were able to produce a consistent Bayesian estimate for the reliability of a component and hence by a simple extension for a system of components in some cases where the rate of failure is not constant but varies over time. Mechanical systems in general have this property since the reliability usually decreases markedly as the parts degrade over time. While they have been able to reduce the Bayesian estimator to a simple closed form for a large class of such systems, the form for the most general case needs to be attacked by the computer. Once a table is generated for this form, researchers will have a numerical form for the general solution. With this, the corresponding probability statements about the reliability of a system can be made in the most general setting. Note that the utilization of uniform Bayesian priors represents a worst case scenario in the sense that as researchers incorporate more expert opinion into the model, they will be able to improve the strength of the probability calculations.

Statistical approaches for the determination of cut points in anti-drug antibody bioassays.

PubMed

Schaarschmidt, Frank; Hofmann, Matthias; Jaki, Thomas; Grün, Bettina; Hothorn, Ludwig A

2015-03-01

Cut points in immunogenicity assays are used to classify future specimens into anti-drug antibody (ADA) positive or negative. To determine a cut point during pre-study validation, drug-naive specimens are often analyzed on multiple microtiter plates taking sources of future variability into account, such as runs, days, analysts, gender, drug-spiked and the biological variability of un-spiked specimens themselves. Five phenomena may complicate the statistical cut point estimation: i) drug-naive specimens may contain already ADA-positives or lead to signals that erroneously appear to be ADA-positive, ii) mean differences between plates may remain after normalization of observations by negative control means, iii) experimental designs may contain several factors in a crossed or hierarchical structure, iv) low sample sizes in such complex designs lead to low power for pre-tests on distribution, outliers and variance structure, and v) the choice between normal and log-normal distribution has a serious impact on the cut point. We discuss statistical approaches to account for these complex data: i) mixture models, which can be used to analyze sets of specimens containing an unknown, possibly larger proportion of ADA-positive specimens, ii) random effects models, followed by the estimation of prediction intervals, which provide cut points while accounting for several factors, and iii) diagnostic plots, which allow the post hoc assessment of model assumptions. All methods discussed are available in the corresponding R add-on package mixADA. Copyright © 2015 Elsevier B.V. All rights reserved.

A NAIVE BAYES SOURCE CLASSIFIER FOR X-RAY SOURCES

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

Broos, Patrick S.; Getman, Konstantin V.; Townsley, Leisa K.

2011-05-01

The Chandra Carina Complex Project (CCCP) provides a sensitive X-ray survey of a nearby starburst region over >1 deg{sup 2} in extent. Thousands of faint X-ray sources are found, many concentrated into rich young stellar clusters. However, significant contamination from unrelated Galactic and extragalactic sources is present in the X-ray catalog. We describe the use of a naive Bayes classifier to assign membership probabilities to individual sources, based on source location, X-ray properties, and visual/infrared properties. For the particular membership decision rule adopted, 75% of CCCP sources are classified as members, 11% are classified as contaminants, and 14% remain unclassified.more » The resulting sample of stars likely to be Carina members is used in several other studies, which appear in this special issue devoted to the CCCP.« less

Model-based Bayesian inference for ROC data analysis

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Bae, K. Ty

2013-03-01

This paper presents a study of model-based Bayesian inference to Receiver Operating Characteristics (ROC) data. The model is a simple version of general non-linear regression model. Different from Dorfman model, it uses a probit link function with a covariate variable having zero-one two values to express binormal distributions in a single formula. Model also includes a scale parameter. Bayesian inference is implemented by Markov Chain Monte Carlo (MCMC) method carried out by Bayesian analysis Using Gibbs Sampling (BUGS). Contrast to the classical statistical theory, Bayesian approach considers model parameters as random variables characterized by prior distributions. With substantial amount of simulated samples generated by sampling algorithm, posterior distributions of parameters as well as parameters themselves can be accurately estimated. MCMC-based BUGS adopts Adaptive Rejection Sampling (ARS) protocol which requires the probability density function (pdf) which samples are drawing from be log concave with respect to the targeted parameters. Our study corrects a common misconception and proves that pdf of this regression model is log concave with respect to its scale parameter. Therefore, ARS's requirement is satisfied and a Gaussian prior which is conjugate and possesses many analytic and computational advantages is assigned to the scale parameter. A cohort of 20 simulated data sets and 20 simulations from each data set are used in our study. Output analysis and convergence diagnostics for MCMC method are assessed by CODA package. Models and methods by using continuous Gaussian prior and discrete categorical prior are compared. Intensive simulations and performance measures are given to illustrate our practice in the framework of model-based Bayesian inference using MCMC method.

Robust Bayesian Factor Analysis

ERIC Educational Resources Information Center

Hayashi, Kentaro; Yuan, Ke-Hai

2003-01-01

Bayesian factor analysis (BFA) assumes the normal distribution of the current sample conditional on the parameters. Practical data in social and behavioral sciences typically have significant skewness and kurtosis. If the normality assumption is not attainable, the posterior analysis will be inaccurate, although the BFA depends less on the current…

Testing adaptive toolbox models: a Bayesian hierarchical approach.

PubMed

Scheibehenne, Benjamin; Rieskamp, Jörg; Wagenmakers, Eric-Jan

2013-01-01

Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

Small molecule drug A-769662 and AMP synergistically activate naive AMPK independent of upstream kinase signaling.

PubMed

Scott, John W; Ling, Naomi; Issa, Samah M A; Dite, Toby A; O'Brien, Matthew T; Chen, Zhi-Ping; Galic, Sandra; Langendorf, Christopher G; Steinberg, Gregory R; Kemp, Bruce E; Oakhill, Jonathan S

2014-05-22

The AMP-activated protein kinase (AMPK) is a metabolic stress-sensing αβγ heterotrimer responsible for energy homeostasis, making it a therapeutic target for metabolic diseases such as type 2 diabetes and obesity. AMPK signaling is triggered by phosphorylation on the AMPK α subunit activation loop Thr172 by upstream kinases. Dephosphorylated, naive AMPK is thought to be catalytically inactive and insensitive to allosteric regulation by AMP and direct AMPK-activating drugs such as A-769662. Here we show that A-769662 activates AMPK independently of α-Thr172 phosphorylation, provided β-Ser108 is phosphorylated. Although neither A-769662 nor AMP individually stimulate the activity of dephosphorylated AMPK, together they stimulate >1,000-fold, bypassing the requirement for β-Ser108 phosphorylation. Consequently A-769662 and AMP together activate naive AMPK entirely allosterically and independently of upstream kinase signaling. These findings have important implications for development of AMPK-targeting therapeutics and point to possible combinatorial therapeutic strategies based on AMP and AMPK drugs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Nonlinear and non-Gaussian Bayesian based handwriting beautification

NASA Astrophysics Data System (ADS)

Shi, Cao; Xiao, Jianguo; Xu, Canhui; Jia, Wenhua

2013-03-01

A framework is proposed in this paper to effectively and efficiently beautify handwriting by means of a novel nonlinear and non-Gaussian Bayesian algorithm. In the proposed framework, format and size of handwriting image are firstly normalized, and then typeface in computer system is applied to optimize vision effect of handwriting. The Bayesian statistics is exploited to characterize the handwriting beautification process as a Bayesian dynamic model. The model parameters to translate, rotate and scale typeface in computer system are controlled by state equation, and the matching optimization between handwriting and transformed typeface is employed by measurement equation. Finally, the new typeface, which is transformed from the original one and gains the best nonlinear and non-Gaussian optimization, is the beautification result of handwriting. Experimental results demonstrate the proposed framework provides a creative handwriting beautification methodology to improve visual acceptance.

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.

A comparison of discontinuation rates of tofacitinib and biologic disease-modifying anti-rheumatic drugs in rheumatoid arthritis: a systematic review and Bayesian network meta-analysis.

PubMed

Park, Sun-Kyeong; Lee, Min-Young; Jang, Eun-Jin; Kim, Hye-Lin; Ha, Dong-Mun; Lee, Eui-Kyung

2017-01-01

The purpose of this study was to compare the discontinuation rates of tofacitinib and biologics (tumour necrosis factor inhibitors (TNFi), abatacept, rituximab, and tocilizumab) in rheumatoid arthritis (RA) patients considering inadequate responses (IRs) to previous treatment(s). Randomised controlled trials of tofacitinib and biologics - reporting at least one total discontinuation, discontinuation due to lack of efficacy (LOE), and discontinuation due to adverse events (AEs) - were identified through systematic review. The analyses were conducted for patients with IRs to conventional synthetic disease-modifying anti-rheumatic drugs (cDMARDs) and for patients with biologics-IR, separately. Bayesian network meta-analysis was used to estimate rate ratio (RR) of a biologic relative to tofacitinib with 95% credible interval (CrI), and probability of RR being <1 (P[RR<1]). The analyses of 34 studies showed no significant differences in discontinuation rates between tofacitinib and biologics in the cDMARDs-IR group. In the biologics-IR group, however, TNFi (RR 0.17, 95% CrI 0.01-3.61, P[RR<1] 92.0%) and rituximab (RR 0.20, 95% CrI 0.01-2.91, P[RR<1] 92.3%) showed significantly lower total discontinuation rates than tofacitinib did. Despite the difference, discontinuation cases owing to LOE and AEs revealed that tofacitinib was comparable to the biologics. The comparability of discontinuation rate between tofacitinib and biologics was different based on previous treatments and discontinuation reasons: LOE, AEs, and total (due to other reasons). Therefore, those factors need to be considered to decide the optimal treatment strategy.

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

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

Nociceptin/orphanin FQ decreases glutamate transmission and blocks ethanol-induced effects in the central amygdala of naive and ethanol-dependent rats.

PubMed

Kallupi, Marsida; Varodayan, Florence P; Oleata, Christopher S; Correia, Diego; Luu, George; Roberto, Marisa

2014-04-01

The central nucleus of the amygdala (CeA) mediates several addiction-related processes and nociceptin/orphanin FQ (nociceptin) regulates ethanol intake and anxiety-like behaviors. Glutamatergic synapses, in the CeA and throughout the brain, are very sensitive to ethanol and contribute to alcohol reinforcement, tolerance, and dependence. Previously, we reported that in the rat CeA, acute and chronic ethanol exposures significantly decrease glutamate transmission by both pre- and postsynaptic actions. In this study, using electrophysiological techniques in an in vitro CeA slice preparation, we investigated the effects of nociceptin on glutamatergic transmission and its interaction with acute ethanol in naive and ethanol-dependent rats. We found that nociceptin (100-1000 nM) diminished basal-evoked compound glutamatergic receptor-mediated excitatory postsynaptic potentials (EPSPs) and spontaneous and miniature EPSCs (s/mEPSCs) by mainly decreasing glutamate release in the CeA of naive rats. Notably, nociceptin blocked the inhibition induced by acute ethanol (44 mM) and ethanol blocked the nociceptin-induced inhibition of evoked EPSPs in CeA neurons of naive rats. In neurons from chronic ethanol-treated (ethanol-dependent) rats, the nociceptin-induced inhibition of evoked EPSP amplitude was not significantly different from that in naive rats. Application of [Nphe1]Nociceptin(1-13)NH2, a nociceptin receptor (NOP) antagonist, revealed tonic inhibitory activity of NOP on evoked CeA glutamatergic transmission only in ethanol-dependent rats. The antagonist also blocked nociceptin-induced decreases in glutamatergic responses, but did not affect ethanol-induced decreases in evoked EPSP amplitude. Taken together, these studies implicate a potential role for the nociceptin system in regulating glutamatergic transmission and a complex interaction with ethanol at CeA glutamatergic synapses.

High level of APOBEC3F/3G editing in HIV-2 DNA vif and pol sequences from antiretroviral-naive patients.

PubMed

Bertine, Mélanie; Charpentier, Charlotte; Visseaux, Benoit; Storto, Alexandre; Collin, Gilles; Larrouy, Lucile; Damond, Florence; Matheron, Sophie; Brun-Vézinet, Françoise; Descamps, Diane

2015-04-24

In HIV-1, hypermutation introduced by APOBEC3F/3G cytidine deaminase activity leads to defective viruses. In-vivo impact of APOBEC3F/3G editing on HIV-2 sequences remains unknown. The objective of this study was to assess the level of APOBEC3F/3G editing in HIV-2-infected antiretroviral-naive patients. Direct sequencing of vif and pol regions was performed on HIV-2 proviral DNA from antiretroviral-naive patients included in the French Agence Nationale de Recherches sur le SIDA et les hépatites virales CO5 HIV-2 cohort. Hypermutated sequences were identified using Hypermut2.0 program. HIV-1 proviral sequences from Genbank were also assessed. Among 82 antiretroviral-naive HIV-2-infected patients assessed, 15 (28.8%) and five (16.7%) displayed Vif proviral defective sequences in HIV-2 groups A and B, respectively. A lower proportion of defective sequences was observed in protease-reverse transcriptase region. A higher median number of G-to-A mutations was observed in HIV-2 group B than in group A, both in Vif and protease-reverse transcriptase regions (P = 0.02 and P = 0.006, respectively). Compared with HIV-1 Vif sequences, a higher number of Vif defective sequences was observed in HIV-2 group A (P = 0.00001) and group B sequences (P = 0.013). We showed for the first time a high level of APOBEC3F/3G editing in HIV-2 sequences from antiretroviral-naive patients. Our study reported a group effect with a significantly higher level of APOBEC3F/3G editing in HIV-2 group B than in group A sequences.

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

PubMed Central

Rohe, Tim; Noppeney, Uta

2015-01-01

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world. PMID:25710328

A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study

ERIC Educational Resources Information Center

Kaplan, David; Chen, Jianshen

2012-01-01

A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for…

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

Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies

ERIC Educational Resources Information Center

Chen, Jianshen; Kaplan, David

2015-01-01

Bayesian alternatives to frequentist propensity score approaches have recently been proposed. However, few studies have investigated their covariate balancing properties. This article compares a recently developed two-step Bayesian propensity score approach to the frequentist approach with respect to covariate balance. The effects of different…

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.

[Bayesian statistics in medicine -- part II: main applications and inference].

PubMed

Montomoli, C; Nichelatti, M

2008-01-01

Bayesian statistics is not only used when one is dealing with 2-way tables, but it can be used for inferential purposes. Using the basic concepts presented in the first part, this paper aims to give a simple overview of Bayesian methods by introducing its foundation (Bayes' theorem) and then applying this rule to a very simple practical example; whenever possible, the elementary processes at the basis of analysis are compared to those of frequentist (classical) statistical analysis. The Bayesian reasoning is naturally connected to medical activity, since it appears to be quite similar to a diagnostic process.

A Gibbs sampler for Bayesian analysis of site-occupancy data

USGS Publications Warehouse

Dorazio, Robert M.; Rodriguez, Daniel Taylor

2012-01-01

1. A Bayesian analysis of site-occupancy data containing covariates of species occurrence and species detection probabilities is usually completed using Markov chain Monte Carlo methods in conjunction with software programs that can implement those methods for any statistical model, not just site-occupancy models. Although these software programs are quite flexible, considerable experience is often required to specify a model and to initialize the Markov chain so that summaries of the posterior distribution can be estimated efficiently and accurately. 2. As an alternative to these programs, we develop a Gibbs sampler for Bayesian analysis of site-occupancy data that include covariates of species occurrence and species detection probabilities. This Gibbs sampler is based on a class of site-occupancy models in which probabilities of species occurrence and detection are specified as probit-regression functions of site- and survey-specific covariate measurements. 3. To illustrate the Gibbs sampler, we analyse site-occupancy data of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly species in Switzerland. Our analysis includes a comparison of results based on Bayesian and classical (non-Bayesian) methods of inference. We also provide code (based on the R software program) for conducting Bayesian and classical analyses of site-occupancy data.

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

Bayesian methods including nonrandomized study data increased the efficiency of postlaunch RCTs.

PubMed

Schmidt, Amand F; Klugkist, Irene; Klungel, Olaf H; Nielen, Mirjam; de Boer, Anthonius; Hoes, Arno W; Groenwold, Rolf H H

2015-04-01

Findings from nonrandomized studies on safety or efficacy of treatment in patient subgroups may trigger postlaunch randomized clinical trials (RCTs). In the analysis of such RCTs, results from nonrandomized studies are typically ignored. This study explores the trade-off between bias and power of Bayesian RCT analysis incorporating information from nonrandomized studies. A simulation study was conducted to compare frequentist with Bayesian analyses using noninformative and informative priors in their ability to detect interaction effects. In simulated subgroups, the effect of a hypothetical treatment differed between subgroups (odds ratio 1.00 vs. 2.33). Simulations varied in sample size, proportions of the subgroups, and specification of the priors. As expected, the results for the informative Bayesian analyses were more biased than those from the noninformative Bayesian analysis or frequentist analysis. However, because of a reduction in posterior variance, informative Bayesian analyses were generally more powerful to detect an effect. In scenarios where the informative priors were in the opposite direction of the RCT data, type 1 error rates could be 100% and power 0%. Bayesian methods incorporating data from nonrandomized studies can meaningfully increase power of interaction tests in postlaunch RCTs. Copyright © 2015 Elsevier Inc. All rights reserved.

Bayesian analysis of caustic-crossing microlensing events

NASA Astrophysics Data System (ADS)

Cassan, A.; Horne, K.; Kains, N.; Tsapras, Y.; Browne, P.

2010-06-01

Aims: Caustic-crossing binary-lens microlensing events are important anomalous events because they are capable of detecting an extrasolar planet companion orbiting the lens star. Fast and robust modelling methods are thus of prime interest in helping to decide whether a planet is detected by an event. Cassan introduced a new set of parameters to model binary-lens events, which are closely related to properties of the light curve. In this work, we explain how Bayesian priors can be added to this framework, and investigate on interesting options. Methods: We develop a mathematical formulation that allows us to compute analytically the priors on the new parameters, given some previous knowledge about other physical quantities. We explicitly compute the priors for a number of interesting cases, and show how this can be implemented in a fully Bayesian, Markov chain Monte Carlo algorithm. Results: Using Bayesian priors can accelerate microlens fitting codes by reducing the time spent considering physically implausible models, and helps us to discriminate between alternative models based on the physical plausibility of their parameters.

Bayesian posterior distributions without Markov chains.

PubMed

Cole, Stephen R; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B

2012-03-01

Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976-1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984-1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.

The Application of Bayesian Analysis to Issues in Developmental Research

ERIC Educational Resources Information Center

Walker, Lawrence J.; Gustafson, Paul; Frimer, Jeremy A.

2007-01-01

This article reviews the concepts and methods of Bayesian statistical analysis, which can offer innovative and powerful solutions to some challenging analytical problems that characterize developmental research. In this article, we demonstrate the utility of Bayesian analysis, explain its unique adeptness in some circumstances, address some…

A Tutorial Introduction to Bayesian Models of Cognitive Development

ERIC Educational Resources Information Center

Perfors, Amy; Tenenbaum, Joshua B.; Griffiths, Thomas L.; Xu, Fei

2011-01-01

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the "what", the "how", and the "why" of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for…

Bayesian methods for characterizing unknown parameters of material models

DOE PAGES

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

2016-02-04

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less

Bayesian methods for characterizing unknown parameters of material models

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

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less

A Bayesian approach to multivariate measurement system assessment

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

Hamada, Michael Scott

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

A Bayesian approach to multivariate measurement system assessment

DOE PAGES

Hamada, Michael Scott

2016-07-01

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

Spectral Bayesian Knowledge Tracing

ERIC Educational Resources Information Center

Falakmasir, Mohammad; Yudelson, Michael; Ritter, Steve; Koedinger, Ken

2015-01-01

Bayesian Knowledge Tracing (BKT) has been in wide use for modeling student skill acquisition in Intelligent Tutoring Systems (ITS). BKT tracks and updates student's latent mastery of a skill as a probability distribution of a binary variable. BKT does so by accounting for observed student successes in applying the skill correctly, where success is…

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

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.

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.

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.

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…

Using Alien Coins to Test Whether Simple Inference Is Bayesian

ERIC Educational Resources Information Center

Cassey, Peter; Hawkins, Guy E.; Donkin, Chris; Brown, Scott D.

2016-01-01

Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we…

Applying Bayesian statistics to the study of psychological trauma: A suggestion for future research.

PubMed

Yalch, Matthew M

2016-03-01

Several contemporary researchers have noted the virtues of Bayesian methods of data analysis. Although debates continue about whether conventional or Bayesian statistics is the "better" approach for researchers in general, there are reasons why Bayesian methods may be well suited to the study of psychological trauma in particular. This article describes how Bayesian statistics offers practical solutions to the problems of data non-normality, small sample size, and missing data common in research on psychological trauma. After a discussion of these problems and the effects they have on trauma research, this article explains the basic philosophical and statistical foundations of Bayesian statistics and how it provides solutions to these problems using an applied example. Results of the literature review and the accompanying example indicates the utility of Bayesian statistics in addressing problems common in trauma research. Bayesian statistics provides a set of methodological tools and a broader philosophical framework that is useful for trauma researchers. Methodological resources are also provided so that interested readers can learn more. (c) 2016 APA, all rights reserved).

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Fatal attraction: sexually cannibalistic invaders attract naive native mantids

PubMed Central

Fea, Murray P.; Stanley, Margaret C.; Holwell, Gregory I.

2013-01-01

Overlap in the form of sexual signals such as pheromones raises the possibility of reproductive interference by invasive species on similar, yet naive native species. Here, we test the potential for reproductive interference through heterospecific mate attraction and subsequent predation of males by females of a sexually cannibalistic invasive praying mantis. Miomantis caffra is invasive in New Zealand, where it is widely considered to be displacing the only native mantis species, Orthodera novaezealandiae, and yet mechanisms behind this displacement are unknown. We demonstrate that native males are more attracted to the chemical cues of introduced females than those of conspecific females. Heterospecific pairings also resulted in a high degree of mortality for native males. This provides evidence for a mechanism behind displacement that has until now been undetected and highlights the potential for reproductive interference to greatly influence the impact of an invasive species. PMID:24284560

Finite‐fault Bayesian inversion of teleseismic body waves

USGS Publications Warehouse

Clayton, Brandon; Hartzell, Stephen; Moschetti, Morgan P.; Minson, Sarah E.

2017-01-01

Inverting geophysical data has provided fundamental information about the behavior of earthquake rupture. However, inferring kinematic source model parameters for finite‐fault ruptures is an intrinsically underdetermined problem (the problem of nonuniqueness), because we are restricted to finite noisy observations. Although many studies use least‐squares techniques to make the finite‐fault problem tractable, these methods generally lack the ability to apply non‐Gaussian error analysis and the imposition of nonlinear constraints. However, the Bayesian approach can be employed to find a Gaussian or non‐Gaussian distribution of all probable model parameters, while utilizing nonlinear constraints. We present case studies to quantify the resolving power and associated uncertainties using only teleseismic body waves in a Bayesian framework to infer the slip history for a synthetic case and two earthquakes: the 2011 Mw 7.1 Van, east Turkey, earthquake and the 2010 Mw 7.2 El Mayor–Cucapah, Baja California, earthquake. In implementing the Bayesian method, we further present two distinct solutions to investigate the uncertainties by performing the inversion with and without velocity structure perturbations. We find that the posterior ensemble becomes broader when including velocity structure variability and introduces a spatial smearing of slip. Using the Bayesian framework solely on teleseismic body waves, we find rake is poorly constrained by the observations and rise time is poorly resolved when slip amplitude is low.

A Bayesian test for Hardy–Weinberg equilibrium of biallelic X-chromosomal markers

PubMed Central

Puig, X; Ginebra, J; Graffelman, J

2017-01-01

The X chromosome is a relatively large chromosome, harboring a lot of genetic information. Much of the statistical analysis of X-chromosomal information is complicated by the fact that males only have one copy. Recently, frequentist statistical tests for Hardy–Weinberg equilibrium have been proposed specifically for dealing with markers on the X chromosome. Bayesian test procedures for Hardy–Weinberg equilibrium for the autosomes have been described, but Bayesian work on the X chromosome in this context is lacking. This paper gives the first Bayesian approach for testing Hardy–Weinberg equilibrium with biallelic markers at the X chromosome. Marginal and joint posterior distributions for the inbreeding coefficient in females and the male to female allele frequency ratio are computed, and used for statistical inference. The paper gives a detailed account of the proposed Bayesian test, and illustrates it with data from the 1000 Genomes project. In that implementation, a novel approach to tackle multiple testing from a Bayesian perspective through posterior predictive checks is used. PMID:28900292

Bayesian stable isotope mixing models

EPA Science Inventory

In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching Bayesian statistical framework which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixtur...

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.

Predicting Ecstasy Use among Young People at Risk: A Prospective Study of Initially Ecstasy-Naive Subjects

ERIC Educational Resources Information Center

Vervaeke, Hylke K.E.; Benschop, Annemieke; Van Den Brink, Wim; Korf, Dirk J.

2008-01-01

Our aim is to identify predictors of first-time ecstasy use in a prospective study among young people at risk. As part of the multidisciplinary Netherlands XTC Toxicity Study (NeXT), we monitored 188 subjects aged up to 18 years who were ecstasy-naive at baseline but seemed likely to start taking ecstasy in the near future. After an 11- to…

Nonparametric Bayesian Modeling for Automated Database Schema Matching

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

Ferragut, Erik M; Laska, Jason A

2015-01-01

The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.

Spectral likelihood expansions for Bayesian inference

NASA Astrophysics Data System (ADS)

Nagel, Joseph B.; Sudret, Bruno

2016-03-01

A spectral approach to Bayesian inference is presented. It pursues the emulation of the posterior probability density. The starting point is a series expansion of the likelihood function in terms of orthogonal polynomials. From this spectral likelihood expansion all statistical quantities of interest can be calculated semi-analytically. The posterior is formally represented as the product of a reference density and a linear combination of polynomial basis functions. Both the model evidence and the posterior moments are related to the expansion coefficients. This formulation avoids Markov chain Monte Carlo simulation and allows one to make use of linear least squares instead. The pros and cons of spectral Bayesian inference are discussed and demonstrated on the basis of simple applications from classical statistics and inverse modeling.

A Bayesian perspective on magnitude estimation.

PubMed

Petzschner, Frederike H; Glasauer, Stefan; Stephan, Klaas E

2015-05-01

Our representation of the physical world requires judgments of magnitudes, such as loudness, distance, or time. Interestingly, magnitude estimates are often not veridical but subject to characteristic biases. These biases are strikingly similar across different sensory modalities, suggesting common processing mechanisms that are shared by different sensory systems. However, the search for universal neurobiological principles of magnitude judgments requires guidance by formal theories. Here, we discuss a unifying Bayesian framework for understanding biases in magnitude estimation. This Bayesian perspective enables a re-interpretation of a range of established psychophysical findings, reconciles seemingly incompatible classical views on magnitude estimation, and can guide future investigations of magnitude estimation and its neurobiological mechanisms in health and in psychiatric diseases, such as schizophrenia. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Bayesian approach for the cost-effectiveness evaluation of healthcare technologies].

PubMed

Berchialla, Paola; Gregori, Dario; Brunello, Franco; Veltri, Andrea; Petrinco, Michele; Pagano, Eva

2009-01-01

The development of Bayesian statistical methods for the assessment of the cost-effectiveness of health care technologies is reviewed. Although many studies adopt a frequentist approach, several authors have advocated the use of Bayesian methods in health economics. Emphasis has been placed on the advantages of the Bayesian approach, which include: (i) the ability to make more intuitive and meaningful inferences; (ii) the ability to tackle complex problems, such as allowing for the inclusion of patients who generate no cost, thanks to the availability of powerful computational algorithms; (iii) the importance of a full use of quantitative and structural prior information to produce realistic inferences. Much literature comparing the cost-effectiveness of two treatments is based on the incremental cost-effectiveness ratio. However, new methods are arising with the purpose of decision making. These methods are based on a net benefits approach. In the present context, the cost-effectiveness acceptability curves have been pointed out to be intrinsically Bayesian in their formulation. They plot the probability of a positive net benefit against the threshold cost of a unit increase in efficacy.A case study is presented in order to illustrate the Bayesian statistics in the cost-effectiveness analysis. Emphasis is placed on the cost-effectiveness acceptability curves. Advantages and disadvantages of the method described in this paper have been compared to frequentist methods and discussed.

A Bayesian Approach for Image Segmentation with Shape Priors

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

Chang, Hang; Yang, Qing; Parvin, Bahram

2008-06-20

Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missingparts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances quality of the segmentation where color and/or texture are not solely adequate. The novelties of our approach are in (i) formulating the segmentation problem in a well-de?ned Bayesian framework with multiple shape priors, (ii) ef?ciently estimating parameters of the Bayesian model, and (iii) multi-object segmentationmore » through user-speci?ed priors. We demonstrate the effectiveness of our method on a set of natural and synthetic images.« less

Bayesian parameter estimation for chiral effective field theory

NASA Astrophysics Data System (ADS)

Wesolowski, Sarah; Furnstahl, Richard; Phillips, Daniel; Klco, Natalie

2016-09-01

The low-energy constants (LECs) of a chiral effective field theory (EFT) interaction in the two-body sector are fit to observable data using a Bayesian parameter estimation framework. By using Bayesian prior probability distributions (pdfs), we quantify relevant physical expectations such as LEC naturalness and include them in the parameter estimation procedure. The final result is a posterior pdf for the LECs, which can be used to propagate uncertainty resulting from the fit to data to the final observable predictions. The posterior pdf also allows an empirical test of operator redundancy and other features of the potential. We compare results of our framework with other fitting procedures, interpreting the underlying assumptions in Bayesian probabilistic language. We also compare results from fitting all partial waves of the interaction simultaneously to cross section data compared to fitting to extracted phase shifts, appropriately accounting for correlations in the data. Supported in part by the NSF and DOE.

Bayesian learning of visual chunks by human observers

PubMed Central

Orbán, Gergő; Fiser, József; Aslin, Richard N.; Lengyel, Máté

2008-01-01

Efficient and versatile processing of any hierarchically structured information requires a learning mechanism that combines lower-level features into higher-level chunks. We investigated this chunking mechanism in humans with a visual pattern-learning paradigm. We developed an ideal learner based on Bayesian model comparison that extracts and stores only those chunks of information that are minimally sufficient to encode a set of visual scenes. Our ideal Bayesian chunk learner not only reproduced the results of a large set of previous empirical findings in the domain of human pattern learning but also made a key prediction that we confirmed experimentally. In accordance with Bayesian learning but contrary to associative learning, human performance was well above chance when pair-wise statistics in the exemplars contained no relevant information. Thus, humans extract chunks from complex visual patterns by generating accurate yet economical representations and not by encoding the full correlational structure of the input. PMID:18268353

Attention in a Bayesian Framework

PubMed Central

Whiteley, Louise; Sahani, Maneesh

2012-01-01

The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models of perception, and use this observation to frame a new computational account of the need for, and action of, attention – unifying diverse attentional phenomena in a way that goes beyond previous inferential, probabilistic and Bayesian models. Attentional effects are most evident in cluttered environments, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental settings, where cues shape expectations about a small number of upcoming stimuli and thus convey “prior” information about clearly defined objects. While operationally consistent with the experiments it seeks to describe, this view of attention as prior seems to miss many essential elements of both its selective and integrative roles, and thus cannot be easily extended to complex environments. We suggest that the resource bottleneck stems from the computational intractability of exact perceptual inference in complex settings, and that attention reflects an evolved mechanism for approximate inference which can be shaped to refine the local accuracy of perception. We show that this approach extends the simple picture of attention as prior, so as to provide a unified and computationally driven account of both selective and integrative attentional phenomena. PMID:22712010

[Nevirapine related hepatotoxicity: the prevalence and risk factors in a cohort of ART naive Han Chinese with AIDS].

PubMed

Gao, Shi-cheng; Gui, Xi-en; Deng, Li-ping; Zhang, Yong-xi; Yan, Ya-jun; Rong, Yu-ping; Liang, Ke; Yang, Rong-rong

2010-09-01

To investigate the incidence of hepatotoxicity in acquired immunodeficiency syndrome (AIDS) patients on combined anti-retroviral therapy (cART) containing nevirapine (NVP) and to assess the risk factors and its impact on cART. 330 AIDS patients from March 2003 to June 2008 at local county were enrolled and a retrospective study using Kaplan-meier survival and Multivariate logistic regression modeling was conducted. 267 out of 330 patients received NVP based cART and 63 cases received EFV-based cART. The deference of prevalences of hepatotoxicity between the two groups is statistically significant (Chi2 = 6.691, P = 0.01). 133 out of 267 (49.8%) patients on NVP based cART had at least one episode of ALT elevation during a median 21 months (interquartile ranges, IQR 6, 37) follow-up time, amounts for 28.5 cases per 100 person-years. Baseline ALT elevation (OR = 14.368, P = 0.017)and HCV co-infection (OR = 3.009, P = 0.000) were risk factors for cART related hepatotoxicity, while greatly increased CD4+ T(CD4) cell count was protective against hepatotoxicity development (OR = 0.996, P = 0.000). Patients co-infected with HCV received NVP-based cART had the higher probability of hepatotoxicity than those without HCV co-infection (Log rank: Chi2 = 16.764, P = 0.000). 23 out of the 133 subjects (17.3%) with NVP related hepatotoxicity discontinued cART temporarily or shifted NVP to efavirenz. NVP related hepatotoxicity was common among ARV naive HIV infected subjects in our cohort. Baseline ALT elevation and HCV co-infection were associated statistically with the development of hepatotoxicity. Hepatotoxicity led to discontinuing cART temporarily or switching to other regimens in some subjects. It suggested that NVP should be used with caution in patients co-infected with HCV among whom anti-HCV therapy before cART initiation may contribute to minimizing the probability of NVP associated hepatotoxicity.

Bayesian Item Selection in Constrained Adaptive Testing Using Shadow Tests

ERIC Educational Resources Information Center

Veldkamp, Bernard P.

2010-01-01

Application of Bayesian item selection criteria in computerized adaptive testing might result in improvement of bias and MSE of the ability estimates. The question remains how to apply Bayesian item selection criteria in the context of constrained adaptive testing, where large numbers of specifications have to be taken into account in the item…

HBV genotypes and drug resistance mutations in antiretroviral treatment-naive and treatment-experienced HBV-HIV-coinfected patients.

PubMed

Archampong, Timothy Na; Boyce, Ceejay L; Lartey, Margaret; Sagoe, Kwamena W; Obo-Akwa, Adjoa; Kenu, Ernest; Blackard, Jason T; Kwara, Awewura

2017-01-01

The presence of HBV resistance mutations upon initiation or during antiretroviral therapy (ART) in HIV-coinfected patients is an important determinant of treatment response. The main objective of the study was to determine the prevalence of HBV resistance mutations in antiretroviral treatment-naive and treatment-experienced HBV-HIV-coinfected Ghanaian patients with detectable HBV viraemia. HBV-HIV-coinfected patients who were ART-naive or had received at least 9 months of lamivudine (3TC)-containing ART were enrolled in a cross-sectional study. Demographic and clinical data were collected and HBV DNA quantified. Partial HBV sequences were amplified by PCR and sequenced bi-directionally to obtain a 2.1-2.2 kb fragment for phylogenetic analysis of HBV genotypes and evaluation of drug resistance mutations. Of the 100 HBV-HIV-coinfected study patients, 75 were successfully PCR-amplified, and 63 were successfully sequenced. Of these 63 patients, 27 (42.9%) were ART-experienced and 58 (92.1%) had HBV genotype E. No resistance mutations were observed in the 36 ART-naive patients, while 21 (77.8%) of 27 treatment-experienced patients had resistance mutations. All patients with resistance mutations had no tenofovir in their regimens, and 80% of them had HIV RNA <40 copies/ml. The 3TC resistance mutations rtL180M and rtM204V were observed in 10 (47.6%) of the 21 patients, while 5 patients (23.8%) had rtV173L, rtL180M and rtM204V mutations. A high proportion of HBV-HIV-coinfected patients with detectable viraemia on 3TC-containing ART had resistance mutations despite good ART adherence as determined by HIV RNA suppression. This study emphasizes the need for dual therapy as part of a fully suppressive ART in all HBV-HIV-coinfected patients in Ghana.

A systematic review of Bayesian articles in psychology: The last 25 years.

PubMed

van de Schoot, Rens; Winter, Sonja D; Ryan, Oisín; Zondervan-Zwijnenburg, Mariëlle; Depaoli, Sarah

2017-06-01

Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statistics plays in psychology. This historical assessment allows us to identify trends and see how Bayesian methods have been integrated into psychological research in the context of different statistical frameworks (e.g., hypothesis testing, cognitive models, IRT, SEM, etc.). We also describe take-home messages and provide "big-picture" recommendations to the field as Bayesian statistics becomes more popular. Our review indicated that Bayesian statistics is used in a variety of contexts across subfields of psychology and related disciplines. There are many different reasons why one might choose to use Bayes (e.g., the use of priors, estimating otherwise intractable models, modeling uncertainty, etc.). We found in this review that the use of Bayes has increased and broadened in the sense that this methodology can be used in a flexible manner to tackle many different forms of questions. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Bayesian Hypothesis Testing

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

Andrews, Stephen A.; Sigeti, David E.

These are a set of slides about Bayesian hypothesis testing, where many hypotheses are tested. The conclusions are the following: The value of the Bayes factor obtained when using the median of the posterior marginal is almost the minimum value of the Bayes factor. The value of τ 2 which minimizes the Bayes factor is a reasonable choice for this parameter. This allows a likelihood ratio to be computed with is the least favorable to H 0.

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 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

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

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.

DR3 regulation of apoptosis of naive T-lymphocytes in children with acute infectious mononucleosis.

PubMed

Filatova, Elena Nikolaevna; Anisenkova, Elena Viktorovna; Presnyakova, Nataliya Borisovna; Utkin, Oleg Vladimirovich

2016-09-01

Acute infectious mononucleosis (AIM) is a widespread viral disease that mostly affects children. Development of AIM is accompanied by a change in the ratio of immune cells. This is provided by means of different biological processes including the regulation of apoptosis of naive T-cells. One of the potential regulators of apoptosis of T-lymphocytes is a death receptor 3 (DR3). We have studied the role of DR3 in the regulation of apoptosis of naive CD4 + (nTh) and CD8 + (nCTL) T-cells in healthy children and children with AIM. In healthy children as well as in children with AIM, the activation of DR3 is accompanied by inhibition of apoptosis of nTh. In healthy children, the stimulation of DR3 resulted in the increase in apoptosis of nCTL. On the contrary, in children with AIM, the level of apoptosis of nCTL decreased after DR3 activation, which is a positive contribution to the antiviral immune response. In children with AIM, nCTL are characterized by reduced level of apoptosis as compared with healthy children. These results indicate that DR3 can be involved in the reduction of sensitivity of nCTL to apoptosis in children with AIM.

IL-2 Enhances Gut Homing Potential of Human Naive Regulatory T Cells Early in Life.

PubMed

Hsu, Peter S; Lai, Catherine L; Hu, Mingjing; Santner-Nanan, Brigitte; Dahlstrom, Jane E; Lee, Cheng Hiang; Ajmal, Ayesha; Bullman, Amanda; Arbuckle, Susan; Al Saedi, Ahmed; Gacis, Lou; Nambiar, Reta; Williams, Andrew; Wong, Melanie; Campbell, Dianne E; Nanan, Ralph

2018-06-15

Recent evidence suggests early environmental factors are important for gut immune tolerance. Although the role of regulatory T (Treg) cells for gut immune homeostasis is well established, the development and tissue homing characteristics of Treg cells in children have not been studied in detail. In this article, we studied the development and homing characteristics of human peripheral blood Treg cell subsets and potential mechanisms inducing homing molecule expression in healthy children. We found contrasting patterns of circulating Treg cell gut and skin tropism, with abundant β7 integrin + Treg cells at birth and increasing cutaneous lymphocyte Ag (CLA + ) Treg cells later in life. β7 integrin + Treg cells were predominantly naive, suggesting acquisition of Treg cell gut tropism early in development. In vitro, IL-7 enhanced gut homing but reduced skin homing molecule expression in conventional T cells, whereas IL-2 induced a similar effect only in Treg cells. This effect was more pronounced in cord compared with adult blood. Our results suggest that early in life, naive Treg cells may be driven for gut tropism by their increased sensitivity to IL-2-induced β7 integrin upregulation, implicating a potential role of IL-2 in gut immune tolerance during this critical period of development. Copyright © 2018 by The American Association of Immunologists, Inc.

CytoBayesJ: software tools for Bayesian analysis of cytogenetic radiation dosimetry data.

PubMed

Ainsbury, Elizabeth A; Vinnikov, Volodymyr; Puig, Pedro; Maznyk, Nataliya; Rothkamm, Kai; Lloyd, David C

2013-08-30

A number of authors have suggested that a Bayesian approach may be most appropriate for analysis of cytogenetic radiation dosimetry data. In the Bayesian framework, probability of an event is described in terms of previous expectations and uncertainty. Previously existing, or prior, information is used in combination with experimental results to infer probabilities or the likelihood that a hypothesis is true. It has been shown that the Bayesian approach increases both the accuracy and quality assurance of radiation dose estimates. New software entitled CytoBayesJ has been developed with the aim of bringing Bayesian analysis to cytogenetic biodosimetry laboratory practice. CytoBayesJ takes a number of Bayesian or 'Bayesian like' methods that have been proposed in the literature and presents them to the user in the form of simple user-friendly tools, including testing for the most appropriate model for distribution of chromosome aberrations and calculations of posterior probability distributions. The individual tools are described in detail and relevant examples of the use of the methods and the corresponding CytoBayesJ software tools are given. In this way, the suitability of the Bayesian approach to biological radiation dosimetry is highlighted and its wider application encouraged by providing a user-friendly software interface and manual in English and Russian. Copyright © 2013 Elsevier B.V. All rights reserved.

A Discrete Choice Conjoint Experiment to Evaluate Parent Preferences for Treatment of Young, Medication Naive Children with ADHD

ERIC Educational Resources Information Center

Waschbusch, Daniel A.; Cunningham, Charles E.; Pelham, William E., Jr.; Rimas, Heather L.; Greiner, Andrew R.; Gnagy, Elizabeth M.; Waxmonsky, James; Fabiano, Gregory A.; Robb, Jessica A.; Burrows-MacLean, Lisa; Scime, Mindy; Hoffman, Martin T.

2011-01-01

The current study examined treatment preferences of 183 parents of young (average age = 5.8 years, SD = 0.6), medication naive children with ADHD. Preferences were evaluated using a discrete choice experiment in which parents made choices between different combinations of treatment characteristics, outcomes, and costs. Latent class analysis…

Bayesian Estimation and Inference Using Stochastic Electronics

PubMed Central

Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan; van Schaik, André

2016-01-01

In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326

Bayesian Estimation and Inference Using Stochastic Electronics.

PubMed

Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André

2016-01-01

In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.

Acute Neuropsychological Effects of Methylphenidate in Stimulant Drug-Naive Boys with ADHD II--Broader Executive and Non-Executive Domains

ERIC Educational Resources Information Center

Rhodes, Sinead M.; Coghill, David R.; Matthews, Keith

2006-01-01

Background: Accumulating evidence supports methylphenidate-induced enhancement of neuropsychological functioning in attention deficit hyperactivity disorder (ADHD). The present study was designed to investigate the acute effects of the psychostimulant drug, methylphenidate (MPH), on neuropsychological performance in stimulant naive boys with ADHD.…

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.

Applications of Bayesian Statistics to Problems in Gamma-Ray Bursts

NASA Technical Reports Server (NTRS)

Meegan, Charles A.

1997-01-01

This presentation will describe two applications of Bayesian statistics to Gamma Ray Bursts (GRBS). The first attempts to quantify the evidence for a cosmological versus galactic origin of GRBs using only the observations of the dipole and quadrupole moments of the angular distribution of bursts. The cosmological hypothesis predicts isotropy, while the galactic hypothesis is assumed to produce a uniform probability distribution over positive values for these moments. The observed isotropic distribution indicates that the Bayes factor for the cosmological hypothesis over the galactic hypothesis is about 300. Another application of Bayesian statistics is in the estimation of chance associations of optical counterparts with galaxies. The Bayesian approach is preferred to frequentist techniques here because the Bayesian approach easily accounts for galaxy mass distributions and because one can incorporate three disjoint hypotheses: (1) bursts come from galactic centers, (2) bursts come from galaxies in proportion to luminosity, and (3) bursts do not come from external galaxies. This technique was used in the analysis of the optical counterpart to GRB970228.

Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy

NASA Astrophysics Data System (ADS)

Sharma, Sanjib

2017-08-01

Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the last decade, we have also seen a steady increase in the number of papers that employ Monte Carlo based Bayesian analysis. New, efficient Monte Carlo based methods are continuously being developed and explored. In this review, we first explain the basics of Bayesian theory and discuss how to set up data analysis problems within this framework. Next, we provide an overview of various Monte Carlo based methods for performing Bayesian data analysis. Finally, we discuss advanced ideas that enable us to tackle complex problems and thus hold great promise for the future. We also distribute downloadable computer software (available at https://github.com/sanjibs/bmcmc/ ) that implements some of the algorithms and examples discussed here.

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.

Bayesian data analysis in observational comparative effectiveness research: rationale and examples.

PubMed

Olson, William H; Crivera, Concetta; Ma, Yi-Wen; Panish, Jessica; Mao, Lian; Lynch, Scott M

2013-11-01

Many comparative effectiveness research and patient-centered outcomes research studies will need to be observational for one or both of two reasons: first, randomized trials are expensive and time-consuming; and second, only observational studies can answer some research questions. It is generally recognized that there is a need to increase the scientific validity and efficiency of observational studies. Bayesian methods for the design and analysis of observational studies are scientifically valid and offer many advantages over frequentist methods, including, importantly, the ability to conduct comparative effectiveness research/patient-centered outcomes research more efficiently. Bayesian data analysis is being introduced into outcomes studies that we are conducting. Our purpose here is to describe our view of some of the advantages of Bayesian methods for observational studies and to illustrate both realized and potential advantages by describing studies we are conducting in which various Bayesian methods have been or could be implemented.

Background level of risk and the survival of predator-naive prey: can neophobia compensate for predator naivety in juvenile coral reef fishes?

PubMed

Ferrari, Maud C O; McCormick, Mark I; Meekan, Mark G; Chivers, Douglas P

2015-01-22

Neophobia--the generalized fear response to novel stimuli--provides the first potential strategy that predator-naive prey may use to survive initial predator encounters. This phenotype appears to be highly plastic and present in individuals experiencing high-risk environments, but rarer in those experiencing low-risk environments. Despite the appeal of this strategy as a 'solution' for prey naivety, we lack evidence that this strategy provides any fitness benefit to prey. Here, we compare the relative effect of environmental risk (high versus low) and predator-recognition training (predator-naive versus predator-experienced individuals) on the survival of juvenile fish in the wild. We found that juveniles raised in high-risk conditions survived better than those raised in low-risk conditions, providing the first empirical evidence that environmental risk, in the absence of any predator-specific information, affects the way naive prey survive in a novel environment. Both risk level and experience affected survival; however, the two factors did not interact, indicating that the information provided by both factors did not interfere or enhance each other. From a mechanistic viewpoint, this indicates that the combination of the two factors may increase the intensity, and hence efficacy, of prey evasion strategies, or that both factors provide qualitatively separate benefits that would result in an additive survival success.

Bayesian learning and the psychology of rule induction

PubMed Central

Endress, Ansgar D.

2014-01-01

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 spell out the underlying assumptions, and to confront them with the empirical results Frank and Tenenbaum (2011) propose to simulate, as well as with novel experiments. While rule-learning is arguably well suited to rational Bayesian approaches, I show that their models are neither psychologically plausible nor ideal observer models. Further, I show that their central assumption is unfounded: humans do not always preferentially learn more specific rules, but, at least in some situations, those rules that happen to be more salient. Even when granting the unsupported assumptions, I show that all of the experiments modeled by Frank and Tenenbaum (2011) either contradict their models, or have a large number of more plausible interpretations. I provide an alternative account of the experimental data based on simple psychological mechanisms, and show that this account both describes the data better, and is easier to falsify. I conclude that, despite the recent surge in Bayesian models of cognitive phenomena, psychological phenomena are best understood by developing and testing psychological theories rather than models that can be fit to virtually any data. PMID:23454791

Using Bayesian neural networks to classify forest scenes

NASA Astrophysics Data System (ADS)

Vehtari, Aki; Heikkonen, Jukka; Lampinen, Jouko; Juujarvi, Jouni

1998-10-01

We present results that compare the performance of Bayesian learning methods for neural networks on the task of classifying forest scenes into trees and background. Classification task is demanding due to the texture richness of the trees, occlusions of the forest scene objects and diverse lighting conditions under operation. This makes it difficult to determine which are optimal image features for the classification. A natural way to proceed is to extract many different types of potentially suitable features, and to evaluate their usefulness in later processing stages. One approach to cope with large number of features is to use Bayesian methods to control the model complexity. Bayesian learning uses a prior on model parameters, combines this with evidence from a training data, and the integrates over the resulting posterior to make predictions. With this method, we can use large networks and many features without fear of overfitting. For this classification task we compare two Bayesian learning methods for multi-layer perceptron (MLP) neural networks: (1) The evidence framework of MacKay uses a Gaussian approximation to the posterior weight distribution and maximizes with respect to hyperparameters. (2) In a Markov Chain Monte Carlo (MCMC) method due to Neal, the posterior distribution of the network parameters is numerically integrated using the MCMC method. As baseline classifiers for comparison we use (3) MLP early stop committee, (4) K-nearest-neighbor and (5) Classification And Regression Tree.

Win-Stay, Lose-Sample: a simple sequential algorithm for approximating Bayesian inference.

PubMed

Bonawitz, Elizabeth; Denison, Stephanie; Gopnik, Alison; Griffiths, Thomas L

2014-11-01

People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm "Win-Stay, Lose-Sample", inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian inference. We investigate the behavior of adults and preschoolers on two causal learning tasks to test whether people might use a similar algorithm. These studies use a "mini-microgenetic method", investigating how people sequentially update their beliefs as they encounter new evidence. Experiment 1 investigates a deterministic causal learning scenario and Experiments 2 and 3 examine how people make inferences in a stochastic scenario. The behavior of adults and preschoolers in these experiments is consistent with our Bayesian version of the WSLS principle. This algorithm provides both a practical method for performing Bayesian inference and a new way to understand people's judgments. Copyright © 2014 Elsevier Inc. All rights reserved.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

PubMed

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

Bayesian Inference on Proportional Elections

PubMed Central

Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

2015-01-01

Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259

Bayesian inference on proportional elections.

PubMed

Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

2015-01-01

Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.

Caveats for the spatial arrangement method: Comment on Hout, Goldinger, and Ferguson (2013).

PubMed

Verheyen, Steven; Voorspoels, Wouter; Vanpaemel, Wolf; Storms, Gert

2016-03-01

The gold standard among proximity data collection methods for multidimensional scaling is the (dis)similarity rating of pairwise presented stimuli. A drawback of the pairwise method is its lengthy duration, which may cause participants to change their strategy over time, become fatigued, or disengage altogether. Hout, Goldinger, and Ferguson (2013) recently made a case for the Spatial Arrangement Method (SpAM) as an alternative to the pairwise method, arguing that it is faster and more engaging. SpAM invites participants to directly arrange stimuli on a computer screen such that the interstimuli distances are proportional to psychological proximity. Based on a reanalysis of the Hout et al. (2013), data we identify three caveats for SpAM. An investigation of the distributional characteristics of the SpAM proximity data reveals that the spatial nature of SpAM imposes structure on the data, invoking a bias against featural representations. Individual-differences scaling of the SpAM proximity data reveals that the two-dimensional nature of SpAM allows individuals to only communicate two dimensions of variation among stimuli properly, invoking a bias against high-dimensional scaling representations. Monte Carlo simulations indicate that in order to obtain reliable estimates of the group average, SpAM requires more individuals to be tested. We conclude with an overview of considerations that can inform the choice between SpAM and the pairwise method and offer suggestions on how to overcome their respective limitations. (c) 2016 APA, all rights reserved).

A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study.

PubMed

Kaplan, David; Chen, Jianshen

2012-07-01

A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for three methods of implementation: propensity score stratification, weighting, and optimal full matching. Three simulation studies and one case study are presented to elaborate the proposed two-step Bayesian propensity score approach. Results of the simulation studies reveal that greater precision in the propensity score equation yields better recovery of the frequentist-based treatment effect. A slight advantage is shown for the Bayesian approach in small samples. Results also reveal that greater precision around the wrong treatment effect can lead to seriously distorted results. However, greater precision around the correct treatment effect parameter yields quite good results, with slight improvement seen with greater precision in the propensity score equation. A comparison of coverage rates for the conventional frequentist approach and proposed Bayesian approach is also provided. The case study reveals that credible intervals are wider than frequentist confidence intervals when priors are non-informative.

MRI/US fusion-guided prostate biopsy allows for equivalent cancer detection with significantly fewer needle cores in biopsy-naive men

PubMed Central

Yarlagadda, Vidhush K.; Lai, Win Shun; Gordetsky, Jennifer B.; Porter, Kristin K.; Nix, Jeffrey W.; Thomas, John V.; Rais-Bahrami, Soroush

2018-01-01

PURPOSE We aimed to investigate the efficiency and cancer detection of magnetic resonance imaging (MRI)/ultrasonography (US) fusion-guided prostate biopsy in a cohort of biopsy-naive men compared with standard-of-care systematic extended sextant transrectal ultrasonography (TRUS)-guided biopsy. METHODS From 2014 to 2016, 72 biopsy-naive men referred for initial prostate cancer evaluation who underwent MRI of the prostate were prospectively evaluated. Retrospective review was performed on 69 patients with lesions suspicious for malignancy who underwent MRI/US fusion-guided biopsy in addition to systematic extended sextant biopsy. Biometric, imaging, and pathology data from both the MRI-targeted biopsies and systematic biopsies were analyzed and compared. RESULTS There were no significant differences in overall prostate cancer detection when comparing MRI-targeted biopsies to standard systematic biopsies (P = 0.39). Furthermore, there were no significant differences in the distribution of severity of cancers based on grade groups in cases with cancer detection (P = 0.68). However, significantly fewer needle cores were taken during the MRI/US fusion-guided biopsy compared with systematic biopsy (63% less cores sampled, P < 0.001) CONCLUSION In biopsy-naive men, MRI/US fusion-guided prostate biopsy offers equal prostate cancer detection compared with systematic TRUS-guided biopsy with significantly fewer tissue cores using the targeted technique. This approach can potentially reduce morbidity in the future if used instead of systematic biopsy without sacrificing the ability to detect prostate cancer, particularly in cases with higher grade disease. PMID:29770762

Active and Interactive Discovery of Goal Selection Knowledge

DTIC Science & Technology

2011-01-01

Generator retrieves the goal ct.g of the most similar case ct and outputs it to the Goal Manager. 5.3 Retention and Maintenance: Active Learning Figure...pp. 202-206). Seattle, WA: AAAI Press. Hu, R., Delaney, S.J., & Mac Namee, B. (2010). EGAL: Exploration guided active learning for TCBR. Proceedings...Sculley, D. (2007). Online active learning methods for fast label- efficient spam filtering. In Proceedings of the Fourth Conference on Email and Anti

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE PAGES

Oyen, Diane; Anderson, Blake; Sentz, Kari; ...

2017-09-15

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

Order priors for Bayesian network discovery with an application to malware phylogeny

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

Oyen, Diane; Anderson, Blake; Sentz, Kari

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

NK cells and their receptors in naive and rituximab-treated patients with anti-MAG polyneuropathy.

PubMed

Benedetti, Luana; Facco, Monica; Franciotta, Diego; Dalla Torre, Chiara; Campagnolo, Marta; Lucchetta, Marta; Boscaro, Elisa; Ermani, Mario; Del Sette, Massimo; Berno, Tamara; Candiotto, Laura; Zambello, Renato; Briani, Chiara

2013-08-15

Natural killer (NK) cells can bridge innate and acquired immunity, and play a role in autoimmunity. A few studies evaluated the distribution of NK cells and the expression of their receptors in chronic immune-mediated demyelinating polyneuropathies. We investigated NK cell distribution and NK cell receptor expression in 20 naïve patients with anti-MAG polyneuropathy (MAG-PN). Using flow cytometry, we analysed NK cells and a series of NK cell receptors in the peripheral blood of patients with MAG-PN, and, as controls, in patients with chronic inflammatory demyelinating peripheral polyradiculoneuropathy (CIDP) and in healthy subjects. Six MAG-PN patients were also tested after rituximab treatment. At baseline the percentage of NK cells did not differ among the groups. KIR2DL2 receptor expression in MAG-PN patients was higher, andCD94/NKG2A receptor expression in both MAG-PN and CIDP patients was lower than in healthy controls. These abnormalities did not correlate with any clinical or demographic variable. No modification was found after rituximab therapy. The data suggest that MAG-PN shows abnormalities in NK cell receptors that characterise other autoimmune diseases, and cannot help in differential diagnosis with CIDP. The impairment of the relevant CD94/NKG2A inhibitory pathway, which might play a central role in the development and perpetuation of MAG-PN, warrants further functional investigations. Copyright © 2013 Elsevier B.V. All rights reserved.

Software Health Management with Bayesian Networks

NASA Technical Reports Server (NTRS)

Mengshoel, Ole; Schumann, JOhann

2011-01-01

Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.

Bayesian Inference in the Modern Design of Experiments

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2008-01-01

This paper provides an elementary tutorial overview of Bayesian inference and its potential for application in aerospace experimentation in general and wind tunnel testing in particular. Bayes Theorem is reviewed and examples are provided to illustrate how it can be applied to objectively revise prior knowledge by incorporating insights subsequently obtained from additional observations, resulting in new (posterior) knowledge that combines information from both sources. A logical merger of Bayesian methods and certain aspects of Response Surface Modeling is explored. Specific applications to wind tunnel testing, computational code validation, and instrumentation calibration are discussed.

Bayesian linkage and segregation analysis: factoring the problem.

PubMed

Matthysse, S

2000-01-01

Complex segregation analysis and linkage methods are mathematical techniques for the genetic dissection of complex diseases. They are used to delineate complex modes of familial transmission and to localize putative disease susceptibility loci to specific chromosomal locations. The computational problem of Bayesian linkage and segregation analysis is one of integration in high-dimensional spaces. In this paper, three available techniques for Bayesian linkage and segregation analysis are discussed: Markov Chain Monte Carlo (MCMC), importance sampling, and exact calculation. The contribution of each to the overall integration will be explicitly discussed.

Bayesian dynamic mediation analysis.

PubMed

Huang, Jing; Yuan, Ying

2017-12-01

Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Mismatched anti-predator behavioral responses in predator-naïve larval anurans.

PubMed

Albecker, Molly; Vance-Chalcraft, Heather D

2015-01-01

Organisms are adept at altering behaviors to balance the tradeoff between foraging and predation risk in spatially and temporally shifting predator environments. In order to optimize this tradeoff, prey need to be able to display an appropriate response based on degree of predation risk. To be most beneficial in the earliest life stages in which many prey are vulnerable to predation, innate anti-predator responses should scale to match the risk imposed by predators until learned anti-predator responses can occur. We conducted an experiment that examined whether tadpoles with no previous exposure to predators (i.e., predator-naive) exhibit innate antipredator behavioral responses (e.g., via refuge use and spatial avoidance) that match the actual risk posed by each predator. Using 7 treatments (6 free-roaming, lethal predators plus no-predator control), we determined the predation rates of each predator on Lithobates sphenocephalus tadpoles. We recorded behavioral observations on an additional 7 nonlethal treatments (6 caged predators plus no-predator control). Tadpoles exhibited innate responses to fish predators, but not non-fish predators, even though two non-fish predators (newt and crayfish) consumed the most tadpoles. Due to a mismatch between innate response and predator consumption, tadpoles may be vulnerable to greater rates of predation at the earliest life stages before learning can occur. Thus, naïve tadpoles in nature may be at a high risk to predation in the presence of a novel predator until learned anti-predator responses provide additional defenses to the surviving tadpoles.

Mismatched anti-predator behavioral responses in predator-naïve larval anurans

PubMed Central

Vance-Chalcraft, Heather D.

2015-01-01

Organisms are adept at altering behaviors to balance the tradeoff between foraging and predation risk in spatially and temporally shifting predator environments. In order to optimize this tradeoff, prey need to be able to display an appropriate response based on degree of predation risk. To be most beneficial in the earliest life stages in which many prey are vulnerable to predation, innate anti-predator responses should scale to match the risk imposed by predators until learned anti-predator responses can occur. We conducted an experiment that examined whether tadpoles with no previous exposure to predators (i.e., predator-naive) exhibit innate antipredator behavioral responses (e.g., via refuge use and spatial avoidance) that match the actual risk posed by each predator. Using 7 treatments (6 free-roaming, lethal predators plus no-predator control), we determined the predation rates of each predator on Lithobates sphenocephalus tadpoles. We recorded behavioral observations on an additional 7 nonlethal treatments (6 caged predators plus no-predator control). Tadpoles exhibited innate responses to fish predators, but not non-fish predators, even though two non-fish predators (newt and crayfish) consumed the most tadpoles. Due to a mismatch between innate response and predator consumption, tadpoles may be vulnerable to greater rates of predation at the earliest life stages before learning can occur. Thus, naïve tadpoles in nature may be at a high risk to predation in the presence of a novel predator until learned anti-predator responses provide additional defenses to the surviving tadpoles. PMID:26664805

Naive T cells are dispensable for memory CD4+ T cell homeostasis in progressive simian immunodeficiency virus infection

PubMed Central

Okoye, Afam A.; Rohankhedkar, Mukta; Abana, Chike; Pattenn, Audrie; Reyes, Matthew; Pexton, Christopher; Lum, Richard; Sylwester, Andrew; Planer, Shannon L.; Legasse, Alfred; Park, Byung S.; Piatak, Michael; Lifson, Jeffrey D.; Axthelm, Michael K.

2012-01-01

The development of AIDS in chronic HIV/simian immunodeficiency virus (SIV) infection has been closely linked to progressive failure of CD4+ memory T cell (TM) homeostasis. CD4+ naive T cells (TN) also decline in these infections, but their contribution to disease progression is less clear. We assessed the role of CD4+ TN in SIV pathogenesis using rhesus macaques (RMs) selectively and permanently depleted of CD4+ TN before SIV infection. CD4+ TN-depleted and CD4+ TN-repleted RMs were created by subjecting juvenile RMs to thymectomy versus sham surgery, respectively, followed by total CD4+ T cell depletion and recovery from this depletion. Although thymectomized and sham-treated RMs manifested comparable CD4+ TM recovery, only sham-treated RMs reconstituted CD4+ TN. CD4+ TN-depleted RMs responded to SIVmac239 infection with markedly attenuated SIV-specific CD4+ T cell responses, delayed SIVenv-specific Ab responses, and reduced SIV-specific CD8+ T cell responses. However, CD4+ TN-depleted and -repleted groups showed similar levels of SIV replication. Moreover, CD4+ TN deficiency had no significant effect on CD4+ TM homeostasis (either on or off anti-retroviral therapy) or disease progression. These data demonstrate that the CD4+ TN compartment is dispensable for CD4+ TM homeostasis in progressive SIV infection, and they confirm that CD4+ TM comprise a homeostatically independent compartment that is intrinsically capable of self-renewal. PMID:22451717

Bayesian Methods and Universal Darwinism

NASA Astrophysics Data System (ADS)

Campbell, John

2009-12-01

Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent Champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a `copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the Operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that Systems will evolve to states of highest entropy subject to the constraints of scientific law. This principle may be inverted to provide illumination as to the nature of scientific law. Our best cosmological theories suggest the universe contained much less complexity during the period shortly after the Big Bang than it does at present. The scientific subject matter of atomic physics, chemistry, biology and the social sciences has been created since that time. An explanation is proposed for the existence of this subject matter as due to the evolution of constraints in the form of adaptations imposed on Maximum Entropy. It is argued these adaptations were discovered and instantiated through the Operations of a succession of Darwinian processes.

Search Parameter Optimization for Discrete, Bayesian, and Continuous Search Algorithms

DTIC Science & Technology

2017-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CONTINUOUS SEARCH ALGORITHMS by...to 09-22-2017 4. TITLE AND SUBTITLE SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CON- TINUOUS SEARCH ALGORITHMS 5. FUNDING NUMBERS 6...simple search and rescue acts to prosecuting aerial/surface/submersible targets on mission. This research looks at varying the known discrete and

Bayesian Nonlinear Assimilation of Eulerian and Lagrangian Coastal Flow Data

DTIC Science & Technology

2015-09-30

Lagrangian Coastal Flow Data Dr. Pierre F.J. Lermusiaux Department of Mechanical Engineering Center for Ocean Science and Engineering Massachusetts...Develop and apply theory, schemes and computational systems for rigorous Bayesian nonlinear assimilation of Eulerian and Lagrangian coastal flow data...coastal ocean fields, both in Eulerian and Lagrangian forms. - Further develop and implement our GMM-DO schemes for robust Bayesian nonlinear estimation

Sequential Inverse Problems Bayesian Principles and the Logistic Map Example

NASA Astrophysics Data System (ADS)

Duan, Lian; Farmer, Chris L.; Moroz, Irene M.

2010-09-01

Bayesian statistics provides a general framework for solving inverse problems, but is not without interpretation and implementation problems. This paper discusses difficulties arising from the fact that forward models are always in error to some extent. Using a simple example based on the one-dimensional logistic map, we argue that, when implementation problems are minimal, the Bayesian framework is quite adequate. In this paper the Bayesian Filter is shown to be able to recover excellent state estimates in the perfect model scenario (PMS) and to distinguish the PMS from the imperfect model scenario (IMS). Through a quantitative comparison of the way in which the observations are assimilated in both the PMS and the IMS scenarios, we suggest that one can, sometimes, measure the degree of imperfection.

Bayesian ionospheric multi-instrument 3D tomography

NASA Astrophysics Data System (ADS)

Norberg, Johannes; Vierinen, Juha; Roininen, Lassi

2017-04-01

The tomographic reconstruction of ionospheric electron densities is an inverse problem that cannot be solved without relatively strong regularising additional information. % Especially the vertical electron density profile is determined predominantly by the regularisation. % %Often utilised regularisations in ionospheric tomography include smoothness constraints and iterative methods with initial ionospheric models. % Despite its crucial role, the regularisation is often hidden in the algorithm as a numerical procedure without physical understanding. % % The Bayesian methodology provides an interpretative approach for the problem, as the regularisation can be given in a physically meaningful and quantifiable prior probability distribution. % The prior distribution can be based on ionospheric physics, other available ionospheric measurements and their statistics. % Updating the prior with measurements results as the posterior distribution that carries all the available information combined. % From the posterior distribution, the most probable state of the ionosphere can then be solved with the corresponding probability intervals. % Altogether, the Bayesian methodology provides understanding on how strong the given regularisation is, what is the information gained with the measurements and how reliable the final result is. % In addition, the combination of different measurements and temporal development can be taken into account in a very intuitive way. However, a direct implementation of the Bayesian approach requires inversion of large covariance matrices resulting in computational infeasibility. % In the presented method, Gaussian Markov random fields are used to form a sparse matrix approximations for the covariances. % The approach makes the problem computationally feasible while retaining the probabilistic and physical interpretation. Here, the Bayesian method with Gaussian Markov random fields is applied for ionospheric 3D tomography over Northern Europe

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

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…

On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood

ERIC Educational Resources Information Center

Karabatsos, George

2017-01-01

This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon…

Cross-view gait recognition using joint Bayesian

NASA Astrophysics Data System (ADS)

Li, Chao; Sun, Shouqian; Chen, Xiaoyu; Min, Xin

2017-07-01

Human gait, as a soft biometric, helps to recognize people by walking. To further improve the recognition performance under cross-view condition, we propose Joint Bayesian to model the view variance. We evaluated our prosed method with the largest population (OULP) dataset which makes our result reliable in a statically way. As a result, we confirmed our proposed method significantly outperformed state-of-the-art approaches for both identification and verification tasks. Finally, sensitivity analysis on the number of training subjects was conducted, we find Joint Bayesian could achieve competitive results even with a small subset of training subjects (100 subjects). For further comparison, experimental results, learning models, and test codes are available.

RasGRP1 regulates antigen-induced developmental programming by naive CD8 T cells.

PubMed

Priatel, John J; Chen, Xiaoxi; Huang, Yu-Hsuan; Chow, Michael T; Zenewicz, Lauren A; Coughlin, Jason J; Shen, Hao; Stone, James C; Tan, Rusung; Teh, Hung Sia

2010-01-15

Ag encounter by naive CD8 T cells initiates a developmental program consisting of cellular proliferation, changes in gene expression, and the formation of effector and memory T cells. The strength and duration of TCR signaling are known to be important parameters regulating the differentiation of naive CD8 T cells, although the molecular signals arbitrating these processes remain poorly defined. The Ras-guanyl nucleotide exchange factor RasGRP1 has been shown to transduce TCR-mediated signals critically required for the maturation of developing thymocytes. To elucidate the role of RasGRP1 in CD8 T cell differentiation, in vitro and in vivo experiments were performed with 2C TCR transgenic CD8 T cells lacking RasGRP1. In this study, we report that RasGRP1 regulates the threshold of T cell activation and Ag-induced expansion, at least in part, through the regulation of IL-2 production. Moreover, RasGRP1(-/-) 2C CD8 T cells exhibit an anergic phenotype in response to cognate Ag stimulation that is partially reversible upon the addition of exogenous IL-2. By contrast, the capacity of IL-2/IL-2R interactions to mediate Ras activation and CD8 T cell expansion and differentiation appears to be largely RasGRP1-independent. Collectively, our results demonstrate that RasGRP1 plays a selective role in T cell signaling, controlling the initiation and duration of CD8 T cell immune responses.

Large-scale sequence and structural comparisons of human naive and antigen-experienced antibody repertoires.

PubMed

DeKosky, Brandon J; Lungu, Oana I; Park, Daechan; Johnson, Erik L; Charab, Wissam; Chrysostomou, Constantine; Kuroda, Daisuke; Ellington, Andrew D; Ippolito, Gregory C; Gray, Jeffrey J; Georgiou, George

2016-05-10

Elucidating how antigen exposure and selection shape the human antibody repertoire is fundamental to our understanding of B-cell immunity. We sequenced the paired heavy- and light-chain variable regions (VH and VL, respectively) from large populations of single B cells combined with computational modeling of antibody structures to evaluate sequence and structural features of human antibody repertoires at unprecedented depth. Analysis of a dataset comprising 55,000 antibody clusters from CD19(+)CD20(+)CD27(-) IgM-naive B cells, >120,000 antibody clusters from CD19(+)CD20(+)CD27(+) antigen-experienced B cells, and >2,000 RosettaAntibody-predicted structural models across three healthy donors led to a number of key findings: (i) VH and VL gene sequences pair in a combinatorial fashion without detectable pairing restrictions at the population level; (ii) certain VH:VL gene pairs were significantly enriched or depleted in the antigen-experienced repertoire relative to the naive repertoire; (iii) antigen selection increased antibody paratope net charge and solvent-accessible surface area; and (iv) public heavy-chain third complementarity-determining region (CDR-H3) antibodies in the antigen-experienced repertoire showed signs of convergent paired light-chain genetic signatures, including shared light-chain third complementarity-determining region (CDR-L3) amino acid sequences and/or Vκ,λ-Jκ,λ genes. The data reported here address several longstanding questions regarding antibody repertoire selection and development and provide a benchmark for future repertoire-scale analyses of antibody responses to vaccination and disease.

Bayesian estimation of differential transcript usage from RNA-seq data.

PubMed

Papastamoulis, Panagiotis; Rattray, Magnus

2017-11-27

Next generation sequencing allows the identification of genes consisting of differentially expressed transcripts, a term which usually refers to changes in the overall expression level. A specific type of differential expression is differential transcript usage (DTU) and targets changes in the relative within gene expression of a transcript. The contribution of this paper is to: (a) extend the use of cjBitSeq to the DTU context, a previously introduced Bayesian model which is originally designed for identifying changes in overall expression levels and (b) propose a Bayesian version of DRIMSeq, a frequentist model for inferring DTU. cjBitSeq is a read based model and performs fully Bayesian inference by MCMC sampling on the space of latent state of each transcript per gene. BayesDRIMSeq is a count based model and estimates the Bayes Factor of a DTU model against a null model using Laplace's approximation. The proposed models are benchmarked against the existing ones using a recent independent simulation study as well as a real RNA-seq dataset. Our results suggest that the Bayesian methods exhibit similar performance with DRIMSeq in terms of precision/recall but offer better calibration of False Discovery Rate.

Characterizing the Nash equilibria of three-player Bayesian quantum games

NASA Astrophysics Data System (ADS)

Solmeyer, Neal; Balu, Radhakrishnan

2017-05-01

Quantum games with incomplete information can be studied within a Bayesian framework. We analyze games quantized within the EWL framework [Eisert, Wilkens, and Lewenstein, Phys Rev. Lett. 83, 3077 (1999)]. We solve for the Nash equilibria of a variety of two-player quantum games and compare the results to the solutions of the corresponding classical games. We then analyze Bayesian games where there is uncertainty about the player types in two-player conflicting interest games. The solutions to the Bayesian games are found to have a phase diagram-like structure where different equilibria exist in different parameter regions, depending both on the amount of uncertainty and the degree of entanglement. We find that in games where a Pareto-optimal solution is not a Nash equilibrium, it is possible for the quantized game to have an advantage over the classical version. In addition, we analyze the behavior of the solutions as the strategy choices approach an unrestricted operation. We find that some games have a continuum of solutions, bounded by the solutions of a simpler restricted game. A deeper understanding of Bayesian quantum game theory could lead to novel quantum applications in a multi-agent setting.

Bayesian Inference on the Radio-quietness of Gamma-ray Pulsars

NASA Astrophysics Data System (ADS)

Yu, Hoi-Fung; Hui, Chung Yue; Kong, Albert K. H.; Takata, Jumpei

2018-04-01

For the first time we demonstrate using a robust Bayesian approach to analyze the populations of radio-quiet (RQ) and radio-loud (RL) gamma-ray pulsars. We quantify their differences and obtain their distributions of the radio-cone opening half-angle δ and the magnetic inclination angle α by Bayesian inference. In contrast to the conventional frequentist point estimations that might be non-representative when the distribution is highly skewed or multi-modal, which is often the case when data points are scarce, Bayesian statistics displays the complete posterior distribution that the uncertainties can be readily obtained regardless of the skewness and modality. We found that the spin period, the magnetic field strength at the light cylinder, the spin-down power, the gamma-ray-to-X-ray flux ratio, and the spectral curvature significance of the two groups of pulsars exhibit significant differences at the 99% level. Using Bayesian inference, we are able to infer the values and uncertainties of δ and α from the distribution of RQ and RL pulsars. We found that δ is between 10° and 35° and the distribution of α is skewed toward large values.

Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.

PubMed

Kutschireiter, Anna; Surace, Simone Carlo; Sprekeler, Henning; Pfister, Jean-Pascal

2017-08-18

The robust estimation of dynamical hidden features, such as the position of prey, based on sensory inputs is one of the hallmarks of perception. This dynamical estimation can be rigorously formulated by nonlinear Bayesian filtering theory. Recent experimental and behavioral studies have shown that animals' performance in many tasks is consistent with such a Bayesian statistical interpretation. However, it is presently unclear how a nonlinear Bayesian filter can be efficiently implemented in a network of neurons that satisfies some minimum constraints of biological plausibility. Here, we propose the Neural Particle Filter (NPF), a sampling-based nonlinear Bayesian filter, which does not rely on importance weights. We show that this filter can be interpreted as the neuronal dynamics of a recurrently connected rate-based neural network receiving feed-forward input from sensory neurons. Further, it captures properties of temporal and multi-sensory integration that are crucial for perception, and it allows for online parameter learning with a maximum likelihood approach. The NPF holds the promise to avoid the 'curse of dimensionality', and we demonstrate numerically its capability to outperform weighted particle filters in higher dimensions and when the number of particles is limited.

BUMPER: the Bayesian User-friendly Model for Palaeo-Environmental Reconstruction

NASA Astrophysics Data System (ADS)

Holden, Phil; Birks, John; Brooks, Steve; Bush, Mark; Hwang, Grace; Matthews-Bird, Frazer; Valencia, Bryan; van Woesik, Robert

2017-04-01

We describe the Bayesian User-friendly Model for Palaeo-Environmental Reconstruction (BUMPER), a Bayesian transfer function for inferring past climate and other environmental variables from microfossil assemblages. The principal motivation for a Bayesian approach is that the palaeoenvironment is treated probabilistically, and can be updated as additional data become available. Bayesian approaches therefore provide a reconstruction-specific quantification of the uncertainty in the data and in the model parameters. BUMPER is fully self-calibrating, straightforward to apply, and computationally fast, requiring 2 seconds to build a 100-taxon model from a 100-site training-set on a standard personal computer. We apply the model's probabilistic framework to generate thousands of artificial training-sets under ideal assumptions. We then use these to demonstrate both the general applicability of the model and the sensitivity of reconstructions to the characteristics of the training-set, considering assemblage richness, taxon tolerances, and the number of training sites. We demonstrate general applicability to real data, considering three different organism types (chironomids, diatoms, pollen) and different reconstructed variables. In all of these applications an identically configured model is used, the only change being the input files that provide the training-set environment and taxon-count data.

Development of dynamic Bayesian models for web application test management

NASA Astrophysics Data System (ADS)

Azarnova, T. V.; Polukhin, P. V.; Bondarenko, Yu V.; Kashirina, I. L.

2018-03-01

The mathematical apparatus of dynamic Bayesian networks is an effective and technically proven tool that can be used to model complex stochastic dynamic processes. According to the results of the research, mathematical models and methods of dynamic Bayesian networks provide a high coverage of stochastic tasks associated with error testing in multiuser software products operated in a dynamically changing environment. Formalized representation of the discrete test process as a dynamic Bayesian model allows us to organize the logical connection between individual test assets for multiple time slices. This approach gives an opportunity to present testing as a discrete process with set structural components responsible for the generation of test assets. Dynamic Bayesian network-based models allow us to combine in one management area individual units and testing components with different functionalities and a direct influence on each other in the process of comprehensive testing of various groups of computer bugs. The application of the proposed models provides an opportunity to use a consistent approach to formalize test principles and procedures, methods used to treat situational error signs, and methods used to produce analytical conclusions based on test results.

Using Bayesian Adaptive Trial Designs for Comparative Effectiveness Research: A Virtual Trial Execution.

PubMed

Luce, Bryan R; Connor, Jason T; Broglio, Kristine R; Mullins, C Daniel; Ishak, K Jack; Saunders, Elijah; Davis, Barry R

2016-09-20

Bayesian and adaptive clinical trial designs offer the potential for more efficient processes that result in lower sample sizes and shorter trial durations than traditional designs. To explore the use and potential benefits of Bayesian adaptive clinical trial designs in comparative effectiveness research. Virtual execution of ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) as if it had been done according to a Bayesian adaptive trial design. Comparative effectiveness trial of antihypertensive medications. Patient data sampled from the more than 42 000 patients enrolled in ALLHAT with publicly available data. Number of patients randomly assigned between groups, trial duration, observed numbers of events, and overall trial results and conclusions. The Bayesian adaptive approach and original design yielded similar overall trial conclusions. The Bayesian adaptive trial randomly assigned more patients to the better-performing group and would probably have ended slightly earlier. This virtual trial execution required limited resampling of ALLHAT patients for inclusion in RE-ADAPT (REsearch in ADAptive methods for Pragmatic Trials). Involvement of a data monitoring committee and other trial logistics were not considered. In a comparative effectiveness research trial, Bayesian adaptive trial designs are a feasible approach and potentially generate earlier results and allocate more patients to better-performing groups. National Heart, Lung, and Blood Institute.

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

The Galileo Bias: A Naive Conceptual Belief That Influences People's Perceptions and Performance in a Ball-Dropping Task

ERIC Educational Resources Information Center

Oberle, Crystal D.; McBeath, Michael K.; Madigan, Sean C.; Sugar, Thomas G.

2005-01-01

This research introduces a new naive physics belief, the Galileo bias, whereby people ignore air resistance and falsely believe that all objects fall at the same rate. Survey results revealed that this bias is held by many and is surprisingly strongest for those with formal physics instruction. In 2 experiments, 98 participants dropped ball pairs…

Probabilistic Damage Characterization Using the Computationally-Efficient Bayesian Approach

NASA Technical Reports Server (NTRS)

Warner, James E.; Hochhalter, Jacob D.

2016-01-01

This work presents a computationally-ecient approach for damage determination that quanti es uncertainty in the provided diagnosis. Given strain sensor data that are polluted with measurement errors, Bayesian inference is used to estimate the location, size, and orientation of damage. This approach uses Bayes' Theorem to combine any prior knowledge an analyst may have about the nature of the damage with information provided implicitly by the strain sensor data to form a posterior probability distribution over possible damage states. The unknown damage parameters are then estimated based on samples drawn numerically from this distribution using a Markov Chain Monte Carlo (MCMC) sampling algorithm. Several modi cations are made to the traditional Bayesian inference approach to provide signi cant computational speedup. First, an ecient surrogate model is constructed using sparse grid interpolation to replace a costly nite element model that must otherwise be evaluated for each sample drawn with MCMC. Next, the standard Bayesian posterior distribution is modi ed using a weighted likelihood formulation, which is shown to improve the convergence of the sampling process. Finally, a robust MCMC algorithm, Delayed Rejection Adaptive Metropolis (DRAM), is adopted to sample the probability distribution more eciently. Numerical examples demonstrate that the proposed framework e ectively provides damage estimates with uncertainty quanti cation and can yield orders of magnitude speedup over standard Bayesian approaches.

Word Learning as Bayesian Inference

ERIC Educational Resources Information Center

Xu, Fei; Tenenbaum, Joshua B.

2007-01-01

The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with…

A Bayesian pick-the-winner design in a randomized phase II clinical trial.

PubMed

Chen, Dung-Tsa; Huang, Po-Yu; Lin, Hui-Yi; Chiappori, Alberto A; Gabrilovich, Dmitry I; Haura, Eric B; Antonia, Scott J; Gray, Jhanelle E

2017-10-24

Many phase II clinical trials evaluate unique experimental drugs/combinations through multi-arm design to expedite the screening process (early termination of ineffective drugs) and to identify the most effective drug (pick the winner) to warrant a phase III trial. Various statistical approaches have been developed for the pick-the-winner design but have been criticized for lack of objective comparison among the drug agents. We developed a Bayesian pick-the-winner design by integrating a Bayesian posterior probability with Simon two-stage design in a randomized two-arm clinical trial. The Bayesian posterior probability, as the rule to pick the winner, is defined as probability of the response rate in one arm higher than in the other arm. The posterior probability aims to determine the winner when both arms pass the second stage of the Simon two-stage design. When both arms are competitive (i.e., both passing the second stage), the Bayesian posterior probability performs better to correctly identify the winner compared with the Fisher exact test in the simulation study. In comparison to a standard two-arm randomized design, the Bayesian pick-the-winner design has a higher power to determine a clear winner. In application to two studies, the approach is able to perform statistical comparison of two treatment arms and provides a winner probability (Bayesian posterior probability) to statistically justify the winning arm. We developed an integrated design that utilizes Bayesian posterior probability, Simon two-stage design, and randomization into a unique setting. It gives objective comparisons between the arms to determine the winner.

Abnormal default-mode network homogeneity and its correlations with personality in drug-naive somatization disorder at rest.

PubMed

Wei, Shubao; Su, Qinji; Jiang, Muliang; Liu, Feng; Yao, Dapeng; Dai, Yi; Long, Liling; Song, Yan; Yu, Miaoyu; Zhang, Zhikun; Zhao, Jingping; Guo, Wenbin

2016-03-15

While the default-mode network (DMN) appears to play a crucial role in patients suffering from somatization disorder (SD), the abnormalities of the network homogeneity (NH) of the DMN in SD patients have been poorly explored. The aim of this study is to examine DMN NH using an NH approach in patients suffering from SD at rest and determine its correlations with personality as measured by the Eysenck Personality Questionnaire (EPQ). A total of 25 drug-naive patients with SD and 28 sex-, age-, and education-matched healthy controls underwent functional magnetic resonance imaging scans at rest. The data were analyzed by an automated NH method. Patients showed increased NH in the left superior frontal gyrus and decreased NH in the bilateral precuneus. Moreover, a significantly negative correlation was observed between the NH values in the bilateral precuneus and the EPQ--Neuroticism scores. The present study should be considered preliminary due to a lenient, uncorrected threshold of p<0.01. The results suggest that abnormal DMN NH exists in drug-naive SD and further highlight the importance of the DMN in the pathophysiology of SD. Copyright © 2015 Elsevier B.V. All rights reserved.

Encoding dependence in Bayesian causal networks

USDA-ARS?s Scientific Manuscript database

Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...

Visual cortex activation in late-onset, Braille naive blind individuals: an fMRI study during semantic and phonological tasks with heard words.

PubMed

Burton, Harold; McLaren, Donald G

2006-01-09

Visual cortex activity in the blind has been shown in Braille literate people, which raise the question of whether Braille literacy influences cross-modal reorganization. We used fMRI to examine visual cortex activation during semantic and phonological tasks with auditory presentation of words in two late-onset blind individuals who lacked Braille literacy. Multiple visual cortical regions were activated in the Braille naive individuals. Positive BOLD responses were noted in lower tier visuotopic (e.g., V1, V2, VP, and V3) and several higher tier visual areas (e.g., V4v, V8, and BA 37). Activity was more extensive and cross-correlation magnitudes were greater during the semantic compared to the phonological task. These results with Braille naive individuals plausibly suggest that visual deprivation alone induces visual cortex reorganization. Cross-modal reorganization of lower tier visual areas may be recruited by developing skills in attending to selected non-visual inputs (e.g., Braille literacy, enhanced auditory skills). Such learning might strengthen remote connections with multisensory cortical areas. Of necessity, the Braille naive participants must attend to auditory stimulation for language. We hypothesize that learning to attend to non-visual inputs probably strengthens the remaining active synapses following visual deprivation, and thereby, increases cross-modal activation of lower tier visual areas when performing highly demanding non-visual tasks of which reading Braille is just one example.

Addition of docetaxel and/or zoledronic acid to standard of care for hormone-naive prostate cancer: a cost-effectiveness analysis.

PubMed

Zhang, Pengfei; Wen, Feng; Fu, Ping; Yang, Yu; Li, Qiu

2017-07-31

The effectiveness of the addition of docetaxel and/or zoledronic acid to the standard of care (SOC) for hormone-naive prostate cancer has been evaluated in the STAMPEDE trial. The object of the present analysis was to evaluate the cost-effectiveness of these treatment options in the treatment of advanced hormone-naive prostate cancer in China. A cost-effectiveness analysis using a Markov model was carried out from the Chinese societal perspective. The efficacy data were obtained from the STAMPEDE trial and health utilities were derived from previous studies. Transition probabilities were calculated based on the survival in each group. The primary endpoint in the analysis was the incremental cost-effectiveness ratio (ICER), and model uncertainties were explored by 1-way sensitivity analysis and probabilistic sensitivity analysis. SOC alone generated an effectiveness of 2.65 quality-adjusted life years (QALYs) at a lifetime cost of $20,969.23. At a cost of $25,001.34, SOC plus zoledronic acid was associated with 2.69 QALYs, resulting in an ICER of $100,802.75/QALY compared with SOC alone. SOC plus docetaxel gained an effectiveness of 2.85 QALYs at a cost of $28,764.66, while the effectiveness and cost data in the SOC plus zoledronic acid/docetaxel group were 2.78 QALYs and $32,640.95. Based on the results of the analysis, SOC plus zoledronic acid, SOC plus docetaxel, and SOC plus zoledronic acid/docetaxel are unlikely to be cost-effective options in patients with advanced hormone-naive prostate cancer compared with SOC alone.

Professional Stereotypes of Interprofessional Education Naive Pharmacy and Nursing Students.

PubMed

Thurston, Maria Miller; Chesson, Melissa M; Harris, Elaine C; Ryan, Gina J

2017-06-01

Objective. To assess and compare interprofessional education (IPE) naive pharmacy and nursing student stereotypes prior to completion of an IPE activity. Methods. Three hundred and twenty-three pharmacy students and 275 nursing students at Mercer University completed the Student Stereotypes Rating Questionnaire. Responses from pharmacy and nursing students were compared, and responses from different level learners within the same profession also were compared. Results. Three hundred and fifty-six (59.5%) students completed the survey. Pharmacy students viewed pharmacists more favorably than nursing students viewed pharmacists for all attributes except the ability to work independently. Additionally, nursing students viewed nurses less favorably than pharmacy students viewed nurses for academic ability and practical skills. There was some variability in stereotypes between professional years. Conclusion. This study confirms the existence of professional stereotypes, although overall student perceptions of their own profession and the other were generally positive.

Professional Stereotypes of Interprofessional Education Naive Pharmacy and Nursing Students

PubMed Central

Thurston, Maria Miller; Harris, Elaine C.; Ryan, Gina J.

2017-01-01

Objective. To assess and compare interprofessional education (IPE) naive pharmacy and nursing student stereotypes prior to completion of an IPE activity. Methods. Three hundred and twenty-three pharmacy students and 275 nursing students at Mercer University completed the Student Stereotypes Rating Questionnaire. Responses from pharmacy and nursing students were compared, and responses from different level learners within the same profession also were compared. Results. Three hundred and fifty-six (59.5%) students completed the survey. Pharmacy students viewed pharmacists more favorably than nursing students viewed pharmacists for all attributes except the ability to work independently. Additionally, nursing students viewed nurses less favorably than pharmacy students viewed nurses for academic ability and practical skills. There was some variability in stereotypes between professional years. Conclusion. This study confirms the existence of professional stereotypes, although overall student perceptions of their own profession and the other were generally positive. PMID:28720912

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data

PubMed Central

Shah, Abhik; Woolf, Peter

2009-01-01

Summary In this paper, we introduce pebl, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing. PMID:20161541

Bayesian population receptive field modelling.

PubMed

Zeidman, Peter; Silson, Edward Harry; Schwarzkopf, Dietrich Samuel; Baker, Chris Ian; Penny, Will

2017-09-08

We introduce a probabilistic (Bayesian) framework and associated software toolbox for mapping population receptive fields (pRFs) based on fMRI data. This generic approach is intended to work with stimuli of any dimension and is demonstrated and validated in the context of 2D retinotopic mapping. The framework enables the experimenter to specify generative (encoding) models of fMRI timeseries, in which experimental stimuli enter a pRF model of neural activity, which in turns drives a nonlinear model of neurovascular coupling and Blood Oxygenation Level Dependent (BOLD) response. The neuronal and haemodynamic parameters are estimated together on a voxel-by-voxel or region-of-interest basis using a Bayesian estimation algorithm (variational Laplace). This offers several novel contributions to receptive field modelling. The variance/covariance of parameters are estimated, enabling receptive fields to be plotted while properly representing uncertainty about pRF size and location. Variability in the haemodynamic response across the brain is accounted for. Furthermore, the framework introduces formal hypothesis testing to pRF analysis, enabling competing models to be evaluated based on their log model evidence (approximated by the variational free energy), which represents the optimal tradeoff between accuracy and complexity. Using simulations and empirical data, we found that parameters typically used to represent pRF size and neuronal scaling are strongly correlated, which is taken into account by the Bayesian methods we describe when making inferences. We used the framework to compare the evidence for six variants of pRF model using 7 T functional MRI data and we found a circular Difference of Gaussians (DoG) model to be the best explanation for our data overall. We hope this framework will prove useful for mapping stimulus spaces with any number of dimensions onto the anatomy of the brain. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Confidence of compliance: a Bayesian approach for percentile standards.

PubMed

McBride, G B; Ellis, J C

2001-04-01

Rules for assessing compliance with percentile standards commonly limit the number of exceedances permitted in a batch of samples taken over a defined assessment period. Such rules are commonly developed using classical statistical methods. Results from alternative Bayesian methods are presented (using beta-distributed prior information and a binomial likelihood), resulting in "confidence of compliance" graphs. These allow simple reading of the consumer's risk and the supplier's risks for any proposed rule. The influence of the prior assumptions required by the Bayesian technique on the confidence results is demonstrated, using two reference priors (uniform and Jeffreys') and also using optimistic and pessimistic user-defined priors. All four give less pessimistic results than does the classical technique, because interpreting classical results as "confidence of compliance" actually invokes a Bayesian approach with an extreme prior distribution. Jeffreys' prior is shown to be the most generally appropriate choice of prior distribution. Cost savings can be expected using rules based on this approach.

Technical Topic 3.2.2.d Bayesian and Non-Parametric Statistics: Integration of Neural Networks with Bayesian Networks for Data Fusion and Predictive Modeling