A hydroclimatological approach to predicting regional landslide probability using Landlab

NASA Astrophysics Data System (ADS)

Strauch, Ronda; Istanbulluoglu, Erkan; Nudurupati, Sai Siddhartha; Bandaragoda, Christina; Gasparini, Nicole M.; Tucker, Gregory E.

2018-02-01

We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km2. The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at mid-elevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

Topological and Orthomodular Modeling of Context in Behavioral Science

NASA Astrophysics Data System (ADS)

Narens, Louis

2017-02-01

Two non-boolean methods are discussed for modeling context in behavioral data and theory. The first is based on intuitionistic logic, which is similar to classical logic except that not every event has a complement. Its probability theory is also similar to classical probability theory except that the definition of probability function needs to be generalized to unions of events instead of applying only to unions of disjoint events. The generalization is needed, because intuitionistic event spaces may not contain enough disjoint events for the classical definition to be effective. The second method develops a version of quantum logic for its underlying probability theory. It differs from Hilbert space logic used in quantum mechanics as a foundation for quantum probability theory in variety of ways. John von Neumann and others have commented about the lack of a relative frequency approach and a rational foundation for this probability theory. This article argues that its version of quantum probability theory does not have such issues. The method based on intuitionistic logic is useful for modeling cognitive interpretations that vary with context, for example, the mood of the decision maker, the context produced by the influence of other items in a choice experiment, etc. The method based on this article's quantum logic is useful for modeling probabilities across contexts, for example, how probabilities of events from different experiments are related.

Modeling marine oily wastewater treatment by a probabilistic agent-based approach.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Ye, Xudong

2018-02-01

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization. Copyright © 2017 Elsevier Ltd. All rights reserved.

Utilizing Adjoint-Based Error Estimates for Surrogate Models to Accurately Predict Probabilities of Events

DOE PAGES

Butler, Troy; Wildey, Timothy

2018-01-01

In thist study, we develop a procedure to utilize error estimates for samples of a surrogate model to compute robust upper and lower bounds on estimates of probabilities of events. We show that these error estimates can also be used in an adaptive algorithm to simultaneously reduce the computational cost and increase the accuracy in estimating probabilities of events using computationally expensive high-fidelity models. Specifically, we introduce the notion of reliability of a sample of a surrogate model, and we prove that utilizing the surrogate model for the reliable samples and the high-fidelity model for the unreliable samples gives preciselymore » the same estimate of the probability of the output event as would be obtained by evaluation of the original model for each sample. The adaptive algorithm uses the additional evaluations of the high-fidelity model for the unreliable samples to locally improve the surrogate model near the limit state, which significantly reduces the number of high-fidelity model evaluations as the limit state is resolved. Numerical results based on a recently developed adjoint-based approach for estimating the error in samples of a surrogate are provided to demonstrate (1) the robustness of the bounds on the probability of an event, and (2) that the adaptive enhancement algorithm provides a more accurate estimate of the probability of the QoI event than standard response surface approximation methods at a lower computational cost.« less

Utilizing Adjoint-Based Error Estimates for Surrogate Models to Accurately Predict Probabilities of Events

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

Butler, Troy; Wildey, Timothy

In thist study, we develop a procedure to utilize error estimates for samples of a surrogate model to compute robust upper and lower bounds on estimates of probabilities of events. We show that these error estimates can also be used in an adaptive algorithm to simultaneously reduce the computational cost and increase the accuracy in estimating probabilities of events using computationally expensive high-fidelity models. Specifically, we introduce the notion of reliability of a sample of a surrogate model, and we prove that utilizing the surrogate model for the reliable samples and the high-fidelity model for the unreliable samples gives preciselymore » the same estimate of the probability of the output event as would be obtained by evaluation of the original model for each sample. The adaptive algorithm uses the additional evaluations of the high-fidelity model for the unreliable samples to locally improve the surrogate model near the limit state, which significantly reduces the number of high-fidelity model evaluations as the limit state is resolved. Numerical results based on a recently developed adjoint-based approach for estimating the error in samples of a surrogate are provided to demonstrate (1) the robustness of the bounds on the probability of an event, and (2) that the adaptive enhancement algorithm provides a more accurate estimate of the probability of the QoI event than standard response surface approximation methods at a lower computational cost.« less

The creation and evaluation of a model predicting the probability of conception in seasonal-calving, pasture-based dairy cows.

PubMed

Fenlon, Caroline; O'Grady, Luke; Doherty, Michael L; Dunnion, John; Shalloo, Laurence; Butler, Stephen T

2017-07-01

Reproductive performance in pasture-based production systems has a fundamentally important effect on economic efficiency. The individual factors affecting the probability of submission and conception are multifaceted and have been extensively researched. The present study analyzed some of these factors in relation to service-level probability of conception in seasonal-calving pasture-based dairy cows to develop a predictive model of conception. Data relating to 2,966 services from 737 cows on 2 research farms were used for model development and data from 9 commercial dairy farms were used for model testing, comprising 4,212 services from 1,471 cows. The data spanned a 15-yr period and originated from seasonal-calving pasture-based dairy herds in Ireland. The calving season for the study herds extended from January to June, with peak calving in February and March. A base mixed-effects logistic regression model was created using a stepwise model-building strategy and incorporated parity, days in milk, interservice interval, calving difficulty, and predicted transmitting abilities for calving interval and milk production traits. To attempt to further improve the predictive capability of the model, the addition of effects that were not statistically significant was considered, resulting in a final model composed of the base model with the inclusion of BCS at service. The models' predictions were evaluated using discrimination to measure their ability to correctly classify positive and negative cases. Precision, recall, F-score, and area under the receiver operating characteristic curve (AUC) were calculated. Calibration tests measured the accuracy of the predicted probabilities. These included tests of overall goodness-of-fit, bias, and calibration error. Both models performed better than using the population average probability of conception. Neither of the models showed high levels of discrimination (base model AUC 0.61, final model AUC 0.62), possibly because of the narrow central range of conception rates in the study herds. The final model was found to reliably predict the probability of conception without bias when evaluated against the full external data set, with a mean absolute calibration error of 2.4%. The chosen model could be used to support a farmer's decision-making and in stochastic simulation of fertility in seasonal-calving pasture-based dairy cows. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Sample Size Determination for Rasch Model Tests

ERIC Educational Resources Information Center

Draxler, Clemens

2010-01-01

This paper is concerned with supplementing statistical tests for the Rasch model so that additionally to the probability of the error of the first kind (Type I probability) the probability of the error of the second kind (Type II probability) can be controlled at a predetermined level by basing the test on the appropriate number of observations.…

Alternative probability theories for cognitive psychology.

PubMed

Narens, Louis

2014-01-01

Various proposals for generalizing event spaces for probability functions have been put forth in the mathematical, scientific, and philosophic literatures. In cognitive psychology such generalizations are used for explaining puzzling results in decision theory and for modeling the influence of context effects. This commentary discusses proposals for generalizing probability theory to event spaces that are not necessarily boolean algebras. Two prominent examples are quantum probability theory, which is based on the set of closed subspaces of a Hilbert space, and topological probability theory, which is based on the set of open sets of a topology. Both have been applied to a variety of cognitive situations. This commentary focuses on how event space properties can influence probability concepts and impact cognitive modeling. Copyright © 2013 Cognitive Science Society, Inc.

Tornado damage risk assessment

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

Reinhold, T.A.; Ellingwood, B.

1982-09-01

Several proposed models were evaluated for predicting tornado wind speed probabilities at nuclear plant sites as part of a program to develop statistical data on tornadoes needed for probability-based load combination analysis. A unified model was developed which synthesized the desired aspects of tornado occurrence and damage potential. The sensitivity of wind speed probability estimates to various tornado modeling assumptions are examined, and the probability distributions of tornado wind speed that are needed for load combination studies are presented.

Bootstrap imputation with a disease probability model minimized bias from misclassification due to administrative database codes.

PubMed

van Walraven, Carl

2017-04-01

Diagnostic codes used in administrative databases cause bias due to misclassification of patient disease status. It is unclear which methods minimize this bias. Serum creatinine measures were used to determine severe renal failure status in 50,074 hospitalized patients. The true prevalence of severe renal failure and its association with covariates were measured. These were compared to results for which renal failure status was determined using surrogate measures including the following: (1) diagnostic codes; (2) categorization of probability estimates of renal failure determined from a previously validated model; or (3) bootstrap methods imputation of disease status using model-derived probability estimates. Bias in estimates of severe renal failure prevalence and its association with covariates were minimal when bootstrap methods were used to impute renal failure status from model-based probability estimates. In contrast, biases were extensive when renal failure status was determined using codes or methods in which model-based condition probability was categorized. Bias due to misclassification from inaccurate diagnostic codes can be minimized using bootstrap methods to impute condition status using multivariable model-derived probability estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs

PubMed Central

2017-01-01

Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package. PMID:29107980

A simplified model for the assessment of the impact probability of fragments.

PubMed

Gubinelli, Gianfilippo; Zanelli, Severino; Cozzani, Valerio

2004-12-31

A model was developed for the assessment of fragment impact probability on a target vessel, following the collapse and fragmentation of a primary vessel due to internal pressure. The model provides the probability of impact of a fragment with defined shape, mass and initial velocity on a target of a known shape and at a given position with respect to the source point. The model is based on the ballistic analysis of the fragment trajectory and on the determination of impact probabilities by the analysis of initial direction of fragment flight. The model was validated using available literature data.

Using effort information with change-in-ratio data for population estimation

USGS Publications Warehouse

Udevitz, Mark S.; Pollock, Kenneth H.

1995-01-01

Most change-in-ratio (CIR) methods for estimating fish and wildlife population sizes have been based only on assumptions about how encounter probabilities vary among population subclasses. When information on sampling effort is available, it is also possible to derive CIR estimators based on assumptions about how encounter probabilities vary over time. This paper presents a generalization of previous CIR models that allows explicit consideration of a range of assumptions about the variation of encounter probabilities among subclasses and over time. Explicit estimators are derived under this model for specific sets of assumptions about the encounter probabilities. Numerical methods are presented for obtaining estimators under the full range of possible assumptions. Likelihood ratio tests for these assumptions are described. Emphasis is on obtaining estimators based on assumptions about variation of encounter probabilities over time.

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.…

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

Tom Elicson; Bentley Harwood; Jim Bouchard

Over a 12 month period, a fire PRA was developed for a DOE facility using the NUREG/CR-6850 EPRI/NRC fire PRA methodology. The fire PRA modeling included calculation of fire severity factors (SFs) and fire non-suppression probabilities (PNS) for each safe shutdown (SSD) component considered in the fire PRA model. The SFs were developed by performing detailed fire modeling through a combination of CFAST fire zone model calculations and Latin Hypercube Sampling (LHS). Component damage times and automatic fire suppression system actuation times calculated in the CFAST LHS analyses were then input to a time-dependent model of fire non-suppression probability. Themore » fire non-suppression probability model is based on the modeling approach outlined in NUREG/CR-6850 and is supplemented with plant specific data. This paper presents the methodology used in the DOE facility fire PRA for modeling fire-induced SSD component failures and includes discussions of modeling techniques for: • Development of time-dependent fire heat release rate profiles (required as input to CFAST), • Calculation of fire severity factors based on CFAST detailed fire modeling, and • Calculation of fire non-suppression probabilities.« less

Interpretation of the results of statistical measurements. [search for basic probability model

NASA Technical Reports Server (NTRS)

Olshevskiy, V. V.

1973-01-01

For random processes, the calculated probability characteristic, and the measured statistical estimate are used in a quality functional, which defines the difference between the two functions. Based on the assumption that the statistical measurement procedure is organized so that the parameters for a selected model are optimized, it is shown that the interpretation of experimental research is a search for a basic probability model.

A More Pedagogically Sound Treatment of Beer's Law: A Derivation Based on a Corpuscular-Probability Model

NASA Astrophysics Data System (ADS)

Bare, William D.

2000-07-01

An argument is presented which suggests that the commonly seen calculus-based derivations of Beer's law may not be adequately useful to students and may in fact contribute to widely held misconceptions about the interaction of light with absorbing samples. For this reason, an alternative derivation of Beer's law based on a corpuscular model and the laws of probability is presented. Unlike many previously reported derivations, that presented here does not require the use of calculus, nor does it require the assumption of absorption properties in an infinitesimally thin film. The corpuscular-probability model and its accompanying derivation of Beer's law are believed to comprise a more pedagogically effective presentation than those presented previously.

Lost in folding space? Comparing four variants of the thermodynamic model for RNA secondary structure prediction.

PubMed

Janssen, Stefan; Schudoma, Christian; Steger, Gerhard; Giegerich, Robert

2011-11-03

Many bioinformatics tools for RNA secondary structure analysis are based on a thermodynamic model of RNA folding. They predict a single, "optimal" structure by free energy minimization, they enumerate near-optimal structures, they compute base pair probabilities and dot plots, representative structures of different abstract shapes, or Boltzmann probabilities of structures and shapes. Although all programs refer to the same physical model, they implement it with considerable variation for different tasks, and little is known about the effects of heuristic assumptions and model simplifications used by the programs on the outcome of the analysis. We extract four different models of the thermodynamic folding space which underlie the programs RNAFOLD, RNASHAPES, and RNASUBOPT. Their differences lie within the details of the energy model and the granularity of the folding space. We implement probabilistic shape analysis for all models, and introduce the shape probability shift as a robust measure of model similarity. Using four data sets derived from experimentally solved structures, we provide a quantitative evaluation of the model differences. We find that search space granularity affects the computed shape probabilities less than the over- or underapproximation of free energy by a simplified energy model. Still, the approximations perform similar enough to implementations of the full model to justify their continued use in settings where computational constraints call for simpler algorithms. On the side, we observe that the rarely used level 2 shapes, which predict the complete arrangement of helices, multiloops, internal loops and bulges, include the "true" shape in a rather small number of predicted high probability shapes. This calls for an investigation of new strategies to extract high probability members from the (very large) level 2 shape space of an RNA sequence. We provide implementations of all four models, written in a declarative style that makes them easy to be modified. Based on our study, future work on thermodynamic RNA folding may make a choice of model based on our empirical data. It can take our implementations as a starting point for further program development.

Transitional probability-based model for HPV clearance in HIV-1-positive adolescent females.

PubMed

Kravchenko, Julia; Akushevich, Igor; Sudenga, Staci L; Wilson, Craig M; Levitan, Emily B; Shrestha, Sadeep

2012-01-01

HIV-1-positive patients clear the human papillomavirus (HPV) infection less frequently than HIV-1-negative. Datasets for estimating HPV clearance probability often have irregular measurements of HPV status and risk factors. A new transitional probability-based model for estimation of probability of HPV clearance was developed to fully incorporate information on HIV-1-related clinical data, such as CD4 counts, HIV-1 viral load (VL), highly active antiretroviral therapy (HAART), and risk factors (measured quarterly), and HPV infection status (measured at 6-month intervals). Data from 266 HIV-1-positive and 134 at-risk HIV-1-negative adolescent females from the Reaching for Excellence in Adolescent Care and Health (REACH) cohort were used in this study. First, the associations were evaluated using the Cox proportional hazard model, and the variables that demonstrated significant effects on HPV clearance were included in transitional probability models. The new model established the efficacy of CD4 cell counts as a main clearance predictor for all type-specific HPV phylogenetic groups. The 3-month probability of HPV clearance in HIV-1-infected patients significantly increased with increasing CD4 counts for HPV16/16-like (p<0.001), HPV18/18-like (p<0.001), HPV56/56-like (p = 0.05), and low-risk HPV (p<0.001) phylogenetic groups, with the lowest probability found for HPV16/16-like infections (21.60±1.81% at CD4 level 200 cells/mm(3), p<0.05; and 28.03±1.47% at CD4 level 500 cells/mm(3)). HIV-1 VL was a significant predictor for clearance of low-risk HPV infections (p<0.05). HAART (with protease inhibitor) was significant predictor of probability of HPV16 clearance (p<0.05). HPV16/16-like and HPV18/18-like groups showed heterogeneity (p<0.05) in terms of how CD4 counts, HIV VL, and HAART affected probability of clearance of each HPV infection. This new model predicts the 3-month probability of HPV infection clearance based on CD4 cell counts and other HIV-1-related clinical measurements.

Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling

PubMed Central

Boos, Moritz; Seer, Caroline; Lange, Florian; Kopp, Bruno

2016-01-01

Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision. PMID:27303323

The Effect of Simulation-Based Learning on Prospective Teachers' Inference Skills in Teaching Probability

ERIC Educational Resources Information Center

Koparan, Timur; Yilmaz, Gül Kaleli

2015-01-01

The effect of simulation-based probability teaching on the prospective teachers' inference skills has been examined with this research. In line with this purpose, it has been aimed to examine the design, implementation and efficiency of a learning environment for experimental probability. Activities were built on modeling, simulation and the…

Computer-aided mathematical analysis of probability of intercept for ground-based communication intercept system

NASA Astrophysics Data System (ADS)

Park, Sang Chul

1989-09-01

We develop a mathematical analysis model to calculate the probability of intercept (POI) for the ground-based communication intercept (COMINT) system. The POI is a measure of the effectiveness of the intercept system. We define the POI as the product of the probability of detection and the probability of coincidence. The probability of detection is a measure of the receiver's capability to detect a signal in the presence of noise. The probability of coincidence is the probability that an intercept system is available, actively listening in the proper frequency band, in the right direction and at the same time that the signal is received. We investigate the behavior of the POI with respect to the observation time, the separation distance, antenna elevations, the frequency of the signal, and the receiver bandwidths. We observe that the coincidence characteristic between the receiver scanning parameters and the signal parameters is the key factor to determine the time to obtain a given POI. This model can be used to find the optimal parameter combination to maximize the POI in a given scenario. We expand this model to a multiple system. This analysis is conducted on a personal computer to provide the portability. The model is also flexible and can be easily implemented under different situations.

Warship Combat System Selection Methodology Based on Discrete Event Simulation

DTIC Science & Technology

2010-09-01

Platform (from Spanish) PD Damage Probability xiv PHit Hit Probability PKill Kill Probability RSM Response Surface Model SAM Surface-Air Missile...such a large target allows an assumption that the probability of a hit ( PHit ) is one. This structure can be considered as a bridge; therefore, the

Uncertainty plus Prior Equals Rational Bias: An Intuitive Bayesian Probability Weighting Function

ERIC Educational Resources Information Center

Fennell, John; Baddeley, Roland

2012-01-01

Empirical research has shown that when making choices based on probabilistic options, people behave as if they overestimate small probabilities, underestimate large probabilities, and treat positive and negative outcomes differently. These distortions have been modeled using a nonlinear probability weighting function, which is found in several…

Site occupancy models with heterogeneous detection probabilities

USGS Publications Warehouse

Royle, J. Andrew

2006-01-01

Models for estimating the probability of occurrence of a species in the presence of imperfect detection are important in many ecological disciplines. In these ?site occupancy? models, the possibility of heterogeneity in detection probabilities among sites must be considered because variation in abundance (and other factors) among sampled sites induces variation in detection probability (p). In this article, I develop occurrence probability models that allow for heterogeneous detection probabilities by considering several common classes of mixture distributions for p. For any mixing distribution, the likelihood has the general form of a zero-inflated binomial mixture for which inference based upon integrated likelihood is straightforward. A recent paper by Link (2003, Biometrics 59, 1123?1130) demonstrates that in closed population models used for estimating population size, different classes of mixture distributions are indistinguishable from data, yet can produce very different inferences about population size. I demonstrate that this problem can also arise in models for estimating site occupancy in the presence of heterogeneous detection probabilities. The implications of this are discussed in the context of an application to avian survey data and the development of animal monitoring programs.

Improving effectiveness of systematic conservation planning with density data.

PubMed

Veloz, Samuel; Salas, Leonardo; Altman, Bob; Alexander, John; Jongsomjit, Dennis; Elliott, Nathan; Ballard, Grant

2015-08-01

Systematic conservation planning aims to design networks of protected areas that meet conservation goals across large landscapes. The optimal design of these conservation networks is most frequently based on the modeled habitat suitability or probability of occurrence of species, despite evidence that model predictions may not be highly correlated with species density. We hypothesized that conservation networks designed using species density distributions more efficiently conserve populations of all species considered than networks designed using probability of occurrence models. To test this hypothesis, we used the Zonation conservation prioritization algorithm to evaluate conservation network designs based on probability of occurrence versus density models for 26 land bird species in the U.S. Pacific Northwest. We assessed the efficacy of each conservation network based on predicted species densities and predicted species diversity. High-density model Zonation rankings protected more individuals per species when networks protected the highest priority 10-40% of the landscape. Compared with density-based models, the occurrence-based models protected more individuals in the lowest 50% priority areas of the landscape. The 2 approaches conserved species diversity in similar ways: predicted diversity was higher in higher priority locations in both conservation networks. We conclude that both density and probability of occurrence models can be useful for setting conservation priorities but that density-based models are best suited for identifying the highest priority areas. Developing methods to aggregate species count data from unrelated monitoring efforts and making these data widely available through ecoinformatics portals such as the Avian Knowledge Network will enable species count data to be more widely incorporated into systematic conservation planning efforts. © 2015, Society for Conservation Biology.

Construction and identification of a D-Vine model applied to the probability distribution of modal parameters in structural dynamics

NASA Astrophysics Data System (ADS)

Dubreuil, S.; Salaün, M.; Rodriguez, E.; Petitjean, F.

2018-01-01

This study investigates the construction and identification of the probability distribution of random modal parameters (natural frequencies and effective parameters) in structural dynamics. As these parameters present various types of dependence structures, the retained approach is based on pair copula construction (PCC). A literature review leads us to choose a D-Vine model for the construction of modal parameters probability distributions. Identification of this model is based on likelihood maximization which makes it sensitive to the dimension of the distribution, namely the number of considered modes in our context. To this respect, a mode selection preprocessing step is proposed. It allows the selection of the relevant random modes for a given transfer function. The second point, addressed in this study, concerns the choice of the D-Vine model. Indeed, D-Vine model is not uniquely defined. Two strategies are proposed and compared. The first one is based on the context of the study whereas the second one is purely based on statistical considerations. Finally, the proposed approaches are numerically studied and compared with respect to their capabilities, first in the identification of the probability distribution of random modal parameters and second in the estimation of the 99 % quantiles of some transfer functions.

Integrating statistical and process-based models to produce probabilistic landslide hazard at regional scale

NASA Astrophysics Data System (ADS)

Strauch, R. L.; Istanbulluoglu, E.

2017-12-01

We develop a landslide hazard modeling approach that integrates a data-driven statistical model and a probabilistic process-based shallow landslide model for mapping probability of landslide initiation, transport, and deposition at regional scales. The empirical model integrates the influence of seven site attribute (SA) classes: elevation, slope, curvature, aspect, land use-land cover, lithology, and topographic wetness index, on over 1,600 observed landslides using a frequency ratio (FR) approach. A susceptibility index is calculated by adding FRs for each SA on a grid-cell basis. Using landslide observations we relate susceptibility index to an empirically-derived probability of landslide impact. This probability is combined with results from a physically-based model to produce an integrated probabilistic map. Slope was key in landslide initiation while deposition was linked to lithology and elevation. Vegetation transition from forest to alpine vegetation and barren land cover with lower root cohesion leads to higher frequency of initiation. Aspect effects are likely linked to differences in root cohesion and moisture controlled by solar insulation and snow. We demonstrate the model in the North Cascades of Washington, USA and identify locations of high and low probability of landslide impacts that can be used by land managers in their design, planning, and maintenance.

Studying the effects of fuel treatment based on burn probability on a boreal forest landscape.

PubMed

Liu, Zhihua; Yang, Jian; He, Hong S

2013-01-30

Fuel treatment is assumed to be a primary tactic to mitigate intense and damaging wildfires. However, how to place treatment units across a landscape and assess its effectiveness is difficult for landscape-scale fuel management planning. In this study, we used a spatially explicit simulation model (LANDIS) to conduct wildfire risk assessments and optimize the placement of fuel treatments at the landscape scale. We first calculated a baseline burn probability map from empirical data (fuel, topography, weather, and fire ignition and size data) to assess fire risk. We then prioritized landscape-scale fuel treatment based on maps of burn probability and fuel loads (calculated from the interactions among tree composition, stand age, and disturbance history), and compared their effects on reducing fire risk. The burn probability map described the likelihood of burning on a given location; the fuel load map described the probability that a high fuel load will accumulate on a given location. Fuel treatment based on the burn probability map specified that stands with high burn probability be treated first, while fuel treatment based on the fuel load map specified that stands with high fuel loads be treated first. Our results indicated that fuel treatment based on burn probability greatly reduced the burned area and number of fires of different intensities. Fuel treatment based on burn probability also produced more dispersed and smaller high-risk fire patches and therefore can improve efficiency of subsequent fire suppression. The strength of our approach is that more model components (e.g., succession, fuel, and harvest) can be linked into LANDIS to map the spatially explicit wildfire risk and its dynamics to fuel management, vegetation dynamics, and harvesting. Copyright © 2012 Elsevier Ltd. All rights reserved.

Evaluating the risk of industrial espionage

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

Bott, T.F.

1998-12-31

A methodology for estimating the relative probabilities of different compromise paths for protected information by insider and visitor intelligence collectors has been developed based on an event-tree analysis of the intelligence collection operation. The analyst identifies target information and ultimate users who might attempt to gain that information. The analyst then uses an event tree to develop a set of compromise paths. Probability models are developed for each of the compromise paths that user parameters based on expert judgment or historical data on security violations. The resulting probability estimates indicate the relative likelihood of different compromise paths and provide anmore » input for security resource allocation. Application of the methodology is demonstrated using a national security example. A set of compromise paths and probability models specifically addressing this example espionage problem are developed. The probability models for hard-copy information compromise paths are quantified as an illustration of the results using parametric values representative of historical data available in secure facilities, supplemented where necessary by expert judgment.« less

Computing rates of Markov models of voltage-gated ion channels by inverting partial differential equations governing the probability density functions of the conducting and non-conducting states.

PubMed

Tveito, Aslak; Lines, Glenn T; Edwards, Andrew G; McCulloch, Andrew

2016-07-01

Markov models are ubiquitously used to represent the function of single ion channels. However, solving the inverse problem to construct a Markov model of single channel dynamics from bilayer or patch-clamp recordings remains challenging, particularly for channels involving complex gating processes. Methods for solving the inverse problem are generally based on data from voltage clamp measurements. Here, we describe an alternative approach to this problem based on measurements of voltage traces. The voltage traces define probability density functions of the functional states of an ion channel. These probability density functions can also be computed by solving a deterministic system of partial differential equations. The inversion is based on tuning the rates of the Markov models used in the deterministic system of partial differential equations such that the solution mimics the properties of the probability density function gathered from (pseudo) experimental data as well as possible. The optimization is done by defining a cost function to measure the difference between the deterministic solution and the solution based on experimental data. By evoking the properties of this function, it is possible to infer whether the rates of the Markov model are identifiable by our method. We present applications to Markov model well-known from the literature. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys.

PubMed

Murn, Campbell; Holloway, Graham J

2016-10-01

Species occurring at low density can be difficult to detect and if not properly accounted for, imperfect detection will lead to inaccurate estimates of occupancy. Understanding sources of variation in detection probability and how they can be managed is a key part of monitoring. We used sightings data of a low-density and elusive raptor (white-headed vulture Trigonoceps occipitalis ) in areas of known occupancy (breeding territories) in a likelihood-based modelling approach to calculate detection probability and the factors affecting it. Because occupancy was known a priori to be 100%, we fixed the model occupancy parameter to 1.0 and focused on identifying sources of variation in detection probability. Using detection histories from 359 territory visits, we assessed nine covariates in 29 candidate models. The model with the highest support indicated that observer speed during a survey, combined with temporal covariates such as time of year and length of time within a territory, had the highest influence on the detection probability. Averaged detection probability was 0.207 (s.e. 0.033) and based on this the mean number of visits required to determine within 95% confidence that white-headed vultures are absent from a breeding area is 13 (95% CI: 9-20). Topographical and habitat covariates contributed little to the best models and had little effect on detection probability. We highlight that low detection probabilities of some species means that emphasizing habitat covariates could lead to spurious results in occupancy models that do not also incorporate temporal components. While variation in detection probability is complex and influenced by effects at both temporal and spatial scales, temporal covariates can and should be controlled as part of robust survey methods. Our results emphasize the importance of accounting for detection probability in occupancy studies, particularly during presence/absence studies for species such as raptors that are widespread and occur at low densities.

Performance of concatenated Reed-Solomon/Viterbi channel coding

NASA Technical Reports Server (NTRS)

Divsalar, D.; Yuen, J. H.

1982-01-01

The concatenated Reed-Solomon (RS)/Viterbi coding system is reviewed. The performance of the system is analyzed and results are derived with a new simple approach. A functional model for the input RS symbol error probability is presented. Based on this new functional model, we compute the performance of a concatenated system in terms of RS word error probability, output RS symbol error probability, bit error probability due to decoding failure, and bit error probability due to decoding error. Finally we analyze the effects of the noisy carrier reference and the slow fading on the system performance.

Statistical methods for incomplete data: Some results on model misspecification.

PubMed

McIsaac, Michael; Cook, R J

2017-02-01

Inverse probability weighted estimating equations and multiple imputation are two of the most studied frameworks for dealing with incomplete data in clinical and epidemiological research. We examine the limiting behaviour of estimators arising from inverse probability weighted estimating equations, augmented inverse probability weighted estimating equations and multiple imputation when the requisite auxiliary models are misspecified. We compute limiting values for settings involving binary responses and covariates and illustrate the effects of model misspecification using simulations based on data from a breast cancer clinical trial. We demonstrate that, even when both auxiliary models are misspecified, the asymptotic biases of double-robust augmented inverse probability weighted estimators are often smaller than the asymptotic biases of estimators arising from complete-case analyses, inverse probability weighting or multiple imputation. We further demonstrate that use of inverse probability weighting or multiple imputation with slightly misspecified auxiliary models can actually result in greater asymptotic bias than the use of naïve, complete case analyses. These asymptotic results are shown to be consistent with empirical results from simulation studies.

Modelling uncertainty with generalized credal sets: application to conjunction and decision

NASA Astrophysics Data System (ADS)

Bronevich, Andrey G.; Rozenberg, Igor N.

2018-01-01

To model conflict, non-specificity and contradiction in information, upper and lower generalized credal sets are introduced. Any upper generalized credal set is a convex subset of plausibility measures interpreted as lower probabilities whose bodies of evidence consist of singletons and a certain event. Analogously, contradiction is modelled in the theory of evidence by a belief function that is greater than zero at empty set. Based on generalized credal sets, we extend the conjunctive rule for contradictory sources of information, introduce constructions like natural extension in the theory of imprecise probabilities and show that the model of generalized credal sets coincides with the model of imprecise probabilities if the profile of a generalized credal set consists of probability measures. We give ways how the introduced model can be applied to decision problems.

Uncertainty plus prior equals rational bias: an intuitive Bayesian probability weighting function.

PubMed

Fennell, John; Baddeley, Roland

2012-10-01

Empirical research has shown that when making choices based on probabilistic options, people behave as if they overestimate small probabilities, underestimate large probabilities, and treat positive and negative outcomes differently. These distortions have been modeled using a nonlinear probability weighting function, which is found in several nonexpected utility theories, including rank-dependent models and prospect theory; here, we propose a Bayesian approach to the probability weighting function and, with it, a psychological rationale. In the real world, uncertainty is ubiquitous and, accordingly, the optimal strategy is to combine probability statements with prior information using Bayes' rule. First, we show that any reasonable prior on probabilities leads to 2 of the observed effects; overweighting of low probabilities and underweighting of high probabilities. We then investigate 2 plausible kinds of priors: informative priors based on previous experience and uninformative priors of ignorance. Individually, these priors potentially lead to large problems of bias and inefficiency, respectively; however, when combined using Bayesian model comparison methods, both forms of prior can be applied adaptively, gaining the efficiency of empirical priors and the robustness of ignorance priors. We illustrate this for the simple case of generic good and bad options, using Internet blogs to estimate the relevant priors of inference. Given this combined ignorant/informative prior, the Bayesian probability weighting function is not only robust and efficient but also matches all of the major characteristics of the distortions found in empirical research. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Single, Complete, Probability Spaces Consistent With EPR-Bohm-Bell Experimental Data

NASA Astrophysics Data System (ADS)

Avis, David; Fischer, Paul; Hilbert, Astrid; Khrennikov, Andrei

2009-03-01

We show that paradoxical consequences of violations of Bell's inequality are induced by the use of an unsuitable probabilistic description for the EPR-Bohm-Bell experiment. The conventional description (due to Bell) is based on a combination of statistical data collected for different settings of polarization beam splitters (PBSs). In fact, such data consists of some conditional probabilities which only partially define a probability space. Ignoring this conditioning leads to apparent contradictions in the classical probabilistic model (due to Kolmogorov). We show how to make a completely consistent probabilistic model by taking into account the probabilities of selecting the settings of the PBSs. Our model matches both the experimental data and is consistent with classical probability theory.

Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach.

DTIC Science & Technology

1998-05-01

Coverage Probability with a Random Optimization Procedure: An Artificial Neural Network Approach by Biing T. Guan, George Z. Gertner, and Alan B...Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach 6. AUTHOR(S) Biing...coverage based on past coverage. Approach A literature survey was conducted to identify artificial neural network analysis techniques applicable for

The Tightness of the Kesten-Stigum Reconstruction Bound of Symmetric Model with Multiple Mutations

NASA Astrophysics Data System (ADS)

Liu, Wenjian; Jammalamadaka, Sreenivasa Rao; Ning, Ning

2018-02-01

It is well known that reconstruction problems, as the interdisciplinary subject, have been studied in numerous contexts including statistical physics, information theory and computational biology, to name a few. We consider a 2 q-state symmetric model, with two categories of q states in each category, and 3 transition probabilities: the probability to remain in the same state, the probability to change states but remain in the same category, and the probability to change categories. We construct a nonlinear second-order dynamical system based on this model and show that the Kesten-Stigum reconstruction bound is not tight when q ≥ 4.

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model

PubMed Central

Guan, Li; Hao, Bibo; Cheng, Qijin; Yip, Paul SF

2015-01-01

Background Traditional offline assessment of suicide probability is time consuming and difficult in convincing at-risk individuals to participate. Identifying individuals with high suicide probability through online social media has an advantage in its efficiency and potential to reach out to hidden individuals, yet little research has been focused on this specific field. Objective The objective of this study was to apply two classification models, Simple Logistic Regression (SLR) and Random Forest (RF), to examine the feasibility and effectiveness of identifying high suicide possibility microblog users in China through profile and linguistic features extracted from Internet-based data. Methods There were nine hundred and nine Chinese microblog users that completed an Internet survey, and those scoring one SD above the mean of the total Suicide Probability Scale (SPS) score, as well as one SD above the mean in each of the four subscale scores in the participant sample were labeled as high-risk individuals, respectively. Profile and linguistic features were fed into two machine learning algorithms (SLR and RF) to train the model that aims to identify high-risk individuals in general suicide probability and in its four dimensions. Models were trained and then tested by 5-fold cross validation; in which both training set and test set were generated under the stratified random sampling rule from the whole sample. There were three classic performance metrics (Precision, Recall, F1 measure) and a specifically defined metric “Screening Efficiency” that were adopted to evaluate model effectiveness. Results Classification performance was generally matched between SLR and RF. Given the best performance of the classification models, we were able to retrieve over 70% of the labeled high-risk individuals in overall suicide probability as well as in the four dimensions. Screening Efficiency of most models varied from 1/4 to 1/2. Precision of the models was generally below 30%. Conclusions Individuals in China with high suicide probability are recognizable by profile and text-based information from microblogs. Although there is still much space to improve the performance of classification models in the future, this study may shed light on preliminary screening of risky individuals via machine learning algorithms, which can work side-by-side with expert scrutiny to increase efficiency in large-scale-surveillance of suicide probability from online social media. PMID:26543921

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model.

PubMed

Guan, Li; Hao, Bibo; Cheng, Qijin; Yip, Paul Sf; Zhu, Tingshao

2015-01-01

Traditional offline assessment of suicide probability is time consuming and difficult in convincing at-risk individuals to participate. Identifying individuals with high suicide probability through online social media has an advantage in its efficiency and potential to reach out to hidden individuals, yet little research has been focused on this specific field. The objective of this study was to apply two classification models, Simple Logistic Regression (SLR) and Random Forest (RF), to examine the feasibility and effectiveness of identifying high suicide possibility microblog users in China through profile and linguistic features extracted from Internet-based data. There were nine hundred and nine Chinese microblog users that completed an Internet survey, and those scoring one SD above the mean of the total Suicide Probability Scale (SPS) score, as well as one SD above the mean in each of the four subscale scores in the participant sample were labeled as high-risk individuals, respectively. Profile and linguistic features were fed into two machine learning algorithms (SLR and RF) to train the model that aims to identify high-risk individuals in general suicide probability and in its four dimensions. Models were trained and then tested by 5-fold cross validation; in which both training set and test set were generated under the stratified random sampling rule from the whole sample. There were three classic performance metrics (Precision, Recall, F1 measure) and a specifically defined metric "Screening Efficiency" that were adopted to evaluate model effectiveness. Classification performance was generally matched between SLR and RF. Given the best performance of the classification models, we were able to retrieve over 70% of the labeled high-risk individuals in overall suicide probability as well as in the four dimensions. Screening Efficiency of most models varied from 1/4 to 1/2. Precision of the models was generally below 30%. Individuals in China with high suicide probability are recognizable by profile and text-based information from microblogs. Although there is still much space to improve the performance of classification models in the future, this study may shed light on preliminary screening of risky individuals via machine learning algorithms, which can work side-by-side with expert scrutiny to increase efficiency in large-scale-surveillance of suicide probability from online social media.

Estimation of the age-specific per-contact probability of Ebola virus transmission in Liberia using agent-based simulations

NASA Astrophysics Data System (ADS)

Siettos, Constantinos I.; Anastassopoulou, Cleo; Russo, Lucia; Grigoras, Christos; Mylonakis, Eleftherios

2016-06-01

Based on multiscale agent-based computations we estimated the per-contact probability of transmission by age of the Ebola virus disease (EVD) that swept through Liberia from May 2014 to March 2015. For the approximation of the epidemic dynamics we have developed a detailed agent-based model with small-world interactions between individuals categorized by age. For the estimation of the structure of the evolving contact network as well as the per-contact transmission probabilities by age group we exploited the so called Equation-Free framework. Model parameters were fitted to official case counts reported by the World Health Organization (WHO) as well as to recently published data of key epidemiological variables, such as the mean time to death, recovery and the case fatality rate.

Spatial capture-recapture models for jointly estimating population density and landscape connectivity

USGS Publications Warehouse

Royle, J. Andrew; Chandler, Richard B.; Gazenski, Kimberly D.; Graves, Tabitha A.

2013-01-01

Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture–recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on “ecological distance,” i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture–recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture–recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.

Spatial capture--recapture models for jointly estimating population density and landscape connectivity.

PubMed

Royle, J Andrew; Chandler, Richard B; Gazenski, Kimberly D; Graves, Tabitha A

2013-02-01

Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture--recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on "ecological distance," i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture-recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture-recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.

Questioning the Relevance of Model-Based Probability Statements on Extreme Weather and Future Climate

NASA Astrophysics Data System (ADS)

Smith, L. A.

2007-12-01

We question the relevance of climate-model based Bayesian (or other) probability statements for decision support and impact assessment on spatial scales less than continental and temporal averages less than seasonal. Scientific assessment of higher resolution space and time scale information is urgently needed, given the commercial availability of "products" at high spatiotemporal resolution, their provision by nationally funded agencies for use both in industry decision making and governmental policy support, and their presentation to the public as matters of fact. Specifically we seek to establish necessary conditions for probability forecasts (projections conditioned on a model structure and a forcing scenario) to be taken seriously as reflecting the probability of future real-world events. We illustrate how risk management can profitably employ imperfect models of complicated chaotic systems, following NASA's study of near-Earth PHOs (Potentially Hazardous Objects). Our climate models will never be perfect, nevertheless the space and time scales on which they provide decision- support relevant information is expected to improve with the models themselves. Our aim is to establish a set of baselines of internal consistency; these are merely necessary conditions (not sufficient conditions) that physics based state-of-the-art models are expected to pass if their output is to be judged decision support relevant. Probabilistic Similarity is proposed as one goal which can be obtained even when our models are not empirically adequate. In short, probabilistic similarity requires that, given inputs similar to today's empirical observations and observational uncertainties, we expect future models to produce similar forecast distributions. Expert opinion on the space and time scales on which we might reasonably expect probabilistic similarity may prove of much greater utility than expert elicitation of uncertainty in parameter values in a model that is not empirically adequate; this may help to explain the reluctance of experts to provide information on "parameter uncertainty." Probability statements about the real world are always conditioned on some information set; they may well be conditioned on "False" making them of little value to a rational decision maker. In other instances, they may be conditioned on physical assumptions not held by any of the modellers whose model output is being cast as a probability distribution. Our models will improve a great deal in the next decades, and our insight into the likely climate fifty years hence will improve: maintaining the credibility of the science and the coherence of science based decision support, as our models improve, require a clear statement of our current limitations. What evidence do we have that today's state-of-the-art models provide decision-relevant probability forecasts? What space and time scales do we currently have quantitative, decision-relevant information on for 2050? 2080?

Reader reaction to "a robust method for estimating optimal treatment regimes" by Zhang et al. (2012).

PubMed

Taylor, Jeremy M G; Cheng, Wenting; Foster, Jared C

2015-03-01

A recent article (Zhang et al., 2012, Biometrics 168, 1010-1018) compares regression based and inverse probability based methods of estimating an optimal treatment regime and shows for a small number of covariates that inverse probability weighted methods are more robust to model misspecification than regression methods. We demonstrate that using models that fit the data better reduces the concern about non-robustness for the regression methods. We extend the simulation study of Zhang et al. (2012, Biometrics 168, 1010-1018), also considering the situation of a larger number of covariates, and show that incorporating random forests into both regression and inverse probability weighted based methods improves their properties. © 2014, The International Biometric Society.

Risk-based decision making to manage water quality failures caused by combined sewer overflows

NASA Astrophysics Data System (ADS)

Sriwastava, A. K.; Torres-Matallana, J. A.; Tait, S.; Schellart, A.

2017-12-01

Regulatory authorities set certain environmental permit for water utilities such that the combined sewer overflows (CSO) managed by these companies conform to the regulations. These utility companies face the risk of paying penalty or negative publicity in case they breach the environmental permit. These risks can be addressed by designing appropriate solutions such as investing in additional infrastructure which improve the system capacity and reduce the impact of CSO spills. The performance of these solutions is often estimated using urban drainage models. Hence, any uncertainty in these models can have a significant effect on the decision making process. This study outlines a risk-based decision making approach to address water quality failure caused by CSO spills. A calibrated lumped urban drainage model is used to simulate CSO spill quality in Haute-Sûre catchment in Luxembourg. Uncertainty in rainfall and model parameters is propagated through Monte Carlo simulations to quantify uncertainty in the concentration of ammonia in the CSO spill. A combination of decision alternatives such as the construction of a storage tank at the CSO and the reduction in the flow contribution of catchment surfaces are selected as planning measures to avoid the water quality failure. Failure is defined as exceedance of a concentration-duration based threshold based on Austrian emission standards for ammonia (De Toffol, 2006) with a certain frequency. For each decision alternative, uncertainty quantification results into a probability distribution of the number of annual CSO spill events which exceed the threshold. For each alternative, a buffered failure probability as defined in Rockafellar & Royset (2010), is estimated. Buffered failure probability (pbf) is a conservative estimate of failure probability (pf), however, unlike failure probability, it includes information about the upper tail of the distribution. A pareto-optimal set of solutions is obtained by performing mean- pbf optimization. The effectiveness of using buffered failure probability compared to the failure probability is tested by comparing the solutions obtained by using mean-pbf and mean-pf optimizations.

Retrospective validation of renewal-based, medium-term earthquake forecasts

NASA Astrophysics Data System (ADS)

Rotondi, R.

2013-10-01

In this paper, some methods for scoring the performances of an earthquake forecasting probability model are applied retrospectively for different goals. The time-dependent occurrence probabilities of a renewal process are tested against earthquakes of Mw ≥ 5.3 recorded in Italy according to decades of the past century. An aim was to check the capability of the model to reproduce the data by which the model was calibrated. The scoring procedures used can be distinguished on the basis of the requirement (or absence) of a reference model and of probability thresholds. Overall, a rank-based score, information gain, gambling scores, indices used in binary predictions and their loss functions are considered. The definition of various probability thresholds as percentages of the hazard functions allows proposals of the values associated with the best forecasting performance as alarm level in procedures for seismic risk mitigation. Some improvements are then made to the input data concerning the completeness of the historical catalogue and the consistency of the composite seismogenic sources with the hypotheses of the probability model. Another purpose of this study was thus to obtain hints on what is the most influential factor and on the suitability of adopting the consequent changes of the data sets. This is achieved by repeating the estimation procedure of the occurrence probabilities and the retrospective validation of the forecasts obtained under the new assumptions. According to the rank-based score, the completeness appears to be the most influential factor, while there are no clear indications of the usefulness of the decomposition of some composite sources, although in some cases, it has led to improvements of the forecast.

Inherent limitations of probabilistic models for protein-DNA binding specificity

PubMed Central

Ruan, Shuxiang

2017-01-01

The specificities of transcription factors are most commonly represented with probabilistic models. These models provide a probability for each base occurring at each position within the binding site and the positions are assumed to contribute independently. The model is simple and intuitive and is the basis for many motif discovery algorithms. However, the model also has inherent limitations that prevent it from accurately representing true binding probabilities, especially for the highest affinity sites under conditions of high protein concentration. The limitations are not due to the assumption of independence between positions but rather are caused by the non-linear relationship between binding affinity and binding probability and the fact that independent normalization at each position skews the site probabilities. Generally probabilistic models are reasonably good approximations, but new high-throughput methods allow for biophysical models with increased accuracy that should be used whenever possible. PMID:28686588

The effects of a FRAX revision for the US

USDA-ARS?s Scientific Manuscript database

The aim of this study was to determine the effects of a revision of the epidemiological data used to compute fracture probabilities in the US with FRAX (registered trademark). Models were constructed to compute fracture probabilities based on updated epidemiology of fracture in the US. The models c...

Combination of a Stressor-Response Model with a Conditional Probability Analysis Approach for Developing Candidate Criteria from MBSS

EPA Science Inventory

I show that a conditional probability analysis using a stressor-response model based on a logistic regression provides a useful approach for developing candidate water quality criteria from empirical data, such as the Maryland Biological Streams Survey (MBSS) data.

Gap probability - Measurements and models of a pecan orchard

NASA Technical Reports Server (NTRS)

Strahler, Alan H.; Li, Xiaowen; Moody, Aaron; Liu, YI

1992-01-01

Measurements and models are compared for gap probability in a pecan orchard. Measurements are based on panoramic photographs of 50* by 135 view angle made under the canopy looking upwards at regular positions along transects between orchard trees. The gap probability model is driven by geometric parameters at two levels-crown and leaf. Crown level parameters include the shape of the crown envelope and spacing of crowns; leaf level parameters include leaf size and shape, leaf area index, and leaf angle, all as functions of canopy position.

Voltage dependency of transmission probability of aperiodic DNA molecule

NASA Astrophysics Data System (ADS)

Wiliyanti, V.; Yudiarsah, E.

2017-07-01

Characteristics of electron transports in aperiodic DNA molecules have been studied. Double stranded DNA model with the sequences of bases, GCTAGTACGTGACGTAGCTAGGATATGCCTGA, in one chain and its complements on the other chains has been used. Tight binding Hamiltonian is used to model DNA molecules. In the model, we consider that on-site energy of the basis has a linearly dependency on the applied electric field. Slater-Koster scheme is used to model electron hopping constant between bases. The transmission probability of electron from one electrode to the next electrode is calculated using a transfer matrix technique and scattering matrix method simultaneously. The results show that, generally, higher voltage gives a slightly larger value of the transmission probability. The applied voltage seems to shift extended states to lower energy. Meanwhile, the value of the transmission increases with twisting motion frequency increment.

Ubiquitous Log Odds: A Common Representation of Probability and Frequency Distortion in Perception, Action, and Cognition

PubMed Central

Zhang, Hang; Maloney, Laurence T.

2012-01-01

In decision from experience, the source of probability information affects how probability is distorted in the decision task. Understanding how and why probability is distorted is a key issue in understanding the peculiar character of experience-based decision. We consider how probability information is used not just in decision-making but also in a wide variety of cognitive, perceptual, and motor tasks. Very similar patterns of distortion of probability/frequency information have been found in visual frequency estimation, frequency estimation based on memory, signal detection theory, and in the use of probability information in decision-making under risk and uncertainty. We show that distortion of probability in all cases is well captured as linear transformations of the log odds of frequency and/or probability, a model with a slope parameter, and an intercept parameter. We then consider how task and experience influence these two parameters and the resulting distortion of probability. We review how the probability distortions change in systematic ways with task and report three experiments on frequency distortion where the distortions change systematically in the same task. We found that the slope of frequency distortions decreases with the sample size, which is echoed by findings in decision from experience. We review previous models of the representation of uncertainty and find that none can account for the empirical findings. PMID:22294978

Reliability based fatigue design and maintenance procedures

NASA Technical Reports Server (NTRS)

Hanagud, S.

1977-01-01

A stochastic model has been developed to describe a probability for fatigue process by assuming a varying hazard rate. This stochastic model can be used to obtain the desired probability of a crack of certain length at a given location after a certain number of cycles or time. Quantitative estimation of the developed model was also discussed. Application of the model to develop a procedure for reliability-based cost-effective fail-safe structural design is presented. This design procedure includes the reliability improvement due to inspection and repair. Methods of obtaining optimum inspection and maintenance schemes are treated.

Knock probability estimation through an in-cylinder temperature model with exogenous noise

NASA Astrophysics Data System (ADS)

Bares, P.; Selmanaj, D.; Guardiola, C.; Onder, C.

2018-01-01

This paper presents a new knock model which combines a deterministic knock model based on the in-cylinder temperature and an exogenous noise disturbing this temperature. The autoignition of the end-gas is modelled by an Arrhenius-like function and the knock probability is estimated by propagating a virtual error probability distribution. Results show that the random nature of knock can be explained by uncertainties at the in-cylinder temperature estimation. The model only has one parameter for calibration and thus can be easily adapted online. In order to reduce the measurement uncertainties associated with the air mass flow sensor, the trapped mass is derived from the in-cylinder pressure resonance, which improves the knock probability estimation and reduces the number of sensors needed for the model. A four stroke SI engine was used for model validation. By varying the intake temperature, the engine speed, the injected fuel mass, and the spark advance, specific tests were conducted, which furnished data with various knock intensities and probabilities. The new model is able to predict the knock probability within a sufficient range at various operating conditions. The trapped mass obtained by the acoustical model was compared in steady conditions by using a fuel balance and a lambda sensor and differences below 1 % were found.

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

The Role of Probability-Based Inference in an Intelligent Tutoring System.

ERIC Educational Resources Information Center

Mislevy, Robert J.; Gitomer, Drew H.

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…

Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

PubMed

Lee, Wen-Chung; Wu, Yun-Chun

2016-01-01

The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

Discriminating Among Probability Weighting Functions Using Adaptive Design Optimization

PubMed Central

Cavagnaro, Daniel R.; Pitt, Mark A.; Gonzalez, Richard; Myung, Jay I.

2014-01-01

Probability weighting functions relate objective probabilities and their subjective weights, and play a central role in modeling choices under risk within cumulative prospect theory. While several different parametric forms have been proposed, their qualitative similarities make it challenging to discriminate among them empirically. In this paper, we use both simulation and choice experiments to investigate the extent to which different parametric forms of the probability weighting function can be discriminated using adaptive design optimization, a computer-based methodology that identifies and exploits model differences for the purpose of model discrimination. The simulation experiments show that the correct (data-generating) form can be conclusively discriminated from its competitors. The results of an empirical experiment reveal heterogeneity between participants in terms of the functional form, with two models (Prelec-2, Linear in Log Odds) emerging as the most common best-fitting models. The findings shed light on assumptions underlying these models. PMID:24453406

Regional Permafrost Probability Modelling in the northwestern Cordillera, 59°N - 61°N, Canada

NASA Astrophysics Data System (ADS)

Bonnaventure, P. P.; Lewkowicz, A. G.

2010-12-01

High resolution (30 x 30 m) permafrost probability models were created for eight mountainous areas in the Yukon and northernmost British Columbia. Empirical-statistical modelling based on the Basal Temperature of Snow (BTS) method was used to develop spatial relationships. Model inputs include equivalent elevation (a variable that incorporates non-uniform temperature change with elevation), potential incoming solar radiation and slope. Probability relationships between predicted BTS and permafrost presence were developed for each area using late-summer physical observations in pits, or by using year-round ground temperature measurements. A high-resolution spatial model for the region has now been generated based on seven of the area models. Each was applied to the entire region, and their predictions were then blended based on a distance decay function from the model source area. The regional model is challenging to validate independently because there are few boreholes in the region. However, a comparison of results to a recently established inventory of rock glaciers for the Yukon suggests its validity because predicted permafrost probabilities were 0.8 or greater for almost 90% of these landforms. Furthermore, the regional model results have a similar spatial pattern to those modelled independently in the eighth area, although predicted probabilities using the regional model are generally higher. The regional model predicts that permafrost underlies about half of the non-glaciated terrain in the region, with probabilities increasing regionally from south to north and from east to west. Elevation is significant, but not always linked in a straightforward fashion because of weak or inverted trends in permafrost probability below treeline. Above treeline, however, permafrost probabilities increase and approach 1.0 in very high elevation areas throughout the study region. The regional model shows many similarities to previous Canadian permafrost maps (Heginbottom and Radburn, 1992; Heginbottom et al., 1995) but is several orders of magnitude more detailed. It also exhibits some significant differences, including the presence of an area of valley-floor continuous permafrost around Beaver Creek near the Alaskan border in the west, as well as higher probabilities of permafrost in the central parts of the region near the boundaries of the sporadic and extensive discontinuous zones. In addition, parts of the northernmost portion of the region would be classified as sporadic discontinuous permafrost because of inversions in the terrestrial surface lapse rate which cause permafrost probabilities to decrease with elevation through the forest. These model predictions are expected to of direct use for infrastructure planning and northern development and can serve as a benchmark for future studies of permafrost distribution in the Yukon. References Heginbottom JR, Dubreuil MA and Haker PT. 1995. Canada Permafrost. (1:7,500,000 scale). In The National Atlas of Canada, 5th Edition, sheet MCR 4177. Ottawa: National Resources Canada. Heginbottom, J.A. and Radburn, L.K. 1992. Permafrost and ground ice conditions of northwestern Canada; Geological Survey of Canada, Map 1691A, scale 1:1,000,000. Digitized by S. Smith, Geological Survey of Canada.

Sandpile-based model for capturing magnitude distributions and spatiotemporal clustering and separation in regional earthquakes

NASA Astrophysics Data System (ADS)

Batac, Rene C.; Paguirigan, Antonino A., Jr.; Tarun, Anjali B.; Longjas, Anthony G.

2017-04-01

We propose a cellular automata model for earthquake occurrences patterned after the sandpile model of self-organized criticality (SOC). By incorporating a single parameter describing the probability to target the most susceptible site, the model successfully reproduces the statistical signatures of seismicity. The energy distributions closely follow power-law probability density functions (PDFs) with a scaling exponent of around -1. 6, consistent with the expectations of the Gutenberg-Richter (GR) law, for a wide range of the targeted triggering probability values. Additionally, for targeted triggering probabilities within the range 0.004-0.007, we observe spatiotemporal distributions that show bimodal behavior, which is not observed previously for the original sandpile. For this critical range of values for the probability, model statistics show remarkable comparison with long-period empirical data from earthquakes from different seismogenic regions. The proposed model has key advantages, the foremost of which is the fact that it simultaneously captures the energy, space, and time statistics of earthquakes by just introducing a single parameter, while introducing minimal parameters in the simple rules of the sandpile. We believe that the critical targeting probability parameterizes the memory that is inherently present in earthquake-generating regions.

Combination of a Stresor-Response Model with a Conditional Probability Anaylsis Approach to Develop Candidate Criteria from Empirical Data

EPA Science Inventory

We show that a conditional probability analysis that utilizes a stressor-response model based on a logistic regression provides a useful approach for developing candidate water quality criterai from empirical data. The critical step in this approach is transforming the response ...

Cluster-based control of a separating flow over a smoothly contoured ramp

NASA Astrophysics Data System (ADS)

Kaiser, Eurika; Noack, Bernd R.; Spohn, Andreas; Cattafesta, Louis N.; Morzyński, Marek

2017-12-01

The ability to manipulate and control fluid flows is of great importance in many scientific and engineering applications. The proposed closed-loop control framework addresses a key issue of model-based control: The actuation effect often results from slow dynamics of strongly nonlinear interactions which the flow reveals at timescales much longer than the prediction horizon of any model. Hence, we employ a probabilistic approach based on a cluster-based discretization of the Liouville equation for the evolution of the probability distribution. The proposed methodology frames high-dimensional, nonlinear dynamics into low-dimensional, probabilistic, linear dynamics which considerably simplifies the optimal control problem while preserving nonlinear actuation mechanisms. The data-driven approach builds upon a state space discretization using a clustering algorithm which groups kinematically similar flow states into a low number of clusters. The temporal evolution of the probability distribution on this set of clusters is then described by a control-dependent Markov model. This Markov model can be used as predictor for the ergodic probability distribution for a particular control law. This probability distribution approximates the long-term behavior of the original system on which basis the optimal control law is determined. We examine how the approach can be used to improve the open-loop actuation in a separating flow dominated by Kelvin-Helmholtz shedding. For this purpose, the feature space, in which the model is learned, and the admissible control inputs are tailored to strongly oscillatory flows.

Lane detection based on color probability model and fuzzy clustering

NASA Astrophysics Data System (ADS)

Yu, Yang; Jo, Kang-Hyun

2018-04-01

In the vehicle driver assistance systems, the accuracy and speed of lane line detection are the most important. This paper is based on color probability model and Fuzzy Local Information C-Means (FLICM) clustering algorithm. The Hough transform and the constraints of structural road are used to detect the lane line accurately. The global map of the lane line is drawn by the lane curve fitting equation. The experimental results show that the algorithm has good robustness.

Effect of Non-speckle Echo Signals on Tissue Characteristics for Liver Fibrosis using Probability Density Function of Ultrasonic B-mode image

NASA Astrophysics Data System (ADS)

Mori, Shohei; Hirata, Shinnosuke; Yamaguchi, Tadashi; Hachiya, Hiroyuki

To develop a quantitative diagnostic method for liver fibrosis using an ultrasound B-mode image, a probability imaging method of tissue characteristics based on a multi-Rayleigh model, which expresses a probability density function of echo signals from liver fibrosis, has been proposed. In this paper, an effect of non-speckle echo signals on tissue characteristics estimated from the multi-Rayleigh model was evaluated. Non-speckle signals were determined and removed using the modeling error of the multi-Rayleigh model. The correct tissue characteristics of fibrotic tissue could be estimated with the removal of non-speckle signals.

Probability of identity by descent in metapopulations.

PubMed Central

Kaj, I; Lascoux, M

1999-01-01

Equilibrium probabilities of identity by descent (IBD), for pairs of genes within individuals, for genes between individuals within subpopulations, and for genes between subpopulations are calculated in metapopulation models with fixed or varying colony sizes. A continuous-time analog to the Moran model was used in either case. For fixed-colony size both propagule and migrant pool models were considered. The varying population size model is based on a birth-death-immigration (BDI) process, to which migration between colonies is added. Wright's F statistics are calculated and compared to previous results. Adding between-island migration to the BDI model can have an important effect on the equilibrium probabilities of IBD and on Wright's index. PMID:10388835

Heuristics can produce surprisingly rational probability estimates: Comment on Costello and Watts (2014).

PubMed

Nilsson, Håkan; Juslin, Peter; Winman, Anders

2016-01-01

Costello and Watts (2014) present a model assuming that people's knowledge of probabilities adheres to probability theory, but that their probability judgments are perturbed by a random noise in the retrieval from memory. Predictions for the relationships between probability judgments for constituent events and their disjunctions and conjunctions, as well as for sums of such judgments were derived from probability theory. Costello and Watts (2014) report behavioral data showing that subjective probability judgments accord with these predictions. Based on the finding that subjective probability judgments follow probability theory, Costello and Watts (2014) conclude that the results imply that people's probability judgments embody the rules of probability theory and thereby refute theories of heuristic processing. Here, we demonstrate the invalidity of this conclusion by showing that all of the tested predictions follow straightforwardly from an account assuming heuristic probability integration (Nilsson, Winman, Juslin, & Hansson, 2009). We end with a discussion of a number of previous findings that harmonize very poorly with the predictions by the model suggested by Costello and Watts (2014). (c) 2015 APA, all rights reserved).

Link importance incorporated failure probability measuring solution for multicast light-trees in elastic optical networks

NASA Astrophysics Data System (ADS)

Li, Xin; Zhang, Lu; Tang, Ying; Huang, Shanguo

2018-03-01

The light-tree-based optical multicasting (LT-OM) scheme provides a spectrum- and energy-efficient method to accommodate emerging multicast services. Some studies focus on the survivability technologies for LTs against a fixed number of link failures, such as single-link failure. However, a few studies involve failure probability constraints when building LTs. It is worth noting that each link of an LT plays different important roles under failure scenarios. When calculating the failure probability of an LT, the importance of its every link should be considered. We design a link importance incorporated failure probability measuring solution (LIFPMS) for multicast LTs under independent failure model and shared risk link group failure model. Based on the LIFPMS, we put forward the minimum failure probability (MFP) problem for the LT-OM scheme. Heuristic approaches are developed to address the MFP problem in elastic optical networks. Numerical results show that the LIFPMS provides an accurate metric for calculating the failure probability of multicast LTs and enhances the reliability of the LT-OM scheme while accommodating multicast services.

The reduced transition probabilities for excited states of rare-earths and actinide even-even nuclei

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

Ghumman, S. S.

The theoretical B(E2) ratios have been calculated on DF, DR and Krutov models. A simple method based on the work of Arima and Iachello is used to calculate the reduced transition probabilities within SU(3) limit of IBA-I framework. The reduced E2 transition probabilities from second excited states of rare-earths and actinide even–even nuclei calculated from experimental energies and intensities from recent data, have been found to compare better with those calculated on the Krutov model and the SU(3) limit of IBA than the DR and DF models.

The Sequential Probability Ratio Test and Binary Item Response Models

ERIC Educational Resources Information Center

Nydick, Steven W.

2014-01-01

The sequential probability ratio test (SPRT) is a common method for terminating item response theory (IRT)-based adaptive classification tests. To decide whether a classification test should stop, the SPRT compares a simple log-likelihood ratio, based on the classification bound separating two categories, to prespecified critical values. As has…

A quantile-based Time at Risk: A new approach for assessing risk in financial markets

NASA Astrophysics Data System (ADS)

Bolgorian, Meysam; Raei, Reza

2013-11-01

In this paper, we provide a new measure for evaluation of risk in financial markets. This measure is based on the return interval of critical events in financial markets or other investment situations. Our main goal was to devise a model like Value at Risk (VaR). As VaR, for a given financial asset, probability level and time horizon, gives a critical value such that the likelihood of loss on the asset over the time horizon exceeds this value is equal to the given probability level, our concept of Time at Risk (TaR), using a probability distribution function of return intervals, provides a critical time such that the probability that the return interval of a critical event exceeds this time equals the given probability level. As an empirical application, we applied our model to data from the Tehran Stock Exchange Price Index (TEPIX) as a financial asset (market portfolio) and reported the results.

A risk assessment method for multi-site damage

NASA Astrophysics Data System (ADS)

Millwater, Harry Russell, Jr.

This research focused on developing probabilistic methods suitable for computing small probabilities of failure, e.g., 10sp{-6}, of structures subject to multi-site damage (MSD). MSD is defined as the simultaneous development of fatigue cracks at multiple sites in the same structural element such that the fatigue cracks may coalesce to form one large crack. MSD is modeled as an array of collinear cracks with random initial crack lengths with the centers of the initial cracks spaced uniformly apart. The data used was chosen to be representative of aluminum structures. The structure is considered failed whenever any two adjacent cracks link up. A fatigue computer model is developed that can accurately and efficiently grow a collinear array of arbitrary length cracks from initial size until failure. An algorithm is developed to compute the stress intensity factors of all cracks considering all interaction effects. The probability of failure of two to 100 cracks is studied. Lower bounds on the probability of failure are developed based upon the probability of the largest crack exceeding a critical crack size. The critical crack size is based on the initial crack size that will grow across the ligament when the neighboring crack has zero length. The probability is evaluated using extreme value theory. An upper bound is based on the probability of the maximum sum of initial cracks being greater than a critical crack size. A weakest link sampling approach is developed that can accurately and efficiently compute small probabilities of failure. This methodology is based on predicting the weakest link, i.e., the two cracks to link up first, for a realization of initial crack sizes, and computing the cycles-to-failure using these two cracks. Criteria to determine the weakest link are discussed. Probability results using the weakest link sampling method are compared to Monte Carlo-based benchmark results. The results indicate that very small probabilities can be computed accurately in a few minutes using a Hewlett-Packard workstation.

Only the carrot, not the stick: incorporating trust into the enforcement of regulation.

PubMed

Mendoza, Juan P; Wielhouwer, Jacco L

2015-01-01

New enforcement strategies allow agents to gain the regulator's trust and consequently face a lower audit probability. Prior research suggests that, in order to prevent lower compliance, a reduction in the audit probability (the "carrot") must be compensated with the introduction of a higher penalty for non-compliance (the "stick"). However, such carrot-and-stick strategies reflect neither the concept of trust nor the strategies observed in practice. In response to this, we define trust-based regulation as a strategy that incorporates rules that allow trust to develop, and using a generic (non-cooperative) game of tax compliance, we examine whether trust-based regulation is feasible (i.e., whether, in equilibrium, a reduction in the audit probability, without ever increasing the penalty for non-compliance, does not lead to reduced compliance). The model shows that trust-based regulation is feasible when the agent sufficiently values the future. In line with the concept of trust, this strategy is feasible when the regulator is uncertain about the agent's intentions. Moreover, the model shows that (i) introducing higher penalties makes trust-based regulation less feasible, and (ii) combining trust and forgiveness can lead to a lower audit probability for both trusted and distrusted agents. Policy recommendations often point toward increasing deterrence. This model shows that the opposite can be optimal.

The Animism Controversy Revisited: A Probability Analysis

ERIC Educational Resources Information Center

Smeets, Paul M.

1973-01-01

Considers methodological issues surrounding the Piaget-Huang controversy. A probability model, based on the difference between the expected and observed animistic and deanimistic responses is applied as an improved technique for the assessment of animism. (DP)

Model-assisted probability of detection of flaws in aluminum blocks using polynomial chaos expansions

NASA Astrophysics Data System (ADS)

Du, Xiaosong; Leifsson, Leifur; Grandin, Robert; Meeker, William; Roberts, Ronald; Song, Jiming

2018-04-01

Probability of detection (POD) is widely used for measuring reliability of nondestructive testing (NDT) systems. Typically, POD is determined experimentally, while it can be enhanced by utilizing physics-based computational models in combination with model-assisted POD (MAPOD) methods. With the development of advanced physics-based methods, such as ultrasonic NDT testing, the empirical information, needed for POD methods, can be reduced. However, performing accurate numerical simulations can be prohibitively time-consuming, especially as part of stochastic analysis. In this work, stochastic surrogate models for computational physics-based measurement simulations are developed for cost savings of MAPOD methods while simultaneously ensuring sufficient accuracy. The stochastic surrogate is used to propagate the random input variables through the physics-based simulation model to obtain the joint probability distribution of the output. The POD curves are then generated based on those results. Here, the stochastic surrogates are constructed using non-intrusive polynomial chaos (NIPC) expansions. In particular, the NIPC methods used are the quadrature, ordinary least-squares (OLS), and least-angle regression sparse (LARS) techniques. The proposed approach is demonstrated on the ultrasonic testing simulation of a flat bottom hole flaw in an aluminum block. The results show that the stochastic surrogates have at least two orders of magnitude faster convergence on the statistics than direct Monte Carlo sampling (MCS). Moreover, the evaluation of the stochastic surrogate models is over three orders of magnitude faster than the underlying simulation model for this case, which is the UTSim2 model.

Analysis and model on space-time characteristics of wind power output based on the measured wind speed data

NASA Astrophysics Data System (ADS)

Shi, Wenhui; Feng, Changyou; Qu, Jixian; Zha, Hao; Ke, Dan

2018-02-01

Most of the existing studies on wind power output focus on the fluctuation of wind farms and the spatial self-complementary of wind power output time series was ignored. Therefore the existing probability models can’t reflect the features of power system incorporating wind farms. This paper analyzed the spatial self-complementary of wind power and proposed a probability model which can reflect temporal characteristics of wind power on seasonal and diurnal timescales based on sufficient measured data and improved clustering method. This model could provide important reference for power system simulation incorporating wind farms.

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

NASA Astrophysics Data System (ADS)

Li, Zhiqiang; Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-04-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.

Lognormal Approximations of Fault Tree Uncertainty Distributions.

PubMed

El-Shanawany, Ashraf Ben; Ardron, Keith H; Walker, Simon P

2018-01-26

Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed-form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling-based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed-form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks's method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models. © 2018 Society for Risk Analysis.

A polygon-based modeling approach to assess exposure of resources and assets to wildfire

Treesearch

Matthew P. Thompson; Joe Scott; Jeffrey D. Kaiden; Julie W. Gilbertson-Day

2013-01-01

Spatially explicit burn probability modeling is increasingly applied to assess wildfire risk and inform mitigation strategy development. Burn probabilities are typically expressed on a per-pixel basis, calculated as the number of times a pixel burns divided by the number of simulation iterations. Spatial intersection of highly valued resources and assets (HVRAs) with...

A Model-Based Architecture Approach to Ship Design Linking Capability Needs to System Solutions

DTIC Science & Technology

2012-06-01

NSSM NATO Sea Sparrow Missile RAM Rolling Airframe Missile CIWS Close-In Weapon System 3D Three Dimensional Ps Probability of Survival PHit ...example effectiveness model. The primary MOP is the inverse of the probability of taking a hit (1- PHit ), which in, this study, will be referred to as

Approximation of the ruin probability using the scaled Laplace transform inversion

PubMed Central

Mnatsakanov, Robert M.; Sarkisian, Khachatur; Hakobyan, Artak

2015-01-01

The problem of recovering the ruin probability in the classical risk model based on the scaled Laplace transform inversion is studied. It is shown how to overcome the problem of evaluating the ruin probability at large values of an initial surplus process. Comparisons of proposed approximations with the ones based on the Laplace transform inversions using a fixed Talbot algorithm as well as on the ones using the Trefethen–Weideman–Schmelzer and maximum entropy methods are presented via a simulation study. PMID:26752796

Atom counting in HAADF STEM using a statistical model-based approach: methodology, possibilities, and inherent limitations.

PubMed

De Backer, A; Martinez, G T; Rosenauer, A; Van Aert, S

2013-11-01

In the present paper, a statistical model-based method to count the number of atoms of monotype crystalline nanostructures from high resolution high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) images is discussed in detail together with a thorough study on the possibilities and inherent limitations. In order to count the number of atoms, it is assumed that the total scattered intensity scales with the number of atoms per atom column. These intensities are quantitatively determined using model-based statistical parameter estimation theory. The distribution describing the probability that intensity values are generated by atomic columns containing a specific number of atoms is inferred on the basis of the experimental scattered intensities. Finally, the number of atoms per atom column is quantified using this estimated probability distribution. The number of atom columns available in the observed STEM image, the number of components in the estimated probability distribution, the width of the components of the probability distribution, and the typical shape of a criterion to assess the number of components in the probability distribution directly affect the accuracy and precision with which the number of atoms in a particular atom column can be estimated. It is shown that single atom sensitivity is feasible taking the latter aspects into consideration. © 2013 Elsevier B.V. All rights reserved.

Modeling Finite-Time Failure Probabilities in Risk Analysis Applications.

PubMed

Dimitrova, Dimitrina S; Kaishev, Vladimir K; Zhao, Shouqi

2015-10-01

In this article, we introduce a framework for analyzing the risk of systems failure based on estimating the failure probability. The latter is defined as the probability that a certain risk process, characterizing the operations of a system, reaches a possibly time-dependent critical risk level within a finite-time interval. Under general assumptions, we define two dually connected models for the risk process and derive explicit expressions for the failure probability and also the joint probability of the time of the occurrence of failure and the excess of the risk process over the risk level. We illustrate how these probabilistic models and results can be successfully applied in several important areas of risk analysis, among which are systems reliability, inventory management, flood control via dam management, infectious disease spread, and financial insolvency. Numerical illustrations are also presented. © 2015 Society for Risk Analysis.

New normative standards of conditional reasoning and the dual-source model

PubMed Central

Singmann, Henrik; Klauer, Karl Christoph; Over, David

2014-01-01

There has been a major shift in research on human reasoning toward Bayesian and probabilistic approaches, which has been called a new paradigm. The new paradigm sees most everyday and scientific reasoning as taking place in a context of uncertainty, and inference is from uncertain beliefs and not from arbitrary assumptions. In this manuscript we present an empirical test of normative standards in the new paradigm using a novel probabilized conditional reasoning task. Our results indicated that for everyday conditional with at least a weak causal connection between antecedent and consequent only the conditional probability of the consequent given antecedent contributes unique variance to predicting the probability of conditional, but not the probability of the conjunction, nor the probability of the material conditional. Regarding normative accounts of reasoning, we found significant evidence that participants' responses were confidence preserving (i.e., p-valid in the sense of Adams, 1998) for MP inferences, but not for MT inferences. Additionally, only for MP inferences and to a lesser degree for DA inferences did the rate of responses inside the coherence intervals defined by mental probability logic (Pfeifer and Kleiter, 2005, 2010) exceed chance levels. In contrast to the normative accounts, the dual-source model (Klauer et al., 2010) is a descriptive model. It posits that participants integrate their background knowledge (i.e., the type of information primary to the normative approaches) and their subjective probability that a conclusion is seen as warranted based on its logical form. Model fits showed that the dual-source model, which employed participants' responses to a deductive task with abstract contents to estimate the form-based component, provided as good an account of the data as a model that solely used data from the probabilized conditional reasoning task. PMID:24860516

New normative standards of conditional reasoning and the dual-source model.

PubMed

Singmann, Henrik; Klauer, Karl Christoph; Over, David

2014-01-01

There has been a major shift in research on human reasoning toward Bayesian and probabilistic approaches, which has been called a new paradigm. The new paradigm sees most everyday and scientific reasoning as taking place in a context of uncertainty, and inference is from uncertain beliefs and not from arbitrary assumptions. In this manuscript we present an empirical test of normative standards in the new paradigm using a novel probabilized conditional reasoning task. Our results indicated that for everyday conditional with at least a weak causal connection between antecedent and consequent only the conditional probability of the consequent given antecedent contributes unique variance to predicting the probability of conditional, but not the probability of the conjunction, nor the probability of the material conditional. Regarding normative accounts of reasoning, we found significant evidence that participants' responses were confidence preserving (i.e., p-valid in the sense of Adams, 1998) for MP inferences, but not for MT inferences. Additionally, only for MP inferences and to a lesser degree for DA inferences did the rate of responses inside the coherence intervals defined by mental probability logic (Pfeifer and Kleiter, 2005, 2010) exceed chance levels. In contrast to the normative accounts, the dual-source model (Klauer et al., 2010) is a descriptive model. It posits that participants integrate their background knowledge (i.e., the type of information primary to the normative approaches) and their subjective probability that a conclusion is seen as warranted based on its logical form. Model fits showed that the dual-source model, which employed participants' responses to a deductive task with abstract contents to estimate the form-based component, provided as good an account of the data as a model that solely used data from the probabilized conditional reasoning task.

Testing Models for Perceptual Discrimination Using Repeatable Noise

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Null, Cynthia H. (Technical Monitor)

1998-01-01

Adding noise to stimuli to be discriminated allows estimation of observer classification functions based on the correlation between observer responses and relevant features of the noisy stimuli. Examples will be presented of stimulus features that are found in auditory tone detection and visual Vernier acuity. Using the standard signal detection model (Thurstone scaling), we derive formulas to estimate the proportion of the observer's decision variable variance that is controlled by the added noise. One is based on the probability of agreement of the observer with him/herself on trials with the same noise sample. Another is based on the relative performance of the observer and the model. When these do not agree, the model can be rejected. A second derivation gives the probability of agreement of observer and model when the observer follows the model except for internal noise. Agreement significantly less than this amount allows rejection of the model.

A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation

PubMed Central

Habas, Piotr A.; Kim, Kio; Corbett-Detig, James M.; Rousseau, Francois; Glenn, Orit A.; Barkovich, A. James; Studholme, Colin

2010-01-01

Modeling and analysis of MR images of the developing human brain is a challenge due to rapid changes in brain morphology and morphometry. We present an approach to the construction of a spatiotemporal atlas of the fetal brain with temporal models of MR intensity, tissue probability and shape changes. This spatiotemporal model is created from a set of reconstructed MR images of fetal subjects with different gestational ages. Groupwise registration of manual segmentations and voxelwise nonlinear modeling allow us to capture the appearance, disappearance and spatial variation of brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific MR templates and tissue probability maps and use them to initialize automatic tissue delineation in new MR images. The choice of model parameters and the final performance are evaluated using clinical MR scans of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Experimental results indicate that quadratic temporal models can correctly capture growth-related changes in the fetal brain anatomy and provide improvement in accuracy of atlas-based tissue segmentation. PMID:20600970

A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis.

PubMed

Zhao, Xing; Zhou, Xiao-Hua; Feng, Zijian; Guo, Pengfei; He, Hongyan; Zhang, Tao; Duan, Lei; Li, Xiaosong

2013-01-01

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

A quantum probability explanation for violations of ‘rational’ decision theory

PubMed Central

Pothos, Emmanuel M.; Busemeyer, Jerome R.

2009-01-01

Two experimental tasks in psychology, the two-stage gambling game and the Prisoner's Dilemma game, show that people violate the sure thing principle of decision theory. These paradoxical findings have resisted explanation by classical decision theory for over a decade. A quantum probability model, based on a Hilbert space representation and Schrödinger's equation, provides a simple and elegant explanation for this behaviour. The quantum model is compared with an equivalent Markov model and it is shown that the latter is unable to account for violations of the sure thing principle. Accordingly, it is argued that quantum probability provides a better framework for modelling human decision-making. PMID:19324743

Tsunami probability in the Caribbean Region

USGS Publications Warehouse

Parsons, T.; Geist, E.L.

2008-01-01

We calculated tsunami runup probability (in excess of 0.5 m) at coastal sites throughout the Caribbean region. We applied a Poissonian probability model because of the variety of uncorrelated tsunami sources in the region. Coastlines were discretized into 20 km by 20 km cells, and the mean tsunami runup rate was determined for each cell. The remarkable ???500-year empirical record compiled by O'Loughlin and Lander (2003) was used to calculate an empirical tsunami probability map, the first of three constructed for this study. However, it is unclear whether the 500-year record is complete, so we conducted a seismic moment-balance exercise using a finite-element model of the Caribbean-North American plate boundaries and the earthquake catalog, and found that moment could be balanced if the seismic coupling coefficient is c = 0.32. Modeled moment release was therefore used to generate synthetic earthquake sequences to calculate 50 tsunami runup scenarios for 500-year periods. We made a second probability map from numerically-calculated runup rates in each cell. Differences between the first two probability maps based on empirical and numerical-modeled rates suggest that each captured different aspects of tsunami generation; the empirical model may be deficient in primary plate-boundary events, whereas numerical model rates lack backarc fault and landslide sources. We thus prepared a third probability map using Bayesian likelihood functions derived from the empirical and numerical rate models and their attendant uncertainty to weight a range of rates at each 20 km by 20 km coastal cell. Our best-estimate map gives a range of 30-year runup probability from 0 - 30% regionally. ?? irkhaueser 2008.

Multiple Diseases in Carrier Probability Estimation: Accounting for Surviving All Cancers Other than Breast and Ovary in BRCAPRO

PubMed Central

Katki, Hormuzd A.; Blackford, Amanda; Chen, Sining; Parmigiani, Giovanni

2008-01-01

SUMMARY Mendelian models can predict who carries an inherited deleterious mutation of known disease genes based on family history. For example, the BRCAPRO model is commonly used to identify families who carry mutations of BRCA1 and BRCA2, based on familial breast and ovarian cancers. These models incorporate the age of diagnosis of diseases in relatives and current age or age of death. We develop a rigorous foundation for handling multiple diseases with censoring. We prove that any disease unrelated to mutations can be excluded from the model, unless it is sufficiently common and dependent on a mutation-related disease time. Furthermore, if a family member has a disease with higher probability density among mutation carriers, but the model does not account for it, then the carrier probability is deflated. However, even if a family only has diseases the model accounts for, if the model excludes a mutation-related disease, then the carrier probability will be inflated. In light of these results, we extend BRCAPRO to account for surviving all non-breast/ovary cancers as a single outcome. The extension also enables BRCAPRO to extract more useful information from male relatives. Using 1500 familes from the Cancer Genetics Network, accounting for surviving other cancers improves BRCAPRO’s concordance index from 0.758 to 0.762 (p = 0.046), improves its positive predictive value from 35% to 39% (p < 10−6) without impacting its negative predictive value, and improves its overall calibration, although calibration slightly worsens for those with carrier probability < 10%. PMID:18407567

Multiple diseases in carrier probability estimation: accounting for surviving all cancers other than breast and ovary in BRCAPRO.

PubMed

Katki, Hormuzd A; Blackford, Amanda; Chen, Sining; Parmigiani, Giovanni

2008-09-30

Mendelian models can predict who carries an inherited deleterious mutation of known disease genes based on family history. For example, the BRCAPRO model is commonly used to identify families who carry mutations of BRCA1 and BRCA2, based on familial breast and ovarian cancers. These models incorporate the age of diagnosis of diseases in relatives and current age or age of death. We develop a rigorous foundation for handling multiple diseases with censoring. We prove that any disease unrelated to mutations can be excluded from the model, unless it is sufficiently common and dependent on a mutation-related disease time. Furthermore, if a family member has a disease with higher probability density among mutation carriers, but the model does not account for it, then the carrier probability is deflated. However, even if a family only has diseases the model accounts for, if the model excludes a mutation-related disease, then the carrier probability will be inflated. In light of these results, we extend BRCAPRO to account for surviving all non-breast/ovary cancers as a single outcome. The extension also enables BRCAPRO to extract more useful information from male relatives. Using 1500 families from the Cancer Genetics Network, accounting for surviving other cancers improves BRCAPRO's concordance index from 0.758 to 0.762 (p=0.046), improves its positive predictive value from 35 to 39 per cent (p<10(-6)) without impacting its negative predictive value, and improves its overall calibration, although calibration slightly worsens for those with carrier probability<10 per cent. Copyright (c) 2008 John Wiley & Sons, Ltd.

Red-shouldered hawk occupancy surveys in central Minnesota, USA

USGS Publications Warehouse

Henneman, C.; McLeod, M.A.; Andersen, D.E.

2007-01-01

Forest-dwelling raptors are often difficult to detect because many species occur at low density or are secretive. Broadcasting conspecific vocalizations can increase the probability of detecting forest-dwelling raptors and has been shown to be an effective method for locating raptors and assessing their relative abundance. Recent advances in statistical techniques based on presence-absence data use probabilistic arguments to derive probability of detection when it is <1 and to provide a model and likelihood-based method for estimating proportion of sites occupied. We used these maximum-likelihood models with data from red-shouldered hawk (Buteo lineatus) call-broadcast surveys conducted in central Minnesota, USA, in 1994-1995 and 2004-2005. Our objectives were to obtain estimates of occupancy and detection probability 1) over multiple sampling seasons (yr), 2) incorporating within-season time-specific detection probabilities, 3) with call type and breeding stage included as covariates in models of probability of detection, and 4) with different sampling strategies. We visited individual survey locations 2-9 times per year, and estimates of both probability of detection (range = 0.28-0.54) and site occupancy (range = 0.81-0.97) varied among years. Detection probability was affected by inclusion of a within-season time-specific covariate, call type, and breeding stage. In 2004 and 2005 we used survey results to assess the effect that number of sample locations, double sampling, and discontinued sampling had on parameter estimates. We found that estimates of probability of detection and proportion of sites occupied were similar across different sampling strategies, and we suggest ways to reduce sampling effort in a monitoring program.

Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes

USGS Publications Warehouse

Mollenhauer, Robert; Mouser, Joshua B.; Brewer, Shannon K.

2018-01-01

Temporal and spatial variability in streams result in heterogeneous gear capture probability (i.e., the proportion of available individuals identified) that confounds interpretation of data used to monitor fish abundance. We modeled tow-barge electrofishing capture probability at multiple spatial scales for nine Ozark Highland stream fishes. In addition to fish size, we identified seven reach-scale environmental characteristics associated with variable capture probability: stream discharge, water depth, conductivity, water clarity, emergent vegetation, wetted width–depth ratio, and proportion of riffle habitat. The magnitude of the relationship between capture probability and both discharge and depth varied among stream fishes. We also identified lithological characteristics among stream segments as a coarse-scale source of variable capture probability. The resulting capture probability model can be used to adjust catch data and derive reach-scale absolute abundance estimates across a wide range of sampling conditions with similar effort as used in more traditional fisheries surveys (i.e., catch per unit effort). Adjusting catch data based on variable capture probability improves the comparability of data sets, thus promoting both well-informed conservation and management decisions and advances in stream-fish ecology.

Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS

PubMed Central

Wang, Yunpeng; Thompson, Wesley K.; Schork, Andrew J.; Holland, Dominic; Chen, Chi-Hua; Bettella, Francesco; Desikan, Rahul S.; Li, Wen; Witoelar, Aree; Zuber, Verena; Devor, Anna; Nöthen, Markus M.; Rietschel, Marcella; Chen, Qiang; Werge, Thomas; Cichon, Sven; Weinberger, Daniel R.; Djurovic, Srdjan; O’Donovan, Michael; Visscher, Peter M.; Andreassen, Ole A.; Dale, Anders M.

2016-01-01

Most of the genetic architecture of schizophrenia (SCZ) has not yet been identified. Here, we apply a novel statistical algorithm called Covariate-Modulated Mixture Modeling (CM3), which incorporates auxiliary information (heterozygosity, total linkage disequilibrium, genomic annotations, pleiotropy) for each single nucleotide polymorphism (SNP) to enable more accurate estimation of replication probabilities, conditional on the observed test statistic (“z-score”) of the SNP. We use a multiple logistic regression on z-scores to combine information from auxiliary information to derive a “relative enrichment score” for each SNP. For each stratum of these relative enrichment scores, we obtain nonparametric estimates of posterior expected test statistics and replication probabilities as a function of discovery z-scores, using a resampling-based approach that repeatedly and randomly partitions meta-analysis sub-studies into training and replication samples. We fit a scale mixture of two Gaussians model to each stratum, obtaining parameter estimates that minimize the sum of squared differences of the scale-mixture model with the stratified nonparametric estimates. We apply this approach to the recent genome-wide association study (GWAS) of SCZ (n = 82,315), obtaining a good fit between the model-based and observed effect sizes and replication probabilities. We observed that SNPs with low enrichment scores replicate with a lower probability than SNPs with high enrichment scores even when both they are genome-wide significant (p < 5x10-8). There were 693 and 219 independent loci with model-based replication rates ≥80% and ≥90%, respectively. Compared to analyses not incorporating relative enrichment scores, CM3 increased out-of-sample yield for SNPs that replicate at a given rate. This demonstrates that replication probabilities can be more accurately estimated using prior enrichment information with CM3. PMID:26808560

Probability estimates of seismic event occurrence compared to health hazards - Forecasting Taipei's Earthquakes

NASA Astrophysics Data System (ADS)

Fung, D. C. N.; Wang, J. P.; Chang, S. H.; Chang, S. C.

2014-12-01

Using a revised statistical model built on past seismic probability models, the probability of different magnitude earthquakes occurring within variable timespans can be estimated. The revised model is based on Poisson distribution and includes the use of best-estimate values of the probability distribution of different magnitude earthquakes recurring from a fault from literature sources. Our study aims to apply this model to the Taipei metropolitan area with a population of 7 million, which lies in the Taipei Basin and is bounded by two normal faults: the Sanchaio and Taipei faults. The Sanchaio fault is suggested to be responsible for previous large magnitude earthquakes, such as the 1694 magnitude 7 earthquake in northwestern Taipei (Cheng et. al., 2010). Based on a magnitude 7 earthquake return period of 543 years, the model predicts the occurrence of a magnitude 7 earthquake within 20 years at 1.81%, within 79 years at 6.77% and within 300 years at 21.22%. These estimates increase significantly when considering a magnitude 6 earthquake; the chance of one occurring within the next 20 years is estimated to be 3.61%, 79 years at 13.54% and 300 years at 42.45%. The 79 year period represents the average lifespan of the Taiwan population. In contrast, based on data from 2013, the probability of Taiwan residents experiencing heart disease or malignant neoplasm is 11.5% and 29%. The inference of this study is that the calculated risk that the Taipei population is at from a potentially damaging magnitude 6 or greater earthquake occurring within their lifetime is just as great as of suffering from a heart attack or other health ailments.

The relationship study between image features and detection probability based on psychology experiments

NASA Astrophysics Data System (ADS)

Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

2011-04-01

Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

PDF-based heterogeneous multiscale filtration model.

PubMed

Gong, Jian; Rutland, Christopher J

2015-04-21

Motivated by modeling of gasoline particulate filters (GPFs), a probability density function (PDF) based heterogeneous multiscale filtration (HMF) model is developed to calculate filtration efficiency of clean particulate filters. A new methodology based on statistical theory and classic filtration theory is developed in the HMF model. Based on the analysis of experimental porosimetry data, a pore size probability density function is introduced to represent heterogeneity and multiscale characteristics of the porous wall. The filtration efficiency of a filter can be calculated as the sum of the contributions of individual collectors. The resulting HMF model overcomes the limitations of classic mean filtration models which rely on tuning of the mean collector size. Sensitivity analysis shows that the HMF model recovers the classical mean model when the pore size variance is very small. The HMF model is validated by fundamental filtration experimental data from different scales of filter samples. The model shows a good agreement with experimental data at various operating conditions. The effects of the microstructure of filters on filtration efficiency as well as the most penetrating particle size are correctly predicted by the model.

Theoretical aspects and modelling of cellular decision making, cell killing and information-processing in photodynamic therapy of cancer.

PubMed

Gkigkitzis, Ioannis

2013-01-01

The aim of this report is to provide a mathematical model of the mechanism for making binary fate decisions about cell death or survival, during and after Photodynamic Therapy (PDT) treatment, and to supply the logical design for this decision mechanism as an application of rate distortion theory to the biochemical processing of information by the physical system of a cell. Based on system biology models of the molecular interactions involved in the PDT processes previously established, and regarding a cellular decision-making system as a noisy communication channel, we use rate distortion theory to design a time dependent Blahut-Arimoto algorithm where the input is a stimulus vector composed of the time dependent concentrations of three PDT related cell death signaling molecules and the output is a cell fate decision. The molecular concentrations are determined by a group of rate equations. The basic steps are: initialize the probability of the cell fate decision, compute the conditional probability distribution that minimizes the mutual information between input and output, compute the cell probability of cell fate decision that minimizes the mutual information and repeat the last two steps until the probabilities converge. Advance to the next discrete time point and repeat the process. Based on the model from communication theory described in this work, and assuming that the activation of the death signal processing occurs when any of the molecular stimulants increases higher than a predefined threshold (50% of the maximum concentrations), for 1800s of treatment, the cell undergoes necrosis within the first 30 minutes with probability range 90.0%-99.99% and in the case of repair/survival, it goes through apoptosis within 3-4 hours with probability range 90.00%-99.00%. Although, there is no experimental validation of the model at this moment, it reproduces some patterns of survival ratios of predicted experimental data. Analytical modeling based on cell death signaling molecules has been shown to be an independent and useful tool for prediction of cell surviving response to PDT. The model can be adjusted to provide important insights for cellular response to other treatments such as hyperthermia, and diseases such as neurodegeneration.

Comparison of the Mortality Probability Admission Model III, National Quality Forum, and Acute Physiology and Chronic Health Evaluation IV hospital mortality models: implications for national benchmarking*.

PubMed

Kramer, Andrew A; Higgins, Thomas L; Zimmerman, Jack E

2014-03-01

To examine the accuracy of the original Mortality Probability Admission Model III, ICU Outcomes Model/National Quality Forum modification of Mortality Probability Admission Model III, and Acute Physiology and Chronic Health Evaluation IVa models for comparing observed and risk-adjusted hospital mortality predictions. Retrospective paired analyses of day 1 hospital mortality predictions using three prognostic models. Fifty-five ICUs at 38 U.S. hospitals from January 2008 to December 2012. Among 174,001 intensive care admissions, 109,926 met model inclusion criteria and 55,304 had data for mortality prediction using all three models. None. We compared patient exclusions and the discrimination, calibration, and accuracy for each model. Acute Physiology and Chronic Health Evaluation IVa excluded 10.7% of all patients, ICU Outcomes Model/National Quality Forum 20.1%, and Mortality Probability Admission Model III 24.1%. Discrimination of Acute Physiology and Chronic Health Evaluation IVa was superior with area under receiver operating curve (0.88) compared with Mortality Probability Admission Model III (0.81) and ICU Outcomes Model/National Quality Forum (0.80). Acute Physiology and Chronic Health Evaluation IVa was better calibrated (lowest Hosmer-Lemeshow statistic). The accuracy of Acute Physiology and Chronic Health Evaluation IVa was superior (adjusted Brier score = 31.0%) to that for Mortality Probability Admission Model III (16.1%) and ICU Outcomes Model/National Quality Forum (17.8%). Compared with observed mortality, Acute Physiology and Chronic Health Evaluation IVa overpredicted mortality by 1.5% and Mortality Probability Admission Model III by 3.1%; ICU Outcomes Model/National Quality Forum underpredicted mortality by 1.2%. Calibration curves showed that Acute Physiology and Chronic Health Evaluation performed well over the entire risk range, unlike the Mortality Probability Admission Model and ICU Outcomes Model/National Quality Forum models. Acute Physiology and Chronic Health Evaluation IVa had better accuracy within patient subgroups and for specific admission diagnoses. Acute Physiology and Chronic Health Evaluation IVa offered the best discrimination and calibration on a large common dataset and excluded fewer patients than Mortality Probability Admission Model III or ICU Outcomes Model/National Quality Forum. The choice of ICU performance benchmarks should be based on a comparison of model accuracy using data for identical patients.

Performance analysis of OOK-based FSO systems in Gamma-Gamma turbulence with imprecise channel models

NASA Astrophysics Data System (ADS)

Feng, Jianfeng; Zhao, Xiaohui

2017-11-01

For an FSO communication system with imprecise channel model, we investigate its system performance based on outage probability, average BEP and ergodic capacity. The exact FSO links are modeled as Gamma-Gamma fading channel in consideration of both atmospheric turbulence and pointing errors, and the imprecise channel model is treated as the superposition of exact channel gain and a Gaussian random variable. After we derive the PDF, CDF and nth moment of the imprecise channel gain, and based on these statistics the expressions for the outage probability, the average BEP and the ergodic capacity in terms of the Meijer's G functions are obtained. Both numerical and analytical results are presented. The simulation results show that the communication performance deteriorates in the imprecise channel model, and approaches to the exact performance curves as the channel model becomes accurate.

Two models for microsimulation of family life cycle and family structure.

PubMed

Bertino, S; Pinnelli, A; Vichi, M

1988-01-01

2 models are proposed for the microsimulation of the family and analysis of family structure and life cycle. These models were devised primarily for teaching purposes. The families are composed of 3 generations (parents, grandparents, children). Cohabitation is not considered. The 1st model is governed by a transition mechanism based on the rules of a Markov multidimensional, nonhonogeneous chain. The 2nd model is based on stochastic point processes. Input data comprise annual mortality probability according to 1) sex, 2) age, 3) civil status, 4) annual probability of 1st marriage, 5) age combinations between the spouses, and 6) the probability of having 1, 2, or 3 children at 6 months intervals from the previous event (marriage or birth of nth child). The applications of the 1st model are presented using 2 mortality and fertility hypotheses (high and low) and a nuptiality hypothesis (West European nature). The various features of family composition are analyzed according to the duration of a couple's marriage and the age of the individual, as well as the characteristic features of the individual and family life cycle given these 2 demographic conditions.

A Probabilistic Model for Predicting Attenuation of Viruses During Percolation in Unsaturated Natural Barriers

NASA Astrophysics Data System (ADS)

Faulkner, B. R.; Lyon, W. G.

2001-12-01

We present a probabilistic model for predicting virus attenuation. The solution employs the assumption of complete mixing. Monte Carlo methods are used to generate ensemble simulations of virus attenuation due to physical, biological, and chemical factors. The model generates a probability of failure to achieve 4-log attenuation. We tabulated data from related studies to develop probability density functions for input parameters, and utilized a database of soil hydraulic parameters based on the 12 USDA soil categories. Regulators can use the model based on limited information such as boring logs, climate data, and soil survey reports for a particular site of interest. Plackett-Burman sensitivity analysis indicated the most important main effects on probability of failure to achieve 4-log attenuation in our model were mean logarithm of saturated hydraulic conductivity (+0.396), mean water content (+0.203), mean solid-water mass transfer coefficient (-0.147), and the mean solid-water equilibrium partitioning coefficient (-0.144). Using the model, we predicted the probability of failure of a one-meter thick proposed hydrogeologic barrier and a water content of 0.3. With the currently available data and the associated uncertainty, we predicted soils classified as sand would fail (p=0.999), silt loams would also fail (p=0.292), but soils classified as clays would provide the required 4-log attenuation (p=0.001). The model is extendible in the sense that probability density functions of parameters can be modified as future studies refine the uncertainty, and the lightweight object-oriented design of the computer model (implemented in Java) will facilitate reuse with modified classes. This is an abstract of a proposed presentation and does not necessarily reflect EPA policy.

Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition.

PubMed

Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen

2017-12-27

Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model. The BayesC model assumes a priori that markers have normally distributed effects with probability [Formula: see text] and no effect with probability (1 - [Formula: see text]). Marker effects and their PEV are estimated by using SVD and the posterior probability of the marker having a non-zero effect is calculated. These posterior probabilities are used to obtain marker-specific effect variances, which are subsequently used to approximate BayesC estimates of marker effects in a linear model. A computer simulation study was conducted to compare alternative genomic prediction methods, where a single reference generation was used to estimate marker effects, which were subsequently used for 10 generations of forward prediction, for which accuracies were evaluated. SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP effects (SNP-BLUP model). When reducing marker density from WGS data to 30 K, SNP-BLUP tended to yield the highest accuracies, at least in the short term. Based on SVD of the genotype matrix, we developed a direct method for the calculation of BayesC estimates of marker effects. Although SVD- and MCMC-based marker effects differed slightly, their prediction accuracies were similar. Assuming that the SVD of the marker genotype matrix is already performed for other reasons (e.g. for SNP-BLUP), computation times for the BayesC predictions were comparable to those of SNP-BLUP.

Sequential Probability Ratio Test for Collision Avoidance Maneuver Decisions Based on a Bank of Norm-Inequality-Constrained Epoch-State Filters

NASA Technical Reports Server (NTRS)

Carpenter, J. R.; Markley, F. L.; Alfriend, K. T.; Wright, C.; Arcido, J.

2011-01-01

Sequential probability ratio tests explicitly allow decision makers to incorporate false alarm and missed detection risks, and are potentially less sensitive to modeling errors than a procedure that relies solely on a probability of collision threshold. Recent work on constrained Kalman filtering has suggested an approach to formulating such a test for collision avoidance maneuver decisions: a filter bank with two norm-inequality-constrained epoch-state extended Kalman filters. One filter models 1he null hypothesis 1ha1 the miss distance is inside the combined hard body radius at the predicted time of closest approach, and one filter models the alternative hypothesis. The epoch-state filter developed for this method explicitly accounts for any process noise present in the system. The method appears to work well using a realistic example based on an upcoming highly-elliptical orbit formation flying mission.

Blocking probability in the hose-model optical VPN with different number of wavelengths

NASA Astrophysics Data System (ADS)

Roslyakov, Alexander V.

2017-04-01

Connection setup with guaranteed quality of service (QoS) in the optical virtual private network (OVPN) is a major goal for the network providers. In order to support this we propose a QoS based OVPN connection set up mechanism over WDM network to the end customer. The proposed WDM network model can be specified in terms of QoS parameter such as blocking probability. We estimated this QoS parameter based on the hose-model OVPN. In this mechanism the OVPN connections also can be created or deleted according to the availability of the wavelengths in the optical path. In this paper we have considered the impact of the number of wavelengths on the computation of blocking probability. The goal of the work is to dynamically provide a best OVPN connection during frequent arrival of connection requests with QoS requirements.

Transition probabilities of health states for workers in Malaysia using a Markov chain model

NASA Astrophysics Data System (ADS)

Samsuddin, Shamshimah; Ismail, Noriszura

2017-04-01

The aim of our study is to estimate the transition probabilities of health states for workers in Malaysia who contribute to the Employment Injury Scheme under the Social Security Organization Malaysia using the Markov chain model. Our study uses four states of health (active, temporary disability, permanent disability and death) based on the data collected from the longitudinal studies of workers in Malaysia for 5 years. The transition probabilities vary by health state, age and gender. The results show that men employees are more likely to have higher transition probabilities to any health state compared to women employees. The transition probabilities can be used to predict the future health of workers in terms of a function of current age, gender and health state.

Tests for senescent decline in annual survival probabilities of common pochards, Aythya ferina

USGS Publications Warehouse

Nichols, J.D.; Hines, J.E.; Blums, P.

1997-01-01

Senescent decline in survival probabilities of animals is a topic about which much has been written but little is known. Here, we present formal tests of senescence hypotheses, using 1373 recaptures from 8877 duckling (age 0) and 504 yearling Common Pochards (Aythya ferina) banded at a Latvian study site, 1975-1992. The tests are based on capture-recapture models that explicitly incorporate sampling probabilities that, themselves, may exhibit timeand age-specific variation. The tests provided no evidence of senescent decline in survival probabilities for this species. Power of the most useful test was low for gradual declines in annual survival probability with age, but good for steeper declines. We recommend use of this type of capture-recapture modeling and analysis for other investigations of senescence in animal survival rates.

Only the Carrot, Not the Stick: Incorporating Trust into the Enforcement of Regulation

PubMed Central

Mendoza, Juan P.; Wielhouwer, Jacco L.

2015-01-01

New enforcement strategies allow agents to gain the regulator’s trust and consequently face a lower audit probability. Prior research suggests that, in order to prevent lower compliance, a reduction in the audit probability (the “carrot”) must be compensated with the introduction of a higher penalty for non-compliance (the “stick”). However, such carrot-and-stick strategies reflect neither the concept of trust nor the strategies observed in practice. In response to this, we define trust-based regulation as a strategy that incorporates rules that allow trust to develop, and using a generic (non-cooperative) game of tax compliance, we examine whether trust-based regulation is feasible (i.e., whether, in equilibrium, a reduction in the audit probability, without ever increasing the penalty for non-compliance, does not lead to reduced compliance). The model shows that trust-based regulation is feasible when the agent sufficiently values the future. In line with the concept of trust, this strategy is feasible when the regulator is uncertain about the agent’s intentions. Moreover, the model shows that (i) introducing higher penalties makes trust-based regulation less feasible, and (ii) combining trust and forgiveness can lead to a lower audit probability for both trusted and distrusted agents. Policy recommendations often point toward increasing deterrence. This model shows that the opposite can be optimal. PMID:25705898

[Development of Markov models for economics evaluation of strategies on hepatitis B vaccination and population-based antiviral treatment in China].

PubMed

Yang, P C; Zhang, S X; Sun, P P; Cai, Y L; Lin, Y; Zou, Y H

2017-07-10

Objective: To construct the Markov models to reflect the reality of prevention and treatment interventions against hepatitis B virus (HBV) infection, simulate the natural history of HBV infection in different age groups and provide evidence for the economics evaluations of hepatitis B vaccination and population-based antiviral treatment in China. Methods: According to the theory and techniques of Markov chain, the Markov models of Chinese HBV epidemic were developed based on the national data and related literature both at home and abroad, including the settings of Markov model states, allowable transitions and initial and transition probabilities. The model construction, operation and verification were conducted by using software TreeAge Pro 2015. Results: Several types of Markov models were constructed to describe the disease progression of HBV infection in neonatal period, perinatal period or adulthood, the progression of chronic hepatitis B after antiviral therapy, hepatitis B prevention and control in adults, chronic hepatitis B antiviral treatment and the natural progression of chronic hepatitis B in general population. The model for the newborn was fundamental which included ten states, i.e . susceptiblity to HBV, HBsAg clearance, immune tolerance, immune clearance, low replication, HBeAg negative CHB, compensated cirrhosis, decompensated cirrhosis, hepatocellular carcinoma (HCC) and death. The susceptible state to HBV was excluded in the perinatal period model, and the immune tolerance state was excluded in the adulthood model. The model for general population only included two states, survive and death. Among the 5 types of models, there were 9 initial states assigned with initial probabilities, and 27 states for transition probabilities. The results of model verifications showed that the probability curves were basically consistent with the situation of HBV epidemic in China. Conclusion: The Markov models developed can be used in economics evaluation of hepatitis B vaccination and treatment for the elimination of HBV infection in China though the structures and parameters in the model have uncertainty with dynamic natures.

Maximum parsimony, substitution model, and probability phylogenetic trees.

PubMed

Weng, J F; Thomas, D A; Mareels, I

2011-01-01

The problem of inferring phylogenies (phylogenetic trees) is one of the main problems in computational biology. There are three main methods for inferring phylogenies-Maximum Parsimony (MP), Distance Matrix (DM) and Maximum Likelihood (ML), of which the MP method is the most well-studied and popular method. In the MP method the optimization criterion is the number of substitutions of the nucleotides computed by the differences in the investigated nucleotide sequences. However, the MP method is often criticized as it only counts the substitutions observable at the current time and all the unobservable substitutions that really occur in the evolutionary history are omitted. In order to take into account the unobservable substitutions, some substitution models have been established and they are now widely used in the DM and ML methods but these substitution models cannot be used within the classical MP method. Recently the authors proposed a probability representation model for phylogenetic trees and the reconstructed trees in this model are called probability phylogenetic trees. One of the advantages of the probability representation model is that it can include a substitution model to infer phylogenetic trees based on the MP principle. In this paper we explain how to use a substitution model in the reconstruction of probability phylogenetic trees and show the advantage of this approach with examples.

Tracking Expected Improvements of Decadal Prediction in Climate Services

NASA Astrophysics Data System (ADS)

Suckling, E.; Thompson, E.; Smith, L. A.

2013-12-01

Physics-based simulation models are ultimately expected to provide the best available (decision-relevant) probabilistic climate predictions, as they can capture the dynamics of the Earth System across a range of situations, situations for which observations for the construction of empirical models are scant if not nonexistent. This fact in itself provides neither evidence that predictions from today's Earth Systems Models will outperform today's empirical models, nor a guide to the space and time scales on which today's model predictions are adequate for a given purpose. Empirical (data-based) models are employed to make probability forecasts on decadal timescales. The skill of these forecasts is contrasted with that of state-of-the-art climate models, and the challenges faced by each approach are discussed. The focus is on providing decision-relevant probability forecasts for decision support. An empirical model, known as Dynamic Climatology is shown to be competitive with CMIP5 climate models on decadal scale probability forecasts. Contrasting the skill of simulation models not only with each other but also with empirical models can reveal the space and time scales on which a generation of simulation models exploits their physical basis effectively. It can also quantify their ability to add information in the formation of operational forecasts. Difficulties (i) of information contamination (ii) of the interpretation of probabilistic skill and (iii) of artificial skill complicate each modelling approach, and are discussed. "Physics free" empirical models provide fixed, quantitative benchmarks for the evaluation of ever more complex climate models, that is not available from (inter)comparisons restricted to only complex models. At present, empirical models can also provide a background term for blending in the formation of probability forecasts from ensembles of simulation models. In weather forecasting this role is filled by the climatological distribution, and can significantly enhance the value of longer lead-time weather forecasts to those who use them. It is suggested that the direct comparison of simulation models with empirical models become a regular component of large model forecast intercomparison and evaluation. This would clarify the extent to which a given generation of state-of-the-art simulation models provide information beyond that available from simpler empirical models. It would also clarify current limitations in using simulation forecasting for decision support. No model-based probability forecast is complete without a quantitative estimate if its own irrelevance; this estimate is likely to increase as a function of lead time. A lack of decision-relevant quantitative skill would not bring the science-based foundation of anthropogenic warming into doubt. Similar levels of skill with empirical models does suggest a clear quantification of limits, as a function of lead time, for spatial and temporal scales on which decisions based on such model output are expected to prove maladaptive. Failing to clearly state such weaknesses of a given generation of simulation models, while clearly stating their strength and their foundation, risks the credibility of science in support of policy in the long term.

Plant calendar pattern based on rainfall forecast and the probability of its success in Deli Serdang regency of Indonesia

NASA Astrophysics Data System (ADS)

Darnius, O.; Sitorus, S.

2018-03-01

The objective of this study was to determine the pattern of plant calendar of three types of crops; namely, palawija, rice, andbanana, based on rainfall in Deli Serdang Regency. In the first stage, we forecasted rainfall by using time series analysis, and obtained appropriate model of ARIMA (1,0,0) (1,1,1)12. Based on the forecast result, we designed a plant calendar pattern for the three types of plant. Furthermore, the probability of success in the plant types following the plant calendar pattern was calculated by using the Markov process by discretizing the continuous rainfall data into three categories; namely, Below Normal (BN), Normal (N), and Above Normal (AN) to form the probability transition matrix. Finally, the combination of rainfall forecasting models and the Markov process were used to determine the pattern of cropping calendars and the probability of success in the three crops. This research used rainfall data of Deli Serdang Regency taken from the office of BMKG (Meteorologist Climatology and Geophysics Agency), Sampali Medan, Indonesia.

Calibrating perceived understanding and competency in probability concepts: A diagnosis of learning difficulties based on Rasch probabilistic model

NASA Astrophysics Data System (ADS)

Mahmud, Zamalia; Porter, Anne; Salikin, Masniyati; Ghani, Nor Azura Md

2015-12-01

Students' understanding of probability concepts have been investigated from various different perspectives. Competency on the other hand is often measured separately in the form of test structure. This study was set out to show that perceived understanding and competency can be calibrated and assessed together using Rasch measurement tools. Forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW have volunteered to participate in the study. Rasch measurement which is based on a probabilistic model is used to calibrate the responses from two survey instruments and investigate the interactions between them. Data were captured from the e-learning platform Moodle where students provided their responses through an online quiz. The study shows that majority of the students perceived little understanding about conditional and independent events prior to learning about it but tend to demonstrate a slightly higher competency level afterward. Based on the Rasch map, there is indication of some increase in learning and knowledge about some probability concepts at the end of the two weeks lessons on probability concepts.

Generalized Wishart Mixtures for Unsupervised Classification of PolSAR Data

NASA Astrophysics Data System (ADS)

Li, Lan; Chen, Erxue; Li, Zengyuan

2013-01-01

This paper presents an unsupervised clustering algorithm based upon the expectation maximization (EM) algorithm for finite mixture modelling, using the complex wishart probability density function (PDF) for the probabilities. The mixture model enables to consider heterogeneous thematic classes which could not be better fitted by the unimodal wishart distribution. In order to make it fast and robust to calculate, we use the recently proposed generalized gamma distribution (GΓD) for the single polarization intensity data to make the initial partition. Then we use the wishart probability density function for the corresponding sample covariance matrix to calculate the posterior class probabilities for each pixel. The posterior class probabilities are used for the prior probability estimates of each class and weights for all class parameter updates. The proposed method is evaluated and compared with the wishart H-Alpha-A classification. Preliminary results show that the proposed method has better performance.

Multinomial mixture model with heterogeneous classification probabilities

USGS Publications Warehouse

Holland, M.D.; Gray, B.R.

2011-01-01

Royle and Link (Ecology 86(9):2505-2512, 2005) proposed an analytical method that allowed estimation of multinomial distribution parameters and classification probabilities from categorical data measured with error. While useful, we demonstrate algebraically and by simulations that this method yields biased multinomial parameter estimates when the probabilities of correct category classifications vary among sampling units. We address this shortcoming by treating these probabilities as logit-normal random variables within a Bayesian framework. We use Markov chain Monte Carlo to compute Bayes estimates from a simulated sample from the posterior distribution. Based on simulations, this elaborated Royle-Link model yields nearly unbiased estimates of multinomial and correct classification probability estimates when classification probabilities are allowed to vary according to the normal distribution on the logit scale or according to the Beta distribution. The method is illustrated using categorical submersed aquatic vegetation data. ?? 2010 Springer Science+Business Media, LLC.

Assessment of the probability of contaminating Mars

NASA Technical Reports Server (NTRS)

Judd, B. R.; North, D. W.; Pezier, J. P.

1974-01-01

New methodology is proposed to assess the probability that the planet Mars will by biologically contaminated by terrestrial microorganisms aboard a spacecraft. Present NASA methods are based on the Sagan-Coleman formula, which states that the probability of contamination is the product of the expected microbial release and a probability of growth. The proposed new methodology extends the Sagan-Coleman approach to permit utilization of detailed information on microbial characteristics, the lethality of release and transport mechanisms, and of other information about the Martian environment. Three different types of microbial release are distinguished in the model for assessing the probability of contamination. The number of viable microbes released by each mechanism depends on the bio-burden in various locations on the spacecraft and on whether the spacecraft landing is accomplished according to plan. For each of the three release mechanisms a probability of growth is computed, using a model for transport into an environment suited to microbial growth.

Aerosol-type retrieval and uncertainty quantification from OMI data

NASA Astrophysics Data System (ADS)

Kauppi, Anu; Kolmonen, Pekka; Laine, Marko; Tamminen, Johanna

2017-11-01

We discuss uncertainty quantification for aerosol-type selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs) and top-of-atmosphere (TOA) spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval.This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions.The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the difficulty in model selection. The posterior probability distribution can provide a comprehensive characterisation of the uncertainty in this kind of problem for aerosol-type selection. As a result, the proposed method can account for the model error and also include the model selection uncertainty in the total uncertainty budget.

A model of airport security work flow based on petri net

NASA Astrophysics Data System (ADS)

Dong, Xinming

2017-09-01

Extremely long lines at airports in the United States have been sharply criticized. In order to find out the bottleneck in the existing security system and put forward reasonable improvement plans and proposal, the Petri net model and the Markov Chain are introduced in this paper. This paper uses data collected by transportation Security Agency (TSA), assuming the data can represent the average level of all airports in the Unites States, to analysis the performance of security check system. By calculating the busy probabilities and the utilization probabilities, the bottleneck is found. Moreover, recommendation is given based on the parameters’ modification in Petri net model.

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

PubMed Central

Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-01-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit. PMID:29765629

Accuracy of clinicians and models for estimating the probability that a pulmonary nodule is malignant.

PubMed

Balekian, Alex A; Silvestri, Gerard A; Simkovich, Suzanne M; Mestaz, Peter J; Sanders, Gillian D; Daniel, Jamie; Porcel, Jackie; Gould, Michael K

2013-12-01

Management of pulmonary nodules depends critically on the probability of malignancy. Models to estimate probability have been developed and validated, but most clinicians rely on judgment. The aim of this study was to compare the accuracy of clinical judgment with that of two prediction models. Physician participants reviewed up to five clinical vignettes, selected at random from a larger pool of 35 vignettes, all based on actual patients with lung nodules of known final diagnosis. Vignettes included clinical information and a representative slice from computed tomography. Clinicians estimated the probability of malignancy for each vignette. To examine agreement with models, we calculated intraclass correlation coefficients (ICC) and kappa statistics. To examine accuracy, we compared areas under the receiver operator characteristic curve (AUC). Thirty-six participants completed 179 vignettes, 47% of which described patients with malignant nodules. Agreement between participants and models was fair for the Mayo Clinic model (ICC, 0.37; 95% confidence interval [CI], 0.23-0.50) and moderate for the Veterans Affairs model (ICC, 0.46; 95% CI, 0.34-0.57). There was no difference in accuracy between participants (AUC, 0.70; 95% CI, 0.62-0.77) and the Mayo Clinic model (AUC, 0.71; 95% CI, 0.62-0.80; P = 0.90) or the Veterans Affairs model (AUC, 0.72; 95% CI, 0.64-0.80; P = 0.54). In this vignette-based study, clinical judgment and models appeared to have similar accuracy for lung nodule characterization, but agreement between judgment and the models was modest, suggesting that qualitative and quantitative approaches may provide complementary information.

Allocating Fire Mitigation Funds on the Basis of the Predicted Probabilities of Forest Wildfire

Treesearch

Ronald E. McRoberts; Greg C. Liknes; Mark D. Nelson; Krista M. Gebert; R. James Barbour; Susan L. Odell; Steven C. Yaddof

2005-01-01

A logistic regression model was used with map-based information to predict the probability of forest fire for forested areas of the United States. Model parameters were estimated using a digital layer depicting the locations of wildfires and satellite imagery depicting thermal hotspots. The area of the United States in the upper 50th percentile with respect to...

Masking by Gratings Predicted by an Image Sequence Discriminating Model: Testing Models for Perceptual Discrimination Using Repeatable Noise

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Null, Cynthia H. (Technical Monitor)

1998-01-01

Adding noise to stimuli to be discriminated allows estimation of observer classification functions based on the correlation between observer responses and relevant features of the noisy stimuli. Examples will be presented of stimulus features that are found in auditory tone detection and visual vernier acuity. using the standard signal detection model (Thurstone scaling), we derive formulas to estimate the proportion of the observers decision variable variance that is controlled by the added noise. one is based on the probability of agreement of the observer with him/herself on trials with the same noise sample. Another is based on the relative performance of the observer and the model. When these do not agree, the model can be rejected. A second derivation gives the probability of agreement of observer and model when the observer follows the model except for internal noise. Agreement significantly less than this amount allows rejection of the model.

Scenarios for Evolving Seismic Crises: Possible Communication Strategies

NASA Astrophysics Data System (ADS)

Steacy, S.

2015-12-01

Recent advances in operational earthquake forecasting mean that we are very close to being able to confidently compute changes in earthquake probability as seismic crises develop. For instance, we now have statistical models such as ETAS and STEP which demonstrate considerable skill in forecasting earthquake rates and recent advances in Coulomb based models are also showing much promise. Communicating changes in earthquake probability is likely be very difficult, however, as the absolute probability of a damaging event is likely to remain quite small despite a significant increase in the relative value. Here, we use a hybrid Coulomb/statistical model to compute probability changes for a series of earthquake scenarios in New Zealand. We discuss the strengths and limitations of the forecasts and suggest a number of possible mechanisms that might be used to communicate results in an actual developing seismic crisis.

Nonprobability and probability-based sampling strategies in sexual science.

PubMed

Catania, Joseph A; Dolcini, M Margaret; Orellana, Roberto; Narayanan, Vasudah

2015-01-01

With few exceptions, much of sexual science builds upon data from opportunistic nonprobability samples of limited generalizability. Although probability-based studies are considered the gold standard in terms of generalizability, they are costly to apply to many of the hard-to-reach populations of interest to sexologists. The present article discusses recent conclusions by sampling experts that have relevance to sexual science that advocates for nonprobability methods. In this regard, we provide an overview of Internet sampling as a useful, cost-efficient, nonprobability sampling method of value to sex researchers conducting modeling work or clinical trials. We also argue that probability-based sampling methods may be more readily applied in sex research with hard-to-reach populations than is typically thought. In this context, we provide three case studies that utilize qualitative and quantitative techniques directed at reducing limitations in applying probability-based sampling to hard-to-reach populations: indigenous Peruvians, African American youth, and urban men who have sex with men (MSM). Recommendations are made with regard to presampling studies, adaptive and disproportionate sampling methods, and strategies that may be utilized in evaluating nonprobability and probability-based sampling methods.

Stochastic optimal operation of reservoirs based on copula functions

NASA Astrophysics Data System (ADS)

Lei, Xiao-hui; Tan, Qiao-feng; Wang, Xu; Wang, Hao; Wen, Xin; Wang, Chao; Zhang, Jing-wen

2018-02-01

Stochastic dynamic programming (SDP) has been widely used to derive operating policies for reservoirs considering streamflow uncertainties. In SDP, there is a need to calculate the transition probability matrix more accurately and efficiently in order to improve the economic benefit of reservoir operation. In this study, we proposed a stochastic optimization model for hydropower generation reservoirs, in which 1) the transition probability matrix was calculated based on copula functions; and 2) the value function of the last period was calculated by stepwise iteration. Firstly, the marginal distribution of stochastic inflow in each period was built and the joint distributions of adjacent periods were obtained using the three members of the Archimedean copulas, based on which the conditional probability formula was derived. Then, the value in the last period was calculated by a simple recursive equation with the proposed stepwise iteration method and the value function was fitted with a linear regression model. These improvements were incorporated into the classic SDP and applied to the case study in Ertan reservoir, China. The results show that the transition probability matrix can be more easily and accurately obtained by the proposed copula function based method than conventional methods based on the observed or synthetic streamflow series, and the reservoir operation benefit can also be increased.

Detection of cat-eye effect echo based on unit APD

NASA Astrophysics Data System (ADS)

Wu, Dong-Sheng; Zhang, Peng; Hu, Wen-Gang; Ying, Jia-Ju; Liu, Jie

2016-10-01

The cat-eye effect echo of optical system can be detected based on CCD, but the detection range is limited within several kilometers. In order to achieve long-range even ultra-long-range detection, it ought to select APD as detector because of the high sensitivity of APD. The detection system of cat-eye effect echo based on unit APD is designed in paper. The implementation scheme and key technology of the detection system is presented. The detection performances of the detection system including detection range, detection probability and false alarm probability are modeled. Based on the model, the performances of the detection system are analyzed using typical parameters. The results of numerical calculation show that the echo signal-to-noise ratio is greater than six, the detection probability is greater than 99.9% and the false alarm probability is less tan 0.1% within 20 km detection range. In order to verify the detection effect, we built the experimental platform of detection system according to the design scheme and carry out the field experiments. The experimental results agree well with the results of numerical calculation, which prove that the detection system based on the unit APD is feasible to realize remote detection for cat-eye effect echo.

A Bayesian model averaging method for improving SMT phrase table

NASA Astrophysics Data System (ADS)

Duan, Nan

2013-03-01

Previous methods on improving translation quality by employing multiple SMT models usually carry out as a second-pass decision procedure on hypotheses from multiple systems using extra features instead of using features in existing models in more depth. In this paper, we propose translation model generalization (TMG), an approach that updates probability feature values for the translation model being used based on the model itself and a set of auxiliary models, aiming to alleviate the over-estimation problem and enhance translation quality in the first-pass decoding phase. We validate our approach for translation models based on auxiliary models built by two different ways. We also introduce novel probability variance features into the log-linear models for further improvements. We conclude our approach can be developed independently and integrated into current SMT pipeline directly. We demonstrate BLEU improvements on the NIST Chinese-to-English MT tasks for single-system decodings.

Ordinal probability effect measures for group comparisons in multinomial cumulative link models.

PubMed

Agresti, Alan; Kateri, Maria

2017-03-01

We consider simple ordinal model-based probability effect measures for comparing distributions of two groups, adjusted for explanatory variables. An "ordinal superiority" measure summarizes the probability that an observation from one distribution falls above an independent observation from the other distribution, adjusted for explanatory variables in a model. The measure applies directly to normal linear models and to a normal latent variable model for ordinal response variables. It equals Φ(β/2) for the corresponding ordinal model that applies a probit link function to cumulative multinomial probabilities, for standard normal cdf Φ and effect β that is the coefficient of the group indicator variable. For the more general latent variable model for ordinal responses that corresponds to a linear model with other possible error distributions and corresponding link functions for cumulative multinomial probabilities, the ordinal superiority measure equals exp(β)/[1+exp(β)] with the log-log link and equals approximately exp(β/2)/[1+exp(β/2)] with the logit link, where β is the group effect. Another ordinal superiority measure generalizes the difference of proportions from binary to ordinal responses. We also present related measures directly for ordinal models for the observed response that need not assume corresponding latent response models. We present confidence intervals for the measures and illustrate with an example. © 2016, The International Biometric Society.

Time dependent data, time independent models: challenges of updating Australia's National Seismic Hazard Assessment

NASA Astrophysics Data System (ADS)

Griffin, J.; Clark, D.; Allen, T.; Ghasemi, H.; Leonard, M.

2017-12-01

Standard probabilistic seismic hazard assessment (PSHA) simulates earthquake occurrence as a time-independent process. However paleoseismic studies in slowly deforming regions such as Australia show compelling evidence that large earthquakes on individual faults cluster within active periods, followed by long periods of quiescence. Therefore the instrumental earthquake catalog, which forms the basis of PSHA earthquake recurrence calculations, may only capture the state of the system over the period of the catalog. Together this means that data informing our PSHA may not be truly time-independent. This poses challenges in developing PSHAs for typical design probabilities (such as 10% in 50 years probability of exceedance): Is the present state observed through the instrumental catalog useful for estimating the next 50 years of earthquake hazard? Can paleo-earthquake data, that shows variations in earthquake frequency over time-scales of 10,000s of years or more, be robustly included in such PSHA models? Can a single PSHA logic tree be useful over a range of different probabilities of exceedance? In developing an updated PSHA for Australia, decadal-scale data based on instrumental earthquake catalogs (i.e. alternative area based source models and smoothed seismicity models) is integrated with paleo-earthquake data through inclusion of a fault source model. Use of time-dependent non-homogeneous Poisson models allows earthquake clustering to be modeled on fault sources with sufficient paleo-earthquake data. This study assesses the performance of alternative models by extracting decade-long segments of the instrumental catalog, developing earthquake probability models based on the remaining catalog, and testing performance against the extracted component of the catalog. Although this provides insights into model performance over the short-term, for longer timescales it is recognised that model choice is subject to considerable epistemic uncertainty. Therefore a formal expert elicitation process has been used to assign weights to alternative models for the 2018 update to Australia's national PSHA.

Accounting for Incomplete Species Detection in Fish Community Monitoring

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

McManamay, Ryan A; Orth, Dr. Donald J; Jager, Yetta

2013-01-01

Riverine fish assemblages are heterogeneous and very difficult to characterize with a one-size-fits-all approach to sampling. Furthermore, detecting changes in fish assemblages over time requires accounting for variation in sampling designs. We present a modeling approach that permits heterogeneous sampling by accounting for site and sampling covariates (including method) in a model-based framework for estimation (versus a sampling-based framework). We snorkeled during three surveys and electrofished during a single survey in suite of delineated habitats stratified by reach types. We developed single-species occupancy models to determine covariates influencing patch occupancy and species detection probabilities whereas community occupancy models estimated speciesmore » richness in light of incomplete detections. For most species, information-theoretic criteria showed higher support for models that included patch size and reach as covariates of occupancy. In addition, models including patch size and sampling method as covariates of detection probabilities also had higher support. Detection probability estimates for snorkeling surveys were higher for larger non-benthic species whereas electrofishing was more effective at detecting smaller benthic species. The number of sites and sampling occasions required to accurately estimate occupancy varied among fish species. For rare benthic species, our results suggested that higher number of occasions, and especially the addition of electrofishing, may be required to improve detection probabilities and obtain accurate occupancy estimates. Community models suggested that richness was 41% higher than the number of species actually observed and the addition of an electrofishing survey increased estimated richness by 13%. These results can be useful to future fish assemblage monitoring efforts by informing sampling designs, such as site selection (e.g. stratifying based on patch size) and determining effort required (e.g. number of sites versus occasions).« less

Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

NASA Technical Reports Server (NTRS)

Jaworske, D. A.; Myers, J. G.; Goodenow, D.; Young, M.; Arellano, J. D.

2016-01-01

Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.

Wildfire Risk Mapping over the State of Mississippi: Land Surface Modeling Approach

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

Cooke, William H.; Mostovoy, Georgy; Anantharaj, Valentine G

2012-01-01

Three fire risk indexes based on soil moisture estimates were applied to simulate wildfire probability over the southern part of Mississippi using the logistic regression approach. The fire indexes were retrieved from: (1) accumulated difference between daily precipitation and potential evapotranspiration (P-E); (2) top 10 cm soil moisture content simulated by the Mosaic land surface model; and (3) the Keetch-Byram drought index (KBDI). The P-E, KBDI, and soil moisture based indexes were estimated from gridded atmospheric and Mosaic-simulated soil moisture data available from the North American Land Data Assimilation System (NLDAS-2). Normalized deviations of these indexes from the 31-year meanmore » (1980-2010) were fitted into the logistic regression model describing probability of wildfires occurrence as a function of the fire index. It was assumed that such normalization provides more robust and adequate description of temporal dynamics of soil moisture anomalies than the original (not normalized) set of indexes. The logistic model parameters were evaluated for 0.25 x0.25 latitude/longitude cells and for probability representing at least one fire event occurred during 5 consecutive days. A 23-year (1986-2008) forest fires record was used. Two periods were selected and examined (January mid June and mid September December). The application of the logistic model provides an overall good agreement between empirical/observed and model-fitted fire probabilities over the study area during both seasons. The fire risk indexes based on the top 10 cm soil moisture and KBDI have the largest impact on the wildfire odds (increasing it by almost 2 times in response to each unit change of the corresponding fire risk index during January mid June period and by nearly 1.5 times during mid September-December) observed over 0.25 x0.25 cells located along the state of Mississippi Coast line. This result suggests a rather strong control of fire risk indexes on fire occurrence probability over this region.« less

A quadrature based method of moments for nonlinear Fokker-Planck equations

NASA Astrophysics Data System (ADS)

Otten, Dustin L.; Vedula, Prakash

2011-09-01

Fokker-Planck equations which are nonlinear with respect to their probability densities and occur in many nonequilibrium systems relevant to mean field interaction models, plasmas, fermions and bosons can be challenging to solve numerically. To address some underlying challenges, we propose the application of the direct quadrature based method of moments (DQMOM) for efficient and accurate determination of transient (and stationary) solutions of nonlinear Fokker-Planck equations (NLFPEs). In DQMOM, probability density (or other distribution) functions are represented using a finite collection of Dirac delta functions, characterized by quadrature weights and locations (or abscissas) that are determined based on constraints due to evolution of generalized moments. Three particular examples of nonlinear Fokker-Planck equations considered in this paper include descriptions of: (i) the Shimizu-Yamada model, (ii) the Desai-Zwanzig model (both of which have been developed as models of muscular contraction) and (iii) fermions and bosons. Results based on DQMOM, for the transient and stationary solutions of the nonlinear Fokker-Planck equations, have been found to be in good agreement with other available analytical and numerical approaches. It is also shown that approximate reconstruction of the underlying probability density function from moments obtained from DQMOM can be satisfactorily achieved using a maximum entropy method.

Probability density and exceedance rate functions of locally Gaussian turbulence

NASA Technical Reports Server (NTRS)

Mark, W. D.

1989-01-01

A locally Gaussian model of turbulence velocities is postulated which consists of the superposition of a slowly varying strictly Gaussian component representing slow temporal changes in the mean wind speed and a more rapidly varying locally Gaussian turbulence component possessing a temporally fluctuating local variance. Series expansions of the probability density and exceedance rate functions of the turbulence velocity model, based on Taylor's series, are derived. Comparisons of the resulting two-term approximations with measured probability density and exceedance rate functions of atmospheric turbulence velocity records show encouraging agreement, thereby confirming the consistency of the measured records with the locally Gaussian model. Explicit formulas are derived for computing all required expansion coefficients from measured turbulence records.

Simulation modeling of population viability for the leopard darter (Percidae: Percina pantherina)

USGS Publications Warehouse

Williams, L.R.; Echelle, A.A.; Toepfer, C.S.; Williams, M.G.; Fisher, W.L.

1999-01-01

We used the computer program RAMAS to perform a population viability analysis for the leopard darter, Percina pantherina. This percid fish is a threatened species confined to five isolated rivers in the Ouachita Mountains of Oklahoma and Arkansas. A base model created from life history data indicated a 6% probability that the leopard darter would go extinct in 50 years. We performed sensitivity analyses to determine the effects of initial population size, variation in age structure, variation in severity and probability of catastrophe, and migration rate. Catastrophe (modeled as the probability and severity of drought) and migration had the greatest effects on persistence. Results of these simulations have implications for management of this species.

Sustainable Odds

NASA Astrophysics Data System (ADS)

Smith, L. A.

2016-12-01

While probability forecasting has many philosophical and mathematical attractions, it is something of a dishonest nonsense if acting on such forecasts is expected to lead to rapid ruin. Model-based probabilities, when interpreted as actionable, are shown to lead to the rapid ruin of a cooperative entity offering odds interpreting the probability forecasts at face value. Arguably, these odds would not be considered "fair", but inasmuch as some definitions of "fair odds" include this case, this presentation will focus on "sustainable odds": Odds which are not expected to lead to the rapid ruin of the cooperative under the assumption that those placing bets have no information beyond that available to the forecast system. It is argued that sustainable odds will not correspond to probabilities outside the Perfect Model Scenario, that the "implied probabilities" determined from sustainable odds will always sum to more than one, and that the excess of this sum over one reflects the skill of the forecast system, being a quantitative measure of structural model error.

Modeling Stochastic Complexity in Complex Adaptive Systems: Non-Kolmogorov Probability and the Process Algebra Approach.

PubMed

Sulis, William H

2017-10-01

Walter Freeman III pioneered the application of nonlinear dynamical systems theories and methodologies in his work on mesoscopic brain dynamics.Sadly, mainstream psychology and psychiatry still cling to linear correlation based data analysis techniques, which threaten to subvert the process of experimentation and theory building. In order to progress, it is necessary to develop tools capable of managing the stochastic complexity of complex biopsychosocial systems, which includes multilevel feedback relationships, nonlinear interactions, chaotic dynamics and adaptability. In addition, however, these systems exhibit intrinsic randomness, non-Gaussian probability distributions, non-stationarity, contextuality, and non-Kolmogorov probabilities, as well as the absence of mean and/or variance and conditional probabilities. These properties and their implications for statistical analysis are discussed. An alternative approach, the Process Algebra approach, is described. It is a generative model, capable of generating non-Kolmogorov probabilities. It has proven useful in addressing fundamental problems in quantum mechanics and in the modeling of developing psychosocial systems.

Modeling stream fish distributions using interval-censored detection times.

PubMed

Ferreira, Mário; Filipe, Ana Filipa; Bardos, David C; Magalhães, Maria Filomena; Beja, Pedro

2016-08-01

Controlling for imperfect detection is important for developing species distribution models (SDMs). Occupancy-detection models based on the time needed to detect a species can be used to address this problem, but this is hindered when times to detection are not known precisely. Here, we extend the time-to-detection model to deal with detections recorded in time intervals and illustrate the method using a case study on stream fish distribution modeling. We collected electrofishing samples of six fish species across a Mediterranean watershed in Northeast Portugal. Based on a Bayesian hierarchical framework, we modeled the probability of water presence in stream channels, and the probability of species occupancy conditional on water presence, in relation to environmental and spatial variables. We also modeled time-to-first detection conditional on occupancy in relation to local factors, using modified interval-censored exponential survival models. Posterior distributions of occupancy probabilities derived from the models were used to produce species distribution maps. Simulations indicated that the modified time-to-detection model provided unbiased parameter estimates despite interval-censoring. There was a tendency for spatial variation in detection rates to be primarily influenced by depth and, to a lesser extent, stream width. Species occupancies were consistently affected by stream order, elevation, and annual precipitation. Bayesian P-values and AUCs indicated that all models had adequate fit and high discrimination ability, respectively. Mapping of predicted occupancy probabilities showed widespread distribution by most species, but uncertainty was generally higher in tributaries and upper reaches. The interval-censored time-to-detection model provides a practical solution to model occupancy-detection when detections are recorded in time intervals. This modeling framework is useful for developing SDMs while controlling for variation in detection rates, as it uses simple data that can be readily collected by field ecologists.

Using Landscape-Based Decision Rules to Prioritize Locations of Fuel Treatments in the Boreal Mixedwood of Western Canada

Treesearch

Marc-André Parisien; Dave R. Junor; Victor G. Kafka

2006-01-01

This study used a rule-based approach to prioritize locations of fuel treatments in the boreal mixedwood forest of western Canada. The burn probability (BP) in and around Prince Albert National Park in Saskatchewan was mapped using the Burn-P3 (Probability, Prediction, and Planning) model. Fuel treatment locations were determined according to three scenarios and five...

Stimulus probability effects in absolute identification.

PubMed

Kent, Christopher; Lamberts, Koen

2016-05-01

This study investigated the effect of stimulus presentation probability on accuracy and response times in an absolute identification task. Three schedules of presentation were used to investigate the interaction between presentation probability and stimulus position within the set. Data from individual participants indicated strong effects of presentation probability on both proportion correct and response times. The effects were moderated by the ubiquitous stimulus position effect. The accuracy and response time data were predicted by an exemplar-based model of perceptual cognition (Kent & Lamberts, 2005). The bow in discriminability was also attenuated when presentation probability for middle items was relatively high, an effect that will constrain future model development. The study provides evidence for item-specific learning in absolute identification. Implications for other theories of absolute identification are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Development of a voltage-dependent current noise algorithm for conductance-based stochastic modelling of auditory nerve fibres.

PubMed

Badenhorst, Werner; Hanekom, Tania; Hanekom, Johan J

2016-12-01

This study presents the development of an alternative noise current term and novel voltage-dependent current noise algorithm for conductance-based stochastic auditory nerve fibre (ANF) models. ANFs are known to have significant variance in threshold stimulus which affects temporal characteristics such as latency. This variance is primarily caused by the stochastic behaviour or microscopic fluctuations of the node of Ranvier's voltage-dependent sodium channels of which the intensity is a function of membrane voltage. Though easy to implement and low in computational cost, existing current noise models have two deficiencies: it is independent of membrane voltage, and it is unable to inherently determine the noise intensity required to produce in vivo measured discharge probability functions. The proposed algorithm overcomes these deficiencies while maintaining its low computational cost and ease of implementation compared to other conductance and Markovian-based stochastic models. The algorithm is applied to a Hodgkin-Huxley-based compartmental cat ANF model and validated via comparison of the threshold probability and latency distributions to measured cat ANF data. Simulation results show the algorithm's adherence to in vivo stochastic fibre characteristics such as an exponential relationship between the membrane noise and transmembrane voltage, a negative linear relationship between the log of the relative spread of the discharge probability and the log of the fibre diameter and a decrease in latency with an increase in stimulus intensity.

Probabilistic representation in syllogistic reasoning: A theory to integrate mental models and heuristics.

PubMed

Hattori, Masasi

2016-12-01

This paper presents a new theory of syllogistic reasoning. The proposed model assumes there are probabilistic representations of given signature situations. Instead of conducting an exhaustive search, the model constructs an individual-based "logical" mental representation that expresses the most probable state of affairs, and derives a necessary conclusion that is not inconsistent with the model using heuristics based on informativeness. The model is a unification of previous influential models. Its descriptive validity has been evaluated against existing empirical data and two new experiments, and by qualitative analyses based on previous empirical findings, all of which supported the theory. The model's behavior is also consistent with findings in other areas, including working memory capacity. The results indicate that people assume the probabilities of all target events mentioned in a syllogism to be almost equal, which suggests links between syllogistic reasoning and other areas of cognition. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

A method of real-time fault diagnosis for power transformers based on vibration analysis

NASA Astrophysics Data System (ADS)

Hong, Kaixing; Huang, Hai; Zhou, Jianping; Shen, Yimin; Li, Yujie

2015-11-01

In this paper, a novel probability-based classification model is proposed for real-time fault detection of power transformers. First, the transformer vibration principle is introduced, and two effective feature extraction techniques are presented. Next, the details of the classification model based on support vector machine (SVM) are shown. The model also includes a binary decision tree (BDT) which divides transformers into different classes according to health state. The trained model produces posterior probabilities of membership to each predefined class for a tested vibration sample. During the experiments, the vibrations of transformers under different conditions are acquired, and the corresponding feature vectors are used to train the SVM classifiers. The effectiveness of this model is illustrated experimentally on typical in-service transformers. The consistency between the results of the proposed model and the actual condition of the test transformers indicates that the model can be used as a reliable method for transformer fault detection.

Advances in modeling trait-based plant community assembly.

PubMed

Laughlin, Daniel C; Laughlin, David E

2013-10-01

In this review, we examine two new trait-based models of community assembly that predict the relative abundance of species from a regional species pool. The models use fundamentally different mathematical approaches and the predictions can differ considerably. Maxent obtains the most even probability distribution subject to community-weighted mean trait constraints. Traitspace predicts low probabilities for any species whose trait distribution does not pass through the environmental filter. Neither model maximizes functional diversity because of the emphasis on environmental filtering over limiting similarity. Traitspace can test for the effects of limiting similarity by explicitly incorporating intraspecific trait variation. The range of solutions in both models could be used to define the range of natural variability of community composition in restoration projects. Copyright © 2013 Elsevier Ltd. All rights reserved.

Uniform California earthquake rupture forecast, version 2 (UCERF 2)

USGS Publications Warehouse

Field, E.H.; Dawson, T.E.; Felzer, K.R.; Frankel, A.D.; Gupta, V.; Jordan, T.H.; Parsons, T.; Petersen, M.D.; Stein, R.S.; Weldon, R.J.; Wills, C.J.

2009-01-01

The 2007 Working Group on California Earthquake Probabilities (WGCEP, 2007) presents the Uniform California Earthquake Rupture Forecast, Version 2 (UCERF 2). This model comprises a time-independent (Poisson-process) earthquake rate model, developed jointly with the National Seismic Hazard Mapping Program and a time-dependent earthquake-probability model, based on recent earthquake rates and stress-renewal statistics conditioned on the date of last event. The models were developed from updated statewide earthquake catalogs and fault deformation databases using a uniform methodology across all regions and implemented in the modular, extensible Open Seismic Hazard Analysis framework. The rate model satisfies integrating measures of deformation across the plate-boundary zone and is consistent with historical seismicity data. An overprediction of earthquake rates found at intermediate magnitudes (6.5 ??? M ???7.0) in previous models has been reduced to within the 95% confidence bounds of the historical earthquake catalog. A logic tree with 480 branches represents the epistemic uncertainties of the full time-dependent model. The mean UCERF 2 time-dependent probability of one or more M ???6.7 earthquakes in the California region during the next 30 yr is 99.7%; this probability decreases to 46% for M ???7.5 and to 4.5% for M ???8.0. These probabilities do not include the Cascadia subduction zone, largely north of California, for which the estimated 30 yr, M ???8.0 time-dependent probability is 10%. The M ???6.7 probabilities on major strike-slip faults are consistent with the WGCEP (2003) study in the San Francisco Bay Area and the WGCEP (1995) study in southern California, except for significantly lower estimates along the San Jacinto and Elsinore faults, owing to provisions for larger multisegment ruptures. Important model limitations are discussed.

The Misapplication of Probability Theory in Quantum Mechanics

NASA Astrophysics Data System (ADS)

Racicot, Ronald

2014-03-01

This article is a revision of two papers submitted to the APS in the past two and a half years. In these papers, arguments and proofs are summarized for the following: (1) The wrong conclusion by EPR that Quantum Mechanics is incomplete, perhaps requiring the addition of ``hidden variables'' for completion. Theorems that assume such ``hidden variables,'' such as Bell's theorem, are also wrong. (2) Quantum entanglement is not a realizable physical phenomenon and is based entirely on assuming a probability superposition model for quantum spin. Such a model directly violates conservation of angular momentum. (3) Simultaneous multiple-paths followed by a quantum particle traveling through space also cannot possibly exist. Besides violating Noether's theorem, the multiple-paths theory is based solely on probability calculations. Probability calculations by themselves cannot possibly represent simultaneous physically real events. None of the reviews of the submitted papers actually refuted the arguments and evidence that was presented. These analyses should therefore be carefully evaluated since the conclusions reached have such important impact in quantum mechanics and quantum information theory.

Effects of variability in probable maximum precipitation patterns on flood losses

NASA Astrophysics Data System (ADS)

Zischg, Andreas Paul; Felder, Guido; Weingartner, Rolf; Quinn, Niall; Coxon, Gemma; Neal, Jeffrey; Freer, Jim; Bates, Paul

2018-05-01

The assessment of the impacts of extreme floods is important for dealing with residual risk, particularly for critical infrastructure management and for insurance purposes. Thus, modelling of the probable maximum flood (PMF) from probable maximum precipitation (PMP) by coupling hydrological and hydraulic models has gained interest in recent years. Herein, we examine whether variability in precipitation patterns exceeds or is below selected uncertainty factors in flood loss estimation and if the flood losses within a river basin are related to the probable maximum discharge at the basin outlet. We developed a model experiment with an ensemble of probable maximum precipitation scenarios created by Monte Carlo simulations. For each rainfall pattern, we computed the flood losses with a model chain and benchmarked the effects of variability in rainfall distribution with other model uncertainties. The results show that flood losses vary considerably within the river basin and depend on the timing and superimposition of the flood peaks from the basin's sub-catchments. In addition to the flood hazard component, the other components of flood risk, exposure, and vulnerability contribute remarkably to the overall variability. This leads to the conclusion that the estimation of the probable maximum expectable flood losses in a river basin should not be based exclusively on the PMF. Consequently, the basin-specific sensitivities to different precipitation patterns and the spatial organization of the settlements within the river basin need to be considered in the analyses of probable maximum flood losses.

Conditional, Time-Dependent Probabilities for Segmented Type-A Faults in the WGCEP UCERF 2

USGS Publications Warehouse

Field, Edward H.; Gupta, Vipin

2008-01-01

This appendix presents elastic-rebound-theory (ERT) motivated time-dependent probabilities, conditioned on the date of last earthquake, for the segmented type-A fault models of the 2007 Working Group on California Earthquake Probabilities (WGCEP). These probabilities are included as one option in the WGCEP?s Uniform California Earthquake Rupture Forecast 2 (UCERF 2), with the other options being time-independent Poisson probabilities and an ?Empirical? model based on observed seismicity rate changes. A more general discussion of the pros and cons of all methods for computing time-dependent probabilities, as well as the justification of those chosen for UCERF 2, are given in the main body of this report (and the 'Empirical' model is also discussed in Appendix M). What this appendix addresses is the computation of conditional, time-dependent probabilities when both single- and multi-segment ruptures are included in the model. Computing conditional probabilities is relatively straightforward when a fault is assumed to obey strict segmentation in the sense that no multi-segment ruptures occur (e.g., WGCEP (1988, 1990) or see Field (2007) for a review of all previous WGCEPs; from here we assume basic familiarity with conditional probability calculations). However, and as we?ll see below, the calculation is not straightforward when multi-segment ruptures are included, in essence because we are attempting to apply a point-process model to a non point process. The next section gives a review and evaluation of the single- and multi-segment rupture probability-calculation methods used in the most recent statewide forecast for California (WGCEP UCERF 1; Petersen et al., 2007). We then present results for the methodology adopted here for UCERF 2. We finish with a discussion of issues and possible alternative approaches that could be explored and perhaps applied in the future. A fault-by-fault comparison of UCERF 2 probabilities with those of previous studies is given in the main part of this report.

Repetitive pulses and laser-induced retinal injury thresholds

NASA Astrophysics Data System (ADS)

Lund, David J.

2007-02-01

Experimental studies with repetitively pulsed lasers show that the ED 50, expressed as energy per pulse, varies as the inverse fourth power of the number of pulses in the exposure, relatively independently of the wavelength, pulse duration, or pulse repetition frequency of the laser. Models based on a thermal damage mechanism cannot readily explain this result. Menendez et al. proposed a probability-summation model for predicting the threshold for a train of pulses based on the probit statistics for a single pulse. The model assumed that each pulse is an independent trial, unaffected by any other pulse in the train of pulses and assumes that the probability of damage for a single pulse is adequately described by the logistic curve. The requirement that the effect of each pulse in the pulse train be unaffected by the effects of other pulses in the train is a showstopper when the end effect is viewed as a thermal effect with each pulse in the train contributing to the end temperature of the target tissue. There is evidence that the induction of cell death by microcavitation bubbles around melanin granules heated by incident laser irradiation can satisfy the condition of pulse independence as required by the probability summation model. This paper will summarize the experimental data and discuss the relevance of the probability summation model given microcavitation as a damage mechanism.

Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk.

PubMed

Trepel, Christopher; Fox, Craig R; Poldrack, Russell A

2005-04-01

Most decisions must be made without advance knowledge of their consequences. Economists and psychologists have devoted much attention to modeling decisions made under conditions of risk in which options can be characterized by a known probability distribution over possible outcomes. The descriptive shortcomings of classical economic models motivated the development of prospect theory (D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk. Econometrica, 4 (1979) 263-291; A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5 (4) (1992) 297-323) the most successful behavioral model of decision under risk. In the prospect theory, subjective value is modeled by a value function that is concave for gains, convex for losses, and steeper for losses than for gains; the impact of probabilities are characterized by a weighting function that overweights low probabilities and underweights moderate to high probabilities. We outline the possible neural bases of the components of prospect theory, surveying evidence from human imaging, lesion, and neuropharmacology studies as well as animal neurophysiology studies. These results provide preliminary suggestions concerning the neural bases of prospect theory that include a broad set of brain regions and neuromodulatory systems. These data suggest that focused studies of decision making in the context of quantitative models may provide substantial leverage towards a fuller understanding of the cognitive neuroscience of decision making.

Modeling the probability of giving birth at health institutions among pregnant women attending antenatal care in West Shewa Zone, Oromia, Ethiopia: a cross sectional study.

PubMed

Dida, Nagasa; Birhanu, Zewdie; Gerbaba, Mulusew; Tilahun, Dejen; Morankar, Sudhakar

2014-06-01

Although ante natal care and institutional delivery is effective means for reducing maternal morbidity and mortality, the probability of giving birth at health institutions among ante natal care attendants has not been modeled in Ethiopia. Therefore, the objective of this study was to model predictors of giving birth at health institutions among expectant mothers following antenatal care. Facility based cross sectional study design was conducted among 322 consecutively selected mothers who were following ante natal care in two districts of West Shewa Zone, Oromia Regional State, Ethiopia. Participants were proportionally recruited from six health institutions. The data were analyzed using SPSS version 17.0. Multivariable logistic regression was employed to develop the prediction model. The final regression model had good discrimination power (89.2%), optimum sensitivity (89.0%) and specificity (80.0%) to predict the probability of giving birth at health institutions. Accordingly, self efficacy (beta=0.41), perceived barrier (beta=-0.31) and perceived susceptibility (beta=0.29) were significantly predicted the probability of giving birth at health institutions. The present study showed that logistic regression model has predicted the probability of giving birth at health institutions and identified significant predictors which health care providers should take into account in promotion of institutional delivery.

TH-A-BRF-02: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - Modeling Tumor Evolution for Adaptive Radiation Therapy

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

Liu, Y; Lee, CG; Chan, TCY

2014-06-15

Purpose: To develop mathematical models of tumor geometry changes under radiotherapy that may support future adaptive paradigms. Methods: A total of 29 cervical patients were scanned using MRI, once for planning and weekly thereafter for treatment monitoring. Using the tumor volumes contoured by a radiologist, three mathematical models were investigated based on the assumption of a stochastic process of tumor evolution. The “weekly MRI” model predicts tumor geometry for the following week from the last two consecutive MRI scans, based on the voxel transition probability. The other two models use only the first pair of consecutive MRI scans, and themore » transition probabilities were estimated via tumor type classified from the entire data set. The classification is based on either measuring the tumor volume (the “weekly volume” model), or implementing an auxiliary “Markov chain” model. These models were compared to a constant volume approach that represents the current clinical practice, using various model parameters; e.g., the threshold probability β converts the probability map into a tumor shape (larger threshold implies smaller tumor). Model performance was measured using volume conformity index (VCI), i.e., the union of the actual target and modeled target volume squared divided by product of these two volumes. Results: The “weekly MRI” model outperforms the constant volume model by 26% on average, and by 103% for the worst 10% of cases in terms of VCI under a wide range of β. The “weekly volume” and “Markov chain” models outperform the constant volume model by 20% and 16% on average, respectively. They also perform better than the “weekly MRI” model when β is large. Conclusion: It has been demonstrated that mathematical models can be developed to predict tumor geometry changes for cervical cancer undergoing radiotherapy. The models can potentially support adaptive radiotherapy paradigm by reducing normal tissue dose. This research was supported in part by the Ontario Consortium for Adaptive Interventions in Radiation Oncology (OCAIRO) funded by the Ontario Research Fund (ORF) and the MITACS Accelerate Internship Program.« less

Probability of survival during accidental immersion in cold water.

PubMed

Wissler, Eugene H

2003-01-01

Estimating the probability of survival during accidental immersion in cold water presents formidable challenges for both theoreticians and empirics. A number of theoretical models have been developed assuming that death occurs when the central body temperature, computed using a mathematical model, falls to a certain level. This paper describes a different theoretical approach to estimating the probability of survival. The human thermal model developed by Wissler is used to compute the central temperature during immersion in cold water. Simultaneously, a survival probability function is computed by solving a differential equation that defines how the probability of survival decreases with increasing time. The survival equation assumes that the probability of occurrence of a fatal event increases as the victim's central temperature decreases. Generally accepted views of the medical consequences of hypothermia and published reports of various accidents provide information useful for defining a "fatality function" that increases exponentially with decreasing central temperature. The particular function suggested in this paper yields a relationship between immersion time for 10% probability of survival and water temperature that agrees very well with Molnar's empirical observations based on World War II data. The method presented in this paper circumvents a serious difficulty with most previous models--that one's ability to survive immersion in cold water is determined almost exclusively by the ability to maintain a high level of shivering metabolism.

Forecasting a winner for Malaysian Cup 2013 using soccer simulation model

NASA Astrophysics Data System (ADS)

Yusof, Muhammad Mat; Fauzee, Mohd Soffian Omar; Latif, Rozita Abdul

2014-07-01

This paper investigates through soccer simulation the calculation of the probability for each team winning Malaysia Cup 2013. Our methodology used here is we predict the outcomes of individual matches and then we simulate the Malaysia Cup 2013 tournament 5000 times. As match outcomes are always a matter of uncertainty, statistical model, in particular a double Poisson model is used to predict the number of goals scored and conceded for each team. Maximum likelihood estimation is use to measure the attacking strength and defensive weakness for each team. Based on our simulation result, LionXII has a higher probability in becoming the winner, followed by Selangor, ATM, JDT and Kelantan. Meanwhile, T-Team, Negeri Sembilan and Felda United have lower probabilities to win Malaysia Cup 2013. In summary, we find that the probability for each team becominga winner is small, indicating that the level of competitive balance in Malaysia Cup 2013 is quite high.

Protein single-model quality assessment by feature-based probability density functions.

PubMed

Cao, Renzhi; Cheng, Jianlin

2016-04-04

Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method-Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

Rolling-Bearing Service Life Based on Probable Cause for Removal: A Tutorial

NASA Technical Reports Server (NTRS)

Zaretsky, Erwin V.; Branzai, Emanuel V.

2017-01-01

In 1947 and 1952, Gustaf Lundberg and Arvid Palmgren developed what is now referred to as the Lundberg-Palmgren Model for Rolling Bearing Life Prediction based on classical rolling-element fatigue. Today, bearing fatigue probably accounts for less than 5 percent of bearings removed from service for cause. A bearing service life prediction methodology and tutorial indexed to eight probable causes for bearing removal, including fatigue, are presented, which incorporate strict series reliability; Weibull statistical analysis; available published field data from the Naval Air Rework Facility; and 224,000 rolling-element bearings removed for rework from commercial aircraft engines.

Model-Based Method for Sensor Validation

NASA Technical Reports Server (NTRS)

Vatan, Farrokh

2012-01-01

Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

An extended car-following model considering the appearing probability of truck and driver's characteristics

NASA Astrophysics Data System (ADS)

Rong, Ying; Wen, Huiying

2018-05-01

In this paper, the appearing probability of truck is introduced and an extended car-following model is presented to analyze the traffic flow based on the consideration of driver's characteristics, under honk environment. The stability condition of this proposed model is obtained through linear stability analysis. In order to study the evolution properties of traffic wave near the critical point, the mKdV equation is derived by the reductive perturbation method. The results show that the traffic flow will become more disorder for the larger appearing probability of truck. Besides, the appearance of leading truck affects not only the stability of traffic flow, but also the effect of other aspects on traffic flow, such as: driver's reaction and honk effect. The effects of them on traffic flow are closely correlated with the appearing probability of truck. Finally, the numerical simulations under the periodic boundary condition are carried out to verify the proposed model. And they are consistent with the theoretical findings.

Theoretical Analysis of Rain Attenuation Probability

NASA Astrophysics Data System (ADS)

Roy, Surendra Kr.; Jha, Santosh Kr.; Jha, Lallan

2007-07-01

Satellite communication technologies are now highly developed and high quality, distance-independent services have expanded over a very wide area. As for the system design of the Hokkaido integrated telecommunications(HIT) network, it must first overcome outages of satellite links due to rain attenuation in ka frequency bands. In this paper theoretical analysis of rain attenuation probability on a slant path has been made. The formula proposed is based Weibull distribution and incorporates recent ITU-R recommendations concerning the necessary rain rates and rain heights inputs. The error behaviour of the model was tested with the loading rain attenuation prediction model recommended by ITU-R for large number of experiments at different probability levels. The novel slant path rain attenuastion prediction model compared to the ITU-R one exhibits a similar behaviour at low time percentages and a better root-mean-square error performance for probability levels above 0.02%. The set of presented models exhibits the advantage of implementation with little complexity and is considered useful for educational and back of the envelope computations.

The probability heuristics model of syllogistic reasoning.

PubMed

Chater, N; Oaksford, M

1999-03-01

A probability heuristic model (PHM) for syllogistic reasoning is proposed. An informational ordering over quantified statements suggests simple probability based heuristics for syllogistic reasoning. The most important is the "min-heuristic": choose the type of the least informative premise as the type of the conclusion. The rationality of this heuristic is confirmed by an analysis of the probabilistic validity of syllogistic reasoning which treats logical inference as a limiting case of probabilistic inference. A meta-analysis of past experiments reveals close fits with PHM. PHM also compares favorably with alternative accounts, including mental logics, mental models, and deduction as verbal reasoning. Crucially, PHM extends naturally to generalized quantifiers, such as Most and Few, which have not been characterized logically and are, consequently, beyond the scope of current mental logic and mental model theories. Two experiments confirm the novel predictions of PHM when generalized quantifiers are used in syllogistic arguments. PHM suggests that syllogistic reasoning performance may be determined by simple but rational informational strategies justified by probability theory rather than by logic. Copyright 1999 Academic Press.

Estimating temporary emigration and breeding proportions using capture-recapture data with Pollock's robust design

USGS Publications Warehouse

Kendall, W.L.; Nichols, J.D.; Hines, J.E.

1997-01-01

Statistical inference for capture-recapture studies of open animal populations typically relies on the assumption that all emigration from the studied population is permanent. However, there are many instances in which this assumption is unlikely to be met. We define two general models for the process of temporary emigration, completely random and Markovian. We then consider effects of these two types of temporary emigration on Jolly-Seber (Seber 1982) estimators and on estimators arising from the full-likelihood approach of Kendall et al. (1995) to robust design data. Capture-recapture data arising from Pollock's (1982) robust design provide the basis for obtaining unbiased estimates of demographic parameters in the presence of temporary emigration and for estimating the probability of temporary emigration. We present a likelihood-based approach to dealing with temporary emigration that permits estimation under different models of temporary emigration and yields tests for completely random and Markovian emigration. In addition, we use the relationship between capture probability estimates based on closed and open models under completely random temporary emigration to derive three ad hoc estimators for the probability of temporary emigration, two of which should be especially useful in situations where capture probabilities are heterogeneous among individual animals. Ad hoc and full-likelihood estimators are illustrated for small mammal capture-recapture data sets. We believe that these models and estimators will be useful for testing hypotheses about the process of temporary emigration, for estimating demographic parameters in the presence of temporary emigration, and for estimating probabilities of temporary emigration. These latter estimates are frequently of ecological interest as indicators of animal movement and, in some sampling situations, as direct estimates of breeding probabilities and proportions.

A robust method using propensity score stratification for correcting verification bias for binary tests

PubMed Central

He, Hua; McDermott, Michael P.

2012-01-01

Sensitivity and specificity are common measures of the accuracy of a diagnostic test. The usual estimators of these quantities are unbiased if data on the diagnostic test result and the true disease status are obtained from all subjects in an appropriately selected sample. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the result of the diagnostic test and other characteristics of the subjects. Estimators of sensitivity and specificity based on this subset of subjects are typically biased; this is known as verification bias. Methods have been proposed to correct verification bias under the assumption that the missing data on disease status are missing at random (MAR), that is, the probability of missingness depends on the true (missing) disease status only through the test result and observed covariate information. When some of the covariates are continuous, or the number of covariates is relatively large, the existing methods require parametric models for the probability of disease or the probability of verification (given the test result and covariates), and hence are subject to model misspecification. We propose a new method for correcting verification bias based on the propensity score, defined as the predicted probability of verification given the test result and observed covariates. This is estimated separately for those with positive and negative test results. The new method classifies the verified sample into several subsamples that have homogeneous propensity scores and allows correction for verification bias. Simulation studies demonstrate that the new estimators are more robust to model misspecification than existing methods, but still perform well when the models for the probability of disease and probability of verification are correctly specified. PMID:21856650

Launch Vehicle Debris Models and Crew Vehicle Ascent Abort Risk

NASA Technical Reports Server (NTRS)

Gee, Ken; Lawrence, Scott

2013-01-01

For manned space launch systems, a reliable abort system is required to reduce the risks associated with a launch vehicle failure during ascent. Understanding the risks associated with failure environments can be achieved through the use of physics-based models of these environments. Debris fields due to destruction of the launch vehicle is one such environment. To better analyze the risk posed by debris, a physics-based model for generating launch vehicle debris catalogs has been developed. The model predicts the mass distribution of the debris field based on formulae developed from analysis of explosions. Imparted velocity distributions are computed using a shock-physics code to model the explosions within the launch vehicle. A comparison of the debris catalog with an existing catalog for the Shuttle external tank show good comparison in the debris characteristics and the predicted debris strike probability. The model is used to analyze the effects of number of debris pieces and velocity distributions on the strike probability and risk.

A Model for Risk Analysis of Oil Tankers

NASA Astrophysics Data System (ADS)

Montewka, Jakub; Krata, Przemysław; Goerland, Floris; Kujala, Pentti

2010-01-01

The paper presents a model for risk analysis regarding marine traffic, with the emphasis on two types of the most common marine accidents which are: collision and grounding. The focus is on oil tankers as these pose the highest environmental risk. A case study in selected areas of Gulf of Finland in ice free conditions is presented. The model utilizes a well-founded formula for risk calculation, which combines the probability of an unwanted event with its consequences. Thus the model is regarded a block type model, consisting of blocks for the probability of collision and grounding estimation respectively as well as blocks for consequences of an accident modelling. Probability of vessel colliding is assessed by means of a Minimum Distance To Collision (MDTC) based model. The model defines in a novel way the collision zone, using mathematical ship motion model and recognizes traffic flow as a non homogeneous process. The presented calculations address waterways crossing between Helsinki and Tallinn, where dense cross traffic during certain hours is observed. For assessment of a grounding probability, a new approach is proposed, which utilizes a newly developed model, where spatial interactions between objects in different locations are recognized. A ship at a seaway and navigational obstructions may be perceived as interacting objects and their repulsion may be modelled by a sort of deterministic formulation. Risk due to tankers running aground addresses an approach fairway to an oil terminal in Sköldvik, near Helsinki. The consequences of an accident are expressed in monetary terms, and concern costs of an oil spill, based on statistics of compensations claimed from the International Oil Pollution Compensation Funds (IOPC Funds) by parties involved.

Charge transport properties of DNA aperiodic molecule: The role of interbase hopping in Watson-Crick base pair

NASA Astrophysics Data System (ADS)

Sinurat, E. N.; Yudiarsah, E.

2017-07-01

The charge transport properties of DNA aperiodic molecule has been studied by considering various interbase hopping parameter on Watson-Crick base pair. 32 base pairs long double-stranded DNA aperiodic model with sequence GCTAGTACGTGACGTAGCTAGGATATGCCTGA on one chain and its complement on the other chain is used. Transfer matrix method has been used to calculate transmission probabilities, for determining I-V characteristic using Landauer Büttiker formula. DNA molecule is modeled using tight binding hamiltonian combined with the theory of Slater-Koster. The result show, the increment of Watson-Crick hopping value leads to the transmission probabilities and current of DNA aperiodic molecule increases.

Rtools: a web server for various secondary structural analyses on single RNA sequences.

PubMed

Hamada, Michiaki; Ono, Yukiteru; Kiryu, Hisanori; Sato, Kengo; Kato, Yuki; Fukunaga, Tsukasa; Mori, Ryota; Asai, Kiyoshi

2016-07-08

The secondary structures, as well as the nucleotide sequences, are the important features of RNA molecules to characterize their functions. According to the thermodynamic model, however, the probability of any secondary structure is very small. As a consequence, any tool to predict the secondary structures of RNAs has limited accuracy. On the other hand, there are a few tools to compensate the imperfect predictions by calculating and visualizing the secondary structural information from RNA sequences. It is desirable to obtain the rich information from those tools through a friendly interface. We implemented a web server of the tools to predict secondary structures and to calculate various structural features based on the energy models of secondary structures. By just giving an RNA sequence to the web server, the user can get the different types of solutions of the secondary structures, the marginal probabilities such as base-paring probabilities, loop probabilities and accessibilities of the local bases, the energy changes by arbitrary base mutations as well as the measures for validations of the predicted secondary structures. The web server is available at http://rtools.cbrc.jp, which integrates software tools, CentroidFold, CentroidHomfold, IPKnot, CapR, Raccess, Rchange and RintD. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Dynamic habitat models: using telemetry data to project fisheries bycatch

PubMed Central

Žydelis, Ramūnas; Lewison, Rebecca L.; Shaffer, Scott A.; Moore, Jeffrey E.; Boustany, Andre M.; Roberts, Jason J.; Sims, Michelle; Dunn, Daniel C.; Best, Benjamin D.; Tremblay, Yann; Kappes, Michelle A.; Halpin, Patrick N.; Costa, Daniel P.; Crowder, Larry B.

2011-01-01

Fisheries bycatch is a recognized threat to marine megafauna. Addressing bycatch of pelagic species however is challenging owing to the dynamic nature of marine environments and vagility of these organisms. In order to assess the potential for species to overlap with fisheries, we propose applying dynamic habitat models to determine relative probabilities of species occurrence for specific oceanographic conditions. We demonstrate this approach by modelling habitats for Laysan (Phoebastria immutabilis) and black-footed albatrosses (Phoebastria nigripes) using telemetry data and relating their occurrence probabilities to observations of Hawaii-based longline fisheries in 1997–2000. We found that modelled habitat preference probabilities of black-footed albatrosses were high within some areas of the fishing range of the Hawaiian fleet and such preferences were important in explaining bycatch occurrence. Conversely, modelled habitats of Laysan albatrosses overlapped little with Hawaii-based longline fisheries and did little to explain the bycatch of this species. Estimated patterns of albatross habitat overlap with the Hawaiian fleet corresponded to bycatch observations: black-footed albatrosses were more frequently caught in this fishery despite being 10 times less abundant than Laysan albatrosses. This case study demonstrates that dynamic habitat models based on telemetry data may help to project interactions with pelagic animals relative to environmental features and that such an approach can serve as a tool to guide conservation and management decisions. PMID:21429921

Short-Term Memory Scanning Viewed as Exemplar-Based Categorization

ERIC Educational Resources Information Center

Nosofsky, Robert M.; Little, Daniel R.; Donkin, Christopher; Fific, Mario

2011-01-01

Exemplar-similarity models such as the exemplar-based random walk (EBRW) model (Nosofsky & Palmeri, 1997b) were designed to provide a formal account of multidimensional classification choice probabilities and response times (RTs). At the same time, a recurring theme has been to use exemplar models to account for old-new item recognition and to…

The impact of joint responses of devices in an airport security system.

PubMed

Nie, Xiaofeng; Batta, Rajan; Drury, Colin G; Lin, Li

2009-02-01

In this article, we consider a model for an airport security system in which the declaration of a threat is based on the joint responses of inspection devices. This is in contrast to the typical system in which each check station independently declares a passenger as having a threat or not having a threat. In our framework the declaration of threat/no-threat is based upon the passenger scores at the check stations he/she goes through. To do this we use concepts from classification theory in the field of multivariate statistics analysis and focus on the main objective of minimizing the expected cost of misclassification. The corresponding correct classification and misclassification probabilities can be obtained by using a simulation-based method. After computing the overall false alarm and false clear probabilities, we compare our joint response system with two other independently operated systems. A model that groups passengers in a manner that minimizes the false alarm probability while maintaining the false clear probability within specifications set by a security authority is considered. We also analyze the staffing needs at each check station for such an inspection scheme. An illustrative example is provided along with sensitivity analysis on key model parameters. A discussion is provided on some implementation issues, on the various assumptions made in the analysis, and on potential drawbacks of the approach.

A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

PubMed

Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen

2014-01-01

Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.

Monthly streamflow forecasting based on hidden Markov model and Gaussian Mixture Regression

NASA Astrophysics Data System (ADS)

Liu, Yongqi; Ye, Lei; Qin, Hui; Hong, Xiaofeng; Ye, Jiajun; Yin, Xingli

2018-06-01

Reliable streamflow forecasts can be highly valuable for water resources planning and management. In this study, we combined a hidden Markov model (HMM) and Gaussian Mixture Regression (GMR) for probabilistic monthly streamflow forecasting. The HMM is initialized using a kernelized K-medoids clustering method, and the Baum-Welch algorithm is then executed to learn the model parameters. GMR derives a conditional probability distribution for the predictand given covariate information, including the antecedent flow at a local station and two surrounding stations. The performance of HMM-GMR was verified based on the mean square error and continuous ranked probability score skill scores. The reliability of the forecasts was assessed by examining the uniformity of the probability integral transform values. The results show that HMM-GMR obtained reasonably high skill scores and the uncertainty spread was appropriate. Different HMM states were assumed to be different climate conditions, which would lead to different types of observed values. We demonstrated that the HMM-GMR approach can handle multimodal and heteroscedastic data.

An artificial system for selecting the optimal surgical team.

PubMed

Saberi, Nahid; Mahvash, Mohsen; Zenati, Marco

2015-01-01

We introduce an intelligent system to optimize a team composition based on the team's historical outcomes and apply this system to compose a surgical team. The system relies on a record of the procedures performed in the past. The optimal team composition is the one with the lowest probability of unfavorable outcome. We use the theory of probability and the inclusion exclusion principle to model the probability of team outcome for a given composition. A probability value is assigned to each person of database and the probability of a team composition is calculated from them. The model allows to determine the probability of all possible team compositions even if there is no recoded procedure for some team compositions. From an analytical perspective, assembling an optimal team is equivalent to minimizing the overlap of team members who have a recurring tendency to be involved with procedures of unfavorable results. A conceptual example shows the accuracy of the proposed system on obtaining the optimal team.

Potential of hydraulically induced fractures to communicate with existing wellbores

NASA Astrophysics Data System (ADS)

Montague, James A.; Pinder, George F.

2015-10-01

The probability that new hydraulically fractured wells drilled within the area of New York underlain by the Marcellus Shale will intersect an existing wellbore is calculated using a statistical model, which incorporates: the depth of a new fracturing well, the vertical growth of induced fractures, and the depths and locations of existing nearby wells. The model first calculates the probability of encountering an existing well in plan view and combines this with the probability of an existing well-being at sufficient depth to intersect the fractured region. Average probability estimates for the entire region of New York underlain by the Marcellus Shale range from 0.00% to 3.45% based upon the input parameters used. The largest contributing parameter on the probability value calculated is the nearby density of wells meaning that due diligence by oil and gas companies during construction in identifying all nearby wells will have the greatest effect in reducing the probability of interwellbore communication.

[Establishment of the mathematic model of total quantum statistical moment standard similarity for application to medical theoretical research].

PubMed

He, Fu-yuan; Deng, Kai-wen; Huang, Sheng; Liu, Wen-long; Shi, Ji-lian

2013-09-01

The paper aims to elucidate and establish a new mathematic model: the total quantum statistical moment standard similarity (TQSMSS) on the base of the original total quantum statistical moment model and to illustrate the application of the model to medical theoretical research. The model was established combined with the statistical moment principle and the normal distribution probability density function properties, then validated and illustrated by the pharmacokinetics of three ingredients in Buyanghuanwu decoction and of three data analytical method for them, and by analysis of chromatographic fingerprint for various extracts with different solubility parameter solvents dissolving the Buyanghanwu-decoction extract. The established model consists of four mainly parameters: (1) total quantum statistical moment similarity as ST, an overlapped area by two normal distribution probability density curves in conversion of the two TQSM parameters; (2) total variability as DT, a confidence limit of standard normal accumulation probability which is equal to the absolute difference value between the two normal accumulation probabilities within integration of their curve nodical; (3) total variable probability as 1-Ss, standard normal distribution probability within interval of D(T); (4) total variable probability (1-beta)alpha and (5) stable confident probability beta(1-alpha): the correct probability to make positive and negative conclusions under confident coefficient alpha. With the model, we had analyzed the TQSMS similarities of pharmacokinetics of three ingredients in Buyanghuanwu decoction and of three data analytical methods for them were at range of 0.3852-0.9875 that illuminated different pharmacokinetic behaviors of each other; and the TQSMS similarities (ST) of chromatographic fingerprint for various extracts with different solubility parameter solvents dissolving Buyanghuanwu-decoction-extract were at range of 0.6842-0.999 2 that showed different constituents with various solvent extracts. The TQSMSS can characterize the sample similarity, by which we can quantitate the correct probability with the test of power under to make positive and negative conclusions no matter the samples come from same population under confident coefficient a or not, by which we can realize an analysis at both macroscopic and microcosmic levels, as an important similar analytical method for medical theoretical research.

A statistical method to estimate low-energy hadronic cross sections

NASA Astrophysics Data System (ADS)

Balassa, Gábor; Kovács, Péter; Wolf, György

2018-02-01

In this article we propose a model based on the Statistical Bootstrap approach to estimate the cross sections of different hadronic reactions up to a few GeV in c.m.s. energy. The method is based on the idea, when two particles collide a so-called fireball is formed, which after a short time period decays statistically into a specific final state. To calculate the probabilities we use a phase space description extended with quark combinatorial factors and the possibility of more than one fireball formation. In a few simple cases the probability of a specific final state can be calculated analytically, where we show that the model is able to reproduce the ratios of the considered cross sections. We also show that the model is able to describe proton-antiproton annihilation at rest. In the latter case we used a numerical method to calculate the more complicated final state probabilities. Additionally, we examined the formation of strange and charmed mesons as well, where we used existing data to fit the relevant model parameters.

PROBABILITIES OF TEMPERATURE EXTREMES IN THE U.S.

EPA Science Inventory

The model Temperature Extremes Version 1.0 provides the capability to estimate the probability, for 332 locations in the 50 U.S. states, that an extreme temperature will occur for one or more consecutive days and/or for any number of days in a given month or season, based on stat...

Spatial prediction models for the probable biological condition of streams and rivers in the USA

EPA Science Inventory

The National Rivers and Streams Assessment (NRSA) is a probability-based survey conducted by the US Environmental Protection Agency and its state and tribal partners. It provides information on the ecological condition of the rivers and streams in the conterminous USA, and the ex...

Random forest models for the probable biological condition of streams and rivers in the USA

EPA Science Inventory

The National Rivers and Streams Assessment (NRSA) is a probability based survey conducted by the US Environmental Protection Agency and its state and tribal partners. It provides information on the ecological condition of the rivers and streams in the conterminous USA, and the ex...

Phased models for evaluating the performability of computing systems

NASA Technical Reports Server (NTRS)

Wu, L. T.; Meyer, J. F.

1979-01-01

A phase-by-phase modelling technique is introduced to evaluate a fault tolerant system's ability to execute different sets of computational tasks during different phases of the control process. Intraphase processes are allowed to differ from phase to phase. The probabilities of interphase state transitions are specified by interphase transition matrices. Based on constraints imposed on the intraphase and interphase transition probabilities, various iterative solution methods are developed for calculating system performability.

Goodness of fit of probability distributions for sightings as species approach extinction.

PubMed

Vogel, Richard M; Hosking, Jonathan R M; Elphick, Chris S; Roberts, David L; Reed, J Michael

2009-04-01

Estimating the probability that a species is extinct and the timing of extinctions is useful in biological fields ranging from paleoecology to conservation biology. Various statistical methods have been introduced to infer the time of extinction and extinction probability from a series of individual sightings. There is little evidence, however, as to which of these models provide adequate fit to actual sighting records. We use L-moment diagrams and probability plot correlation coefficient (PPCC) hypothesis tests to evaluate the goodness of fit of various probabilistic models to sighting data collected for a set of North American and Hawaiian bird populations that have either gone extinct, or are suspected of having gone extinct, during the past 150 years. For our data, the uniform, truncated exponential, and generalized Pareto models performed moderately well, but the Weibull model performed poorly. Of the acceptable models, the uniform distribution performed best based on PPCC goodness of fit comparisons and sequential Bonferroni-type tests. Further analyses using field significance tests suggest that although the uniform distribution is the best of those considered, additional work remains to evaluate the truncated exponential model more fully. The methods we present here provide a framework for evaluating subsequent models.

The combined effect of age and basal follicle-stimulating hormone on the cost of a live birth at assisted reproductive technology.

PubMed

Henne, Melinda B; Stegmann, Barbara J; Neithardt, Adrienne B; Catherino, William H; Armstrong, Alicia Y; Kao, Tzu-Cheg; Segars, James H

2008-01-01

To predict the cost of a delivery following assisted reproductive technologies (ART). Cost analysis based on retrospective chart analysis. University-based ART program. Women aged >or=26 and

Bayesian data analysis tools for atomic physics

NASA Astrophysics Data System (ADS)

Trassinelli, Martino

2017-10-01

We present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes' theorem and its applications. In particular we discuss about how to calculate simple and joint probability distributions and the Bayesian evidence, a model dependent quantity that allows to assign probabilities to different hypotheses from the analysis of a same data set. To give some practical examples, these methods are applied to two concrete cases. In the first example, the presence or not of a satellite line in an atomic spectrum is investigated. In the second example, we determine the most probable model among a set of possible profiles from the analysis of a statistically poor spectrum. We show also how to calculate the probability distribution of the main spectral component without having to determine uniquely the spectrum modeling. For these two studies, we implement the program Nested_fit to calculate the different probability distributions and other related quantities. Nested_fit is a Fortran90/Python code developed during the last years for analysis of atomic spectra. As indicated by the name, it is based on the nested algorithm, which is presented in details together with the program itself.

MRI Brain Tumor Segmentation and Necrosis Detection Using Adaptive Sobolev Snakes.

PubMed

Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

2014-03-21

Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at different points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D diffusion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.

MRI brain tumor segmentation and necrosis detection using adaptive Sobolev snakes

NASA Astrophysics Data System (ADS)

Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

2014-03-01

Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at di erent points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D di usion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.

Fixation of strategies driven by switching probabilities in evolutionary games

NASA Astrophysics Data System (ADS)

Xu, Zimin; Zhang, Jianlei; Zhang, Chunyan; Chen, Zengqiang

2016-12-01

We study the evolutionary dynamics of strategies in finite populations which are homogeneous and well mixed by means of the pairwise comparison process, the core of which is the proposed switching probability. Previous studies about this subject are usually based on the known payoff comparison of the related players, which is an ideal assumption. In real social systems, acquiring the accurate payoffs of partners at each round of interaction may be not easy. So we bypass the need of explicit knowledge of payoffs, and encode the payoffs into the willingness of any individual shift from her current strategy to the competing one, and the switching probabilities are wholly independent of payoffs. Along this way, the strategy updating can be performed when game models are fixed and payoffs are unclear, expected to extend ideal assumptions to be more realistic one. We explore the impact of the switching probability on the fixation probability and derive a simple formula which determines the fixation probability. Moreover we find that cooperation dominates defection if the probability of cooperation replacing defection is always larger than the probability of defection replacing cooperation in finite populations. Last, we investigate the influences of model parameters on the fixation of strategies in the framework of three concrete game models: prisoner's dilemma, snowdrift game and stag-hunt game, which effectively portray the characteristics of cooperative dilemmas in real social systems.

Probability for Weather and Climate

NASA Astrophysics Data System (ADS)

Smith, L. A.

2013-12-01

Over the last 60 years, the availability of large-scale electronic computers has stimulated rapid and significant advances both in meteorology and in our understanding of the Earth System as a whole. The speed of these advances was due, in large part, to the sudden ability to explore nonlinear systems of equations. The computer allows the meteorologist to carry a physical argument to its conclusion; the time scales of weather phenomena then allow the refinement of physical theory, numerical approximation or both in light of new observations. Prior to this extension, as Charney noted, the practicing meteorologist could ignore the results of theory with good conscience. Today, neither the practicing meteorologist nor the practicing climatologist can do so, but to what extent, and in what contexts, should they place the insights of theory above quantitative simulation? And in what circumstances can one confidently estimate the probability of events in the world from model-based simulations? Despite solid advances of theory and insight made possible by the computer, the fidelity of our models of climate differs in kind from the fidelity of models of weather. While all prediction is extrapolation in time, weather resembles interpolation in state space, while climate change is fundamentally an extrapolation. The trichotomy of simulation, observation and theory which has proven essential in meteorology will remain incomplete in climate science. Operationally, the roles of probability, indeed the kinds of probability one has access too, are different in operational weather forecasting and climate services. Significant barriers to forming probability forecasts (which can be used rationally as probabilities) are identified. Monte Carlo ensembles can explore sensitivity, diversity, and (sometimes) the likely impact of measurement uncertainty and structural model error. The aims of different ensemble strategies, and fundamental differences in ensemble design to support of decision making versus advance science, are noted. It is argued that, just as no point forecast is complete without an estimate of its accuracy, no model-based probability forecast is complete without an estimate of its own irrelevance. The same nonlinearities that made the electronic computer so valuable links the selection and assimilation of observations, the formation of ensembles, the evolution of models, the casting of model simulations back into observables, and the presentation of this information to those who use it to take action or to advance science. Timescales of interest exceed the lifetime of a climate model and the career of a climate scientist, disarming the trichotomy that lead to swift advances in weather forecasting. Providing credible, informative climate services is a more difficult task. In this context, the value of comparing the forecasts of simulation models not only with each other but also with the performance of simple empirical models, whenever possible, is stressed. The credibility of meteorology is based on its ability to forecast and explain the weather. The credibility of climatology will always be based on flimsier stuff. Solid insights of climate science may be obscured if the severe limits on our ability to see the details of the future even probabilistically are not communicated clearly.

PARTS: Probabilistic Alignment for RNA joinT Secondary structure prediction

PubMed Central

Harmanci, Arif Ozgun; Sharma, Gaurav; Mathews, David H.

2008-01-01

A novel method is presented for joint prediction of alignment and common secondary structures of two RNA sequences. The joint consideration of common secondary structures and alignment is accomplished by structural alignment over a search space defined by the newly introduced motif called matched helical regions. The matched helical region formulation generalizes previously employed constraints for structural alignment and thereby better accommodates the structural variability within RNA families. A probabilistic model based on pseudo free energies obtained from precomputed base pairing and alignment probabilities is utilized for scoring structural alignments. Maximum a posteriori (MAP) common secondary structures, sequence alignment and joint posterior probabilities of base pairing are obtained from the model via a dynamic programming algorithm called PARTS. The advantage of the more general structural alignment of PARTS is seen in secondary structure predictions for the RNase P family. For this family, the PARTS MAP predictions of secondary structures and alignment perform significantly better than prior methods that utilize a more restrictive structural alignment model. For the tRNA and 5S rRNA families, the richer structural alignment model of PARTS does not offer a benefit and the method therefore performs comparably with existing alternatives. For all RNA families studied, the posterior probability estimates obtained from PARTS offer an improvement over posterior probability estimates from a single sequence prediction. When considering the base pairings predicted over a threshold value of confidence, the combination of sensitivity and positive predictive value is superior for PARTS than for the single sequence prediction. PARTS source code is available for download under the GNU public license at http://rna.urmc.rochester.edu. PMID:18304945

'Unconventional' experiments in biology and medicine with optimized design based on quantum-like correlations.

PubMed

Beauvais, Francis

2017-02-01

In previous articles, a description of 'unconventional' experiments (e.g. in vitro or clinical studies based on high dilutions, 'memory of water' or homeopathy) using quantum-like probability was proposed. Because the mathematical formulations of quantum logic are frequently an obstacle for physicians and biologists, a modified modeling that rests on classical probability is described in the present article. This modeling is inspired from a relational interpretation of quantum physics that applies not only to microscopic objects, but also to macroscopic structures, including experimental devices and observers. In this framework, any outcome of an experiment is not an absolute property of the observed system as usually considered but is expressed relatively to an observer. A team of interacting observers is thus described from an external view point based on two principles: the outcomes of experiments are expressed relatively to each observer and the observers agree on outcomes when they interact with each other. If probability fluctuations are also taken into account, correlations between 'expected' and observed outcomes emerge. Moreover, quantum-like correlations are predicted in experiments with local blind design but not with centralized blind design. No assumption on 'memory' or other physical modification of water is necessary in the present description although such hypotheses cannot be formally discarded. In conclusion, a simple modeling of 'unconventional' experiments based on classical probability is now available and its predictions can be tested. The underlying concepts are sufficiently intuitive to be spread into the homeopathy community and beyond. It is hoped that this modeling will encourage new studies with optimized designs for in vitro experiments and clinical trials. Copyright © 2017 The Faculty of Homeopathy. Published by Elsevier Ltd. All rights reserved.

Framing From Experience: Cognitive Processes and Predictions of Risky Choice.

PubMed

Gonzalez, Cleotilde; Mehlhorn, Katja

2016-07-01

A framing bias shows risk aversion in problems framed as "gains" and risk seeking in problems framed as "losses," even when these are objectively equivalent and probabilities and outcomes values are explicitly provided. We test this framing bias in situations where decision makers rely on their own experience, sampling the problem's options (safe and risky) and seeing the outcomes before making a choice. In Experiment 1, we replicate the framing bias in description-based decisions and find risk indifference in gains and losses in experience-based decisions. Predictions of an Instance-Based Learning model suggest that objective probabilities as well as the number of samples taken are factors that contribute to the lack of framing effect. We test these two factors in Experiment 2 and find no framing effect when a few samples are taken but when large samples are taken, the framing effect appears regardless of the objective probability values. Implications of behavioral results and cognitive modeling are discussed. Copyright © 2015 Cognitive Science Society, Inc.

A nonparametric multiple imputation approach for missing categorical data.

PubMed

Zhou, Muhan; He, Yulei; Yu, Mandi; Hsu, Chiu-Hsieh

2017-06-06

Incomplete categorical variables with more than two categories are common in public health data. However, most of the existing missing-data methods do not use the information from nonresponse (missingness) probabilities. We propose a nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each category. The donor set for imputation is formed by measuring distances between each missing value with other non-missing values. The distance function is calculated based on a predictive score, which is derived from two working models: one fits a multinomial logistic regression for predicting the missing categorical outcome (the outcome model) and the other fits a logistic regression for predicting missingness probabilities (the missingness model). A weighting scheme is used to accommodate contributions from two working models when generating the predictive score. A missing value is imputed by randomly selecting one of the non-missing values with the smallest distances. We conduct a simulation to evaluate the performance of the proposed method and compare it with several alternative methods. A real-data application is also presented. The simulation study suggests that the proposed method performs well when missingness probabilities are not extreme under some misspecifications of the working models. However, the calibration estimator, which is also based on two working models, can be highly unstable when missingness probabilities for some observations are extremely high. In this scenario, the proposed method produces more stable and better estimates. In addition, proper weights need to be chosen to balance the contributions from the two working models and achieve optimal results for the proposed method. We conclude that the proposed multiple imputation method is a reasonable approach to dealing with missing categorical outcome data with more than two levels for assessing the distribution of the outcome. In terms of the choices for the working models, we suggest a multinomial logistic regression for predicting the missing outcome and a binary logistic regression for predicting the missingness probability.

Stochastic and deterministic model of microbial heat inactivation.

PubMed

Corradini, Maria G; Normand, Mark D; Peleg, Micha

2010-03-01

Microbial inactivation is described by a model based on the changing survival probabilities of individual cells or spores. It is presented in a stochastic and discrete form for small groups, and as a continuous deterministic model for larger populations. If the underlying mortality probability function remains constant throughout the treatment, the model generates first-order ("log-linear") inactivation kinetics. Otherwise, it produces survival patterns that include Weibullian ("power-law") with upward or downward concavity, tailing with a residual survival level, complete elimination, flat "shoulder" with linear or curvilinear continuation, and sigmoid curves. In both forms, the same algorithm or model equation applies to isothermal and dynamic heat treatments alike. Constructing the model does not require assuming a kinetic order or knowledge of the inactivation mechanism. The general features of its underlying mortality probability function can be deduced from the experimental survival curve's shape. Once identified, the function's coefficients, the survival parameters, can be estimated directly from the experimental survival ratios by regression. The model is testable in principle but matching the estimated mortality or inactivation probabilities with those of the actual cells or spores can be a technical challenge. The model is not intended to replace current models to calculate sterility. Its main value, apart from connecting the various inactivation patterns to underlying probabilities at the cellular level, might be in simulating the irregular survival patterns of small groups of cells and spores. In principle, it can also be used for nonthermal methods of microbial inactivation and their combination with heat.

A model-based approach to estimating forest area

Treesearch

Ronald E. McRoberts

2006-01-01

A logistic regression model based on forest inventory plot data and transformations of Landsat Thematic Mapper satellite imagery was used to predict the probability of forest for 15 study areas in Indiana, USA, and 15 in Minnesota, USA. Within each study area, model-based estimates of forest area were obtained for circular areas with radii of 5 km, 10 km, and 15 km and...

Use and interpretation of logistic regression in habitat-selection studies

USGS Publications Warehouse

Keating, Kim A.; Cherry, Steve

2004-01-01

 Logistic regression is an important tool for wildlife habitat-selection studies, but the method frequently has been misapplied due to an inadequate understanding of the logistic model, its interpretation, and the influence of sampling design. To promote better use of this method, we review its application and interpretation under 3 sampling designs: random, case-control, and use-availability. Logistic regression is appropriate for habitat use-nonuse studies employing random sampling and can be used to directly model the conditional probability of use in such cases. Logistic regression also is appropriate for studies employing case-control sampling designs, but careful attention is required to interpret results correctly. Unless bias can be estimated or probability of use is small for all habitats, results of case-control studies should be interpreted as odds ratios, rather than probability of use or relative probability of use. When data are gathered under a use-availability design, logistic regression can be used to estimate approximate odds ratios if probability of use is small, at least on average. More generally, however, logistic regression is inappropriate for modeling habitat selection in use-availability studies. In particular, using logistic regression to fit the exponential model of Manly et al. (2002:100) does not guarantee maximum-likelihood estimates, valid probabilities, or valid likelihoods. We show that the resource selection function (RSF) commonly used for the exponential model is proportional to a logistic discriminant function. Thus, it may be used to rank habitats with respect to probability of use and to identify important habitat characteristics or their surrogates, but it is not guaranteed to be proportional to probability of use. Other problems associated with the exponential model also are discussed. We describe an alternative model based on Lancaster and Imbens (1996) that offers a method for estimating conditional probability of use in use-availability studies. Although promising, this model fails to converge to a unique solution in some important situations. Further work is needed to obtain a robust method that is broadly applicable to use-availability studies.

Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset

PubMed Central

Liu, Zhao; Zhu, Yunhong; Wu, Chenxue

2016-01-01

Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users’ privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transition probabilities in the transition matrices. Next, we regard a mobility model for an LBS requester as a stationary stochastic process and compute the n-step transition probability matrices by raising the normalized transition probability matrices to the power n. Furthermore, we propose two location prediction methods: rough prediction and accurate prediction. The former achieves the probabilities of arriving at target locations along simple paths those include only current locations, target locations and transition steps. By iteratively combining the probabilities for simple paths with n steps and the probabilities for detailed paths with n-1 steps, the latter method calculates transition probabilities for detailed paths with n steps from current locations to target locations. Finally, we conduct extensive experiments, and correctness and flexibility of our proposed algorithm have been verified. PMID:27508502

Integrating occupancy modeling and interview data for corridor identification: A case study for jaguars in Nicaragua

USGS Publications Warehouse

Zeller, K.A.; Nijhawan, S.; Salom-Perez, R.; Potosme, S.H.; Hines, J.E.

2011-01-01

Corridors are critical elements in the long-term conservation of wide-ranging species like the jaguar (Panthera onca). Jaguar corridors across the range of the species were initially identified using a GIS-based least-cost corridor model. However, due to inherent errors in remotely sensed data and model uncertainties, these corridors warrant field verification before conservation efforts can begin. We developed a novel corridor assessment protocol based on interview data and site occupancy modeling. We divided our pilot study area, in southeastern Nicaragua, into 71, 6. ??. 6 km sampling units and conducted 160 structured interviews with local residents. Interviews were designed to collect data on jaguar and seven prey species so that detection/non-detection matrices could be constructed for each sampling unit. Jaguars were reportedly detected in 57% of the sampling units and had a detection probability of 28%. With the exception of white-lipped peccary, prey species were reportedly detected in 82-100% of the sampling units. Though the use of interview data may violate some assumptions of the occupancy modeling approach for determining 'proportion of area occupied', we countered these shortcomings through study design and interpreting the occupancy parameter, psi, as 'probability of habitat used'. Probability of habitat use was modeled for each target species using single state or multistate models. A combination of the estimated probabilities of habitat use for jaguar and prey was selected to identify the final jaguar corridor. This protocol provides an efficient field methodology for identifying corridors for easily-identifiable species, across large study areas comprised of unprotected, private lands. ?? 2010 Elsevier Ltd.

Multiseason occupancy models for correlated replicate surveys

USGS Publications Warehouse

Hines, James; Nichols, James D.; Collazo, Jaime

2014-01-01

Occupancy surveys collecting data from adjacent (sometimes correlated) spatial replicates have become relatively popular for logistical reasons. Hines et al. (2010) presented one approach to modelling such data for single-season occupancy surveys. Here, we present a multiseason analogue of this model (with corresponding software) for inferences about occupancy dynamics. We include a new parameter to deal with the uncertainty associated with the first spatial replicate for both single-season and multiseason models. We use a case study, based on the brown-headed nuthatch, to assess the need for these models when analysing data from the North American Breeding Bird Survey (BBS), and we test various hypotheses about occupancy dynamics for this species in the south-eastern United States. The new model permits inference about local probabilities of extinction, colonization and occupancy for sampling conducted over multiple seasons. The model performs adequately, based on a small simulation study and on results of the case study analysis. The new model incorporating correlated replicates was strongly favoured by model selection for the BBS data for brown-headed nuthatch (Sitta pusilla). Latitude was found to be an important source of variation in local colonization and occupancy probabilities for brown-headed nuthatch, with both probabilities being higher near the centre of the species range, as opposed to more northern and southern areas. We recommend this new occupancy model for detection–nondetection studies that use potentially correlated replicates.

A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application

PubMed Central

Zhang, Yongsheng; Wei, Heng; Zheng, Kangning

2017-01-01

Considering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, the formulation, estimation and application of a constrained multinomial probit (CMNP) route choice model in the metro network are carried out in this paper. The utility function is formulated as three components: the compensatory component is a function of influencing factors; the non-compensatory component measures the impacts of routes set on utility; following a multivariate normal distribution, the covariance of error component is structured into three parts, representing the correlation among routes, the transfer variance of route, and the unobserved variance respectively. Considering multidimensional integrals of the multivariate normal probability density function, the CMNP model is rewritten as Hierarchical Bayes formula and M-H sampling algorithm based Monte Carlo Markov Chain approach is constructed to estimate all parameters. Based on Guangzhou Metro data, reliable estimation results are gained. Furthermore, the proposed CMNP model also shows a good forecasting performance for the route choice probabilities calculation and a good application performance for transfer flow volume prediction. PMID:28591188

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

Rejecting probability summation for radial frequency patterns, not so Quick!

PubMed

Baldwin, Alex S; Schmidtmann, Gunnar; Kingdom, Frederick A A; Hess, Robert F

2016-05-01

Radial frequency (RF) patterns are used to assess how the visual system processes shape. They are thought to be detected globally. This is supported by studies that have found summation for RF patterns to be greater than what is possible if the parts were being independently detected and performance only then improved with an increasing number of cycles by probability summation between them. However, the model of probability summation employed in these previous studies was based on High Threshold Theory (HTT), rather than Signal Detection Theory (SDT). We conducted rating scale experiments to investigate the receiver operating characteristics. We find these are of the curved form predicted by SDT, rather than the straight lines predicted by HTT. This means that to test probability summation we must use a model based on SDT. We conducted a set of summation experiments finding that thresholds decrease as the number of modulated cycles increases at approximately the same rate as previously found. As this could be consistent with either additive or probability summation, we performed maximum-likelihood fitting of a set of summation models (Matlab code provided in our Supplementary material) and assessed the fits using cross validation. We find we are not able to distinguish whether the responses to the parts of an RF pattern are combined by additive or probability summation, because the predictions are too similar. We present similar results for summation between separate RF patterns, suggesting that the summation process there may be the same as that within a single RF. Copyright © 2016 Elsevier Ltd. All rights reserved.

Controllable uncertain opinion diffusion under confidence bound and unpredicted diffusion probability

NASA Astrophysics Data System (ADS)

Yan, Fuhan; Li, Zhaofeng; Jiang, Yichuan

2016-05-01

The issues of modeling and analyzing diffusion in social networks have been extensively studied in the last few decades. Recently, many studies focus on uncertain diffusion process. The uncertainty of diffusion process means that the diffusion probability is unpredicted because of some complex factors. For instance, the variety of individuals' opinions is an important factor that can cause uncertainty of diffusion probability. In detail, the difference between opinions can influence the diffusion probability, and then the evolution of opinions will cause the uncertainty of diffusion probability. It is known that controlling the diffusion process is important in the context of viral marketing and political propaganda. However, previous methods are hardly feasible to control the uncertain diffusion process of individual opinion. In this paper, we present suitable strategy to control this diffusion process based on the approximate estimation of the uncertain factors. We formulate a model in which the diffusion probability is influenced by the distance between opinions, and briefly discuss the properties of the diffusion model. Then, we present an optimization problem at the background of voting to show how to control this uncertain diffusion process. In detail, it is assumed that each individual can choose one of the two candidates or abstention based on his/her opinion. Then, we present strategy to set suitable initiators and their opinions so that the advantage of one candidate will be maximized at the end of diffusion. The results show that traditional influence maximization algorithms are not applicable to this problem, and our algorithm can achieve expected performance.

Survival and breeding advantages of larger Black Brant (Branta bernicla nigricans) goslings: Within- and among-cohort variation

USGS Publications Warehouse

Sedinger, J.S.; Chelgren, N.D.

2007-01-01

We examined the relationship between mass late in the first summer and survival and return to the natal breeding colony for 12 cohorts (1986-1997) of female Black Brant (Branta bernicla nigricans). We used Cormack-Jolly-Seber methods and the program MARK to analyze capture-recapture data. Models included two kinds of residuals from regressions of mass on days after peak of hatch when goslings were measured; one based on the entire sample (12 cohorts) and the other based only on individuals in the same cohort. Some models contained date of peak of hatch (a group covariate related to lateness of nesting in that year) and mean cohort residual mass. Finally, models allowed survival to vary among cohorts. The best model of encounter probability included an effect of residual mass on encounter probability and allowed encounter probability to vary among age classes and across years. All competitive models contained an effect of one of the estimates of residual mass; relatively larger goslings survived their first year at higher rates. Goslings in cohorts from later years in the analysis tended to have lower first-year survival, after controlling for residual mass, which reflected the generally smaller mean masses for these cohorts but was potentially also a result of population-density effects additional to those on growth. Variation among cohorts in mean mass accounted for 56% of variation among cohorts in first-year survival. Encounter probabilities, which were correlated with breeding probability, increased with relative mass, which suggests that larger goslings not only survived at higher rates but also bred at higher rates. Although our findings support the well-established linkage between gosling mass and fitness, they suggest that additional environmental factors also influence first-year survival.

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

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.

Analysis of blocking probability for OFDM-based variable bandwidth optical network

NASA Astrophysics Data System (ADS)

Gong, Lei; Zhang, Jie; Zhao, Yongli; Lin, Xuefeng; Wu, Yuyao; Gu, Wanyi

2011-12-01

Orthogonal Frequency Division Multiplexing (OFDM) has recently been proposed as a modulation technique. For optical networks, because of its good spectral efficiency, flexibility, and tolerance to impairments, optical OFDM is much more flexible compared to traditional WDM systems, enabling elastic bandwidth transmissions, and optical networking is the future trend of development. In OFDM-based optical network the research of blocking rate has very important significance for network assessment. Current research for WDM network is basically based on a fixed bandwidth, in order to accommodate the future business and the fast-changing development of optical network, our study is based on variable bandwidth OFDM-based optical networks. We apply the mathematical analysis and theoretical derivation, based on the existing theory and algorithms, research blocking probability of the variable bandwidth of optical network, and then we will build a model for blocking probability.

Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty

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

Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.

2004-03-01

The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates basedmore » on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four projections, and associated kriging variances, were averaged using the posterior model probabilities as weights. Finally, cross-validation was conducted by eliminating from consideration all data from one borehole at a time, repeating the above process, and comparing the predictive capability of the model-averaged result with that of each individual model. Using two quantitative measures of comparison, the model-averaged result was superior to any individual geostatistical model of log permeability considered.« less

Spacing distribution functions for the one-dimensional point-island model with irreversible attachment

NASA Astrophysics Data System (ADS)

González, Diego Luis; Pimpinelli, Alberto; Einstein, T. L.

2011-07-01

We study the configurational structure of the point-island model for epitaxial growth in one dimension. In particular, we calculate the island gap and capture zone distributions. Our model is based on an approximate description of nucleation inside the gaps. Nucleation is described by the joint probability density pnXY(x,y), which represents the probability density to have nucleation at position x within a gap of size y. Our proposed functional form for pnXY(x,y) describes excellently the statistical behavior of the system. We compare our analytical model with extensive numerical simulations. Our model retains the most relevant physical properties of the system.

IRT Models for Ability-Based Guessing

ERIC Educational Resources Information Center

Martin, Ernesto San; del Pino, Guido; De Boeck, Paul

2006-01-01

An ability-based guessing model is formulated and applied to several data sets regarding educational tests in language and in mathematics. The formulation of the model is such that the probability of a correct guess does not only depend on the item but also on the ability of the individual, weighted with a general discrimination parameter. By so…

Contrasting support for alternative models of genomic variation based on microhabitat preference: species-specific effects of climate change in alpine sedges.

PubMed

Massatti, Rob; Knowles, L Lacey

2016-08-01

Deterministic processes may uniquely affect codistributed species' phylogeographic patterns such that discordant genetic variation among taxa is predicted. Yet, explicitly testing expectations of genomic discordance in a statistical framework remains challenging. Here, we construct spatially and temporally dynamic models to investigate the hypothesized effect of microhabitat preferences on the permeability of glaciated regions to gene flow in two closely related montane species. Utilizing environmental niche models from the Last Glacial Maximum and the present to inform demographic models of changes in habitat suitability over time, we evaluate the relative probabilities of two alternative models using approximate Bayesian computation (ABC) in which glaciated regions are either (i) permeable or (ii) a barrier to gene flow. Results based on the fit of the empirical data to data sets simulated using a spatially explicit coalescent under alternative models indicate that genomic data are consistent with predictions about the hypothesized role of microhabitat in generating discordant patterns of genetic variation among the taxa. Specifically, a model in which glaciated areas acted as a barrier was much more probable based on patterns of genomic variation in Carex nova, a wet-adapted species. However, in the dry-adapted Carex chalciolepis, the permeable model was more probable, although the difference in the support of the models was small. This work highlights how statistical inferences can be used to distinguish deterministic processes that are expected to result in discordant genomic patterns among species, including species-specific responses to climate change. © 2016 John Wiley & Sons Ltd.

A Quantum Probability Model of Causal Reasoning

PubMed Central

Trueblood, Jennifer S.; Busemeyer, Jerome R.

2012-01-01

People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause) with diagnostic judgments (i.e., the conditional probability of a cause given an effect). The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment. PMID:22593747

Probability-based collaborative filtering model for predicting gene-disease associations.

PubMed

Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-12-28

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

Simulation-Based Model Checking for Nondeterministic Systems and Rare Events

DTIC Science & Technology

2016-03-24

year, we have investigated AO* search and Monte Carlo Tree Search algorithms to complement and enhance CMU’s SMCMDP. 1 Final Report, March 14... tree , so we can use it to find the probability of reachability for a property in PRISM’s Probabilistic LTL. By finding the maximum probability of...savings, particularly when handling very large models. 2.3 Monte Carlo Tree Search The Monte Carlo sampling process in SMCMDP can take a long time to

Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models.

PubMed

Butler, T; Graham, L; Estep, D; Dawson, C; Westerink, J J

2015-04-01

The uncertainty in spatially heterogeneous Manning's n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented. Technical details that arise in practice by applying the framework to determine the Manning's n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of "condition" for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. This notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning's n parameter and the effect on model predictions is analyzed.

Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models

NASA Astrophysics Data System (ADS)

Butler, T.; Graham, L.; Estep, D.; Dawson, C.; Westerink, J. J.

2015-04-01

The uncertainty in spatially heterogeneous Manning's n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented. Technical details that arise in practice by applying the framework to determine the Manning's n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of "condition" for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. This notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning's n parameter and the effect on model predictions is analyzed.

Short-term droughts forecast using Markov chain model in Victoria, Australia

NASA Astrophysics Data System (ADS)

Rahmat, Siti Nazahiyah; Jayasuriya, Niranjali; Bhuiyan, Muhammed A.

2017-07-01

A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.

Of goals and habits: age-related and individual differences in goal-directed decision-making.

PubMed

Eppinger, Ben; Walter, Maik; Heekeren, Hauke R; Li, Shu-Chen

2013-01-01

In this study we investigated age-related and individual differences in habitual (model-free) and goal-directed (model-based) decision-making. Specifically, we were interested in three questions. First, does age affect the balance between model-based and model-free decision mechanisms? Second, are these age-related changes due to age differences in working memory (WM) capacity? Third, can model-based behavior be affected by manipulating the distinctiveness of the reward value of choice options? To answer these questions we used a two-stage Markov decision task in in combination with computational modeling to dissociate model-based and model-free decision mechanisms. To affect model-based behavior in this task we manipulated the distinctiveness of reward probabilities of choice options. The results show age-related deficits in model-based decision-making, which are particularly pronounced if unexpected reward indicates the need for a shift in decision strategy. In this situation younger adults explore the task structure, whereas older adults show perseverative behavior. Consistent with previous findings, these results indicate that older adults have deficits in the representation and updating of expected reward value. We also observed substantial individual differences in model-based behavior. In younger adults high WM capacity is associated with greater model-based behavior and this effect is further elevated when reward probabilities are more distinct. However, in older adults we found no effect of WM capacity. Moreover, age differences in model-based behavior remained statistically significant, even after controlling for WM capacity. Thus, factors other than decline in WM, such as deficits in the in the integration of expected reward value into strategic decisions may contribute to the observed impairments in model-based behavior in older adults.

Of goals and habits: age-related and individual differences in goal-directed decision-making

PubMed Central

Eppinger, Ben; Walter, Maik; Heekeren, Hauke R.; Li, Shu-Chen

2013-01-01

In this study we investigated age-related and individual differences in habitual (model-free) and goal-directed (model-based) decision-making. Specifically, we were interested in three questions. First, does age affect the balance between model-based and model-free decision mechanisms? Second, are these age-related changes due to age differences in working memory (WM) capacity? Third, can model-based behavior be affected by manipulating the distinctiveness of the reward value of choice options? To answer these questions we used a two-stage Markov decision task in in combination with computational modeling to dissociate model-based and model-free decision mechanisms. To affect model-based behavior in this task we manipulated the distinctiveness of reward probabilities of choice options. The results show age-related deficits in model-based decision-making, which are particularly pronounced if unexpected reward indicates the need for a shift in decision strategy. In this situation younger adults explore the task structure, whereas older adults show perseverative behavior. Consistent with previous findings, these results indicate that older adults have deficits in the representation and updating of expected reward value. We also observed substantial individual differences in model-based behavior. In younger adults high WM capacity is associated with greater model-based behavior and this effect is further elevated when reward probabilities are more distinct. However, in older adults we found no effect of WM capacity. Moreover, age differences in model-based behavior remained statistically significant, even after controlling for WM capacity. Thus, factors other than decline in WM, such as deficits in the in the integration of expected reward value into strategic decisions may contribute to the observed impairments in model-based behavior in older adults. PMID:24399925

Uncertainty Analysis via Failure Domain Characterization: Unrestricted Requirement Functions

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2011-01-01

This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. The methods developed herein, which are based on nonlinear constrained optimization, are applicable to requirement functions whose functional dependency on the uncertainty is arbitrary and whose explicit form may even be unknown. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the assumed uncertainty model (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.

Electrical cable utilization for wave energy converters

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

Bull, Diana; Baca, Michael; Schenkman, Benjamin

Here, this paper investigates the suitability of sizing the electrical export cable based on the rating of the contributing WECs within a farm. These investigations have produced a new methodology to evaluate the probabilities associated with peak power values on an annual basis. It has been shown that the peaks in pneumatic power production will follow an exponential probability function for a linear model. A methodology to combine all the individual probability functions into an annual view has been demonstrated on pneumatic power production by a Backward Bent Duct Buoy (BBDB). These investigations have also resulted in a highly simplifiedmore » and perfunctory model of installed cable cost as a function of voltage and conductor cross-section. This work solidifies the need to determine electrical export cable rating based on expected energy delivery as opposed to device rating as small decreases in energy delivery can result in cost savings.« less

Electrical cable utilization for wave energy converters

DOE PAGES

Bull, Diana; Baca, Michael; Schenkman, Benjamin

2018-04-27

Here, this paper investigates the suitability of sizing the electrical export cable based on the rating of the contributing WECs within a farm. These investigations have produced a new methodology to evaluate the probabilities associated with peak power values on an annual basis. It has been shown that the peaks in pneumatic power production will follow an exponential probability function for a linear model. A methodology to combine all the individual probability functions into an annual view has been demonstrated on pneumatic power production by a Backward Bent Duct Buoy (BBDB). These investigations have also resulted in a highly simplifiedmore » and perfunctory model of installed cable cost as a function of voltage and conductor cross-section. This work solidifies the need to determine electrical export cable rating based on expected energy delivery as opposed to device rating as small decreases in energy delivery can result in cost savings.« less

Exact and Approximate Probabilistic Symbolic Execution

NASA Technical Reports Server (NTRS)

Luckow, Kasper; Pasareanu, Corina S.; Dwyer, Matthew B.; Filieri, Antonio; Visser, Willem

2014-01-01

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes.

File compression and encryption based on LLS and arithmetic coding

NASA Astrophysics Data System (ADS)

Yu, Changzhi; Li, Hengjian; Wang, Xiyu

2018-03-01

e propose a file compression model based on arithmetic coding. Firstly, the original symbols, to be encoded, are input to the encoder one by one, we produce a set of chaotic sequences by using the Logistic and sine chaos system(LLS), and the values of this chaotic sequences are randomly modified the Upper and lower limits of current symbols probability. In order to achieve the purpose of encryption, we modify the upper and lower limits of all character probabilities when encoding each symbols. Experimental results show that the proposed model can achieve the purpose of data encryption while achieving almost the same compression efficiency as the arithmetic coding.

Holocene paleoseismicity, temporal clustering, and probabilities of future large (M > 7) earthquakes on the Wasatch fault zone, Utah

USGS Publications Warehouse

McCalpin, J.P.; Nishenko, S.P.

1996-01-01

The chronology of M>7 paleoearthquakes on the central five segments of the Wasatch fault zone (WFZ) is one of the best dated in the world and contains 16 earthquakes in the past 5600 years with an average repeat time of 350 years. Repeat times for individual segments vary by a factor of 2, and range from about 1200 to 2600 years. Four of the central five segments ruptured between ??? 620??30 and 1230??60 calendar years B.P. The remaining segment (Brigham City segment) has not ruptured in the past 2120??100 years. Comparison of the WFZ space-time diagram of paleoearthquakes with synthetic paleoseismic histories indicates that the observed temporal clusters and gaps have about an equal probability (depending on model assumptions) of reflecting random coincidence as opposed to intersegment contagion. Regional seismicity suggests that for exposure times of 50 and 100 years, the probability for an earthquake of M>7 anywhere within the Wasatch Front region, based on a Poisson model, is 0.16 and 0.30, respectively. A fault-specific WFZ model predicts 50 and 100 year probabilities for a M>7 earthquake on the WFZ itself, based on a Poisson model, as 0.13 and 0.25, respectively. In contrast, segment-specific earthquake probabilities that assume quasi-periodic recurrence behavior on the Weber, Provo, and Nephi segments are less (0.01-0.07 in 100 years) than the regional or fault-specific estimates (0.25-0.30 in 100 years), due to the short elapsed times compared to average recurrence intervals on those segments. The Brigham City and Salt Lake City segments, however, have time-dependent probabilities that approach or exceed the regional and fault specific probabilities. For the Salt Lake City segment, these elevated probabilities are due to the elapsed time being approximately equal to the average late Holocene recurrence time. For the Brigham City segment, the elapsed time is significantly longer than the segment-specific late Holocene recurrence time.

Does probability guided hysteroscopy reduce costs in women investigated for postmenopausal bleeding?

PubMed

Breijer, M C; van Hanegem, N; Visser, N C M; Verheijen, R H M; Mol, B W J; Pijnenborg, J M A; Opmeer, B C; Timmermans, A

2015-01-01

To evaluate whether a model to predict a failed endometrial biopsy in women with postmenopausal bleeding (PMB) and a thickened endometrium can reduce costs without compromising diagnostic accuracy. Model based cost-minimization analysis. A decision analytic model was designed to compare two diagnostic strategies for women with PMB: (I) attempting office endometrial biopsy and performing outpatient hysteroscopy after failed biopsy and (II) predicted probability of a failed endometrial biopsy based on patient characteristics to guide the decision for endometrial biopsy or immediate hysteroscopy. Robustness of assumptions regarding costs was evaluated in sensitivity analyses. Costs for the different strategies. At different cut-offs for the predicted probability of failure of an endometrial biopsy, strategy I was generally less expensive than strategy II. The costs for strategy I were always € 460; the costs for strategy II varied between € 457 and € 475. At a 65% cut-off, a possible saving of € 3 per woman could be achieved. Individualizing the decision to perform an endometrial biopsy or immediate hysteroscopy in women presenting with postmenopausal bleeding based on patient characteristics does not increase the efficiency of the diagnostic work-up.

A model to predict progression in brain-injured patients.

PubMed

Tommasino, N; Forteza, D; Godino, M; Mizraji, R; Alvarez, I

2014-11-01

The study of brain death (BD) epidemiology and the acute brain injury (ABI) progression profile is important to improve public health programs, organ procurement strategies, and intensive care unit (ICU) protocols. The purpose of this study was to analyze the ABI progression profile among patients admitted to ICUs with a Glasgow Coma Score (GCS) ≤8, as well as establishing a prediction model of probability of death and BD. This was a retrospective analysis of prospective data that included all brain-injured patients with GCS ≤8 admitted to a total of four public and private ICUs in Uruguay (N = 1447). The independent predictor factors of death and BD were studied using logistic regression analysis. A hierarchical model consisting of 2 nested logit regression models was then created. With these models, the probabilities of death, BD, and death by cardiorespiratory arrest were analyzed. In the first regression, we observed that as the GCS decreased and age increased, the probability of death rose. Each additional year of age increased the probability of death by 0.014. In the second model, however, BD risk decreased with each year of age. The presence of swelling, mass effect, and/or space-occupying lesion increased BD risk for the same given GCS. In the presence of injuries compatible with intracranial hypertension, age behaved as a protective factor that reduced the probability of BD. Based on the analysis of the local epidemiology, a model to predict the probability of death and BD can be developed. The organ potential donation of a country, region, or hospital can be predicted on the basis of this model, customizing it to each specific situation.

Weighing Clinical Evidence Using Patient Preferences: An Application of Probabilistic Multi-Criteria Decision Analysis.

PubMed

Broekhuizen, Henk; IJzerman, Maarten J; Hauber, A Brett; Groothuis-Oudshoorn, Catharina G M

2017-03-01

The need for patient engagement has been recognized by regulatory agencies, but there is no consensus about how to operationalize this. One approach is the formal elicitation and use of patient preferences for weighing clinical outcomes. The aim of this study was to demonstrate how patient preferences can be used to weigh clinical outcomes when both preferences and clinical outcomes are uncertain by applying a probabilistic value-based multi-criteria decision analysis (MCDA) method. Probability distributions were used to model random variation and parameter uncertainty in preferences, and parameter uncertainty in clinical outcomes. The posterior value distributions and rank probabilities for each treatment were obtained using Monte-Carlo simulations. The probability of achieving the first rank is the probability that a treatment represents the highest value to patients. We illustrated our methodology for a simplified case on six HIV treatments. Preferences were modeled with normal distributions and clinical outcomes were modeled with beta distributions. The treatment value distributions showed the rank order of treatments according to patients and illustrate the remaining decision uncertainty. This study demonstrated how patient preference data can be used to weigh clinical evidence using MCDA. The model takes into account uncertainty in preferences and clinical outcomes. The model can support decision makers during the aggregation step of the MCDA process and provides a first step toward preference-based personalized medicine, yet requires further testing regarding its appropriate use in real-world settings.

On the probability of extinction of the Haiti cholera epidemic

NASA Astrophysics Data System (ADS)

Bertuzzo, Enrico; Finger, Flavio; Mari, Lorenzo; Gatto, Marino; Rinaldo, Andrea

2014-05-01

Nearly 3 years after its appearance in Haiti, cholera has already exacted more than 8,200 deaths and 670,000 reported cases and it is feared to become endemic. However, no clear evidence of a stable environmental reservoir of pathogenic Vibrio cholerae, the infective agent of the disease, has emerged so far, suggesting that the transmission cycle of the disease is being maintained by bacteria freshly shed by infected individuals. Thus in principle cholera could possibly be eradicated from Haiti. Here, we develop a framework for the estimation of the probability of extinction of the epidemic based on current epidemiological dynamics and health-care practice. Cholera spreading is modelled by an individual-based spatially-explicit stochastic model that accounts for the dynamics of susceptible, infected and recovered individuals hosted in different local communities connected through hydrologic and human mobility networks. Our results indicate that the probability that the epidemic goes extinct before the end of 2016 is of the order of 1%. This low probability of extinction highlights the need for more targeted and effective interventions to possibly stop cholera in Haiti.

An occupancy-based quantification of the highly imperiled status of desert fishes of the southwestern United States.

PubMed

Budy, Phaedra; Conner, Mary M; Salant, Nira L; Macfarlane, William W

2015-08-01

Desert fishes are some of the most imperiled vertebrates worldwide due to their low economic worth and because they compete with humans for water. An ecological complex of fishes, 2 suckers (Catostomus latipinnis, Catostomus discobolus) and a chub (Gila robusta) (collectively managed as the so-called three species) are endemic to the U.S. Colorado River Basin, are affected by multiple stressors, and have allegedly declined dramatically. We built a series of occupancy models to determine relationships between trends in occupancy, local extinction, and local colonization rates, identify potential limiting factors, and evaluate the suitability of managing the 3 species collectively. For a historical period (1889-2011), top performing models (AICc) included a positive time trend in local extinction probability and a negative trend in local colonization probability. As flood frequency decreased post-development local extinction probability increased. By the end of the time series, 47% (95% CI 34-61) and 15% (95% CI 6-33) of sites remained occupied by the suckers and the chub, respectively, and models with the 2 species of sucker as one group and the chub as the other performed best. For a contemporary period (2001-2011), top performing (based on AICc ) models included peak annual discharge. As peak discharge increased, local extinction probability decreased and local colonization probability increased. For the contemporary period, results of models that split all 3 species into separate groups were similar to results of models that combined the 2 suckers but not the chub. Collectively, these results confirmed that declines in these fishes were strongly associated with water development and that relative to their historic distribution all 3 species have declined dramatically. Further, the chub was distinct in that it declined the most dramatically and therefore may need to be managed separately. Our modeling approach may be useful in other situations in which targeted data are sparse and conservation status and best management approach for multiple species are uncertain. © 2015 Society for Conservation Biology.

Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model.

PubMed

Steven Ernest, C; Nyberg, Joakim; Karlsson, Mats O; Hooker, Andrew C

2014-12-01

D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM(total)). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIM(total) was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM(total). Through the use of an approximate analytic solution and weighting schemes, the FIM(total) for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.

Estimating the effect of treatment rate changes when treatment benefits are heterogeneous: antibiotics and otitis media.

PubMed

Park, Tae-Ryong; Brooks, John M; Chrischilles, Elizabeth A; Bergus, George

2008-01-01

Contrast methods to assess the health effects of a treatment rate change when treatment benefits are heterogeneous across patients. Antibiotic prescribing for children with otitis media (OM) in Iowa Medicaid is the empirical example. Instrumental variable (IV) and linear probability model (LPM) are used to estimate the effect of antibiotic treatments on cure probabilities for children with OM in Iowa Medicaid. Local area physician supply per capita is the instrument in the IV models. Estimates are contrasted in terms of their ability to make inferences for patients whose treatment choices may be affected by a change in population treatment rates. The instrument was positively related to the probability of being prescribed an antibiotic. LPM estimates showed a positive effect of antibiotics on OM patient cure probability while IV estimates showed no relationship between antibiotics and patient cure probability. Linear probability model estimation yields the average effects of the treatment on patients that were treated. IV estimation yields the average effects for patients whose treatment choices were affected by the instrument. As antibiotic treatment effects are heterogeneous across OM patients, our estimates from these approaches are aligned with clinical evidence and theory. The average estimate for treated patients (higher severity) from the LPM model is greater than estimates for patients whose treatment choices are affected by the instrument (lower severity) from the IV models. Based on our IV estimates it appears that lowering antibiotic use in OM patients in Iowa Medicaid did not result in lost cures.

Estimating the Exceedance Probability of the Reservoir Inflow Based on the Long-Term Weather Outlooks

NASA Astrophysics Data System (ADS)

Huang, Q. Z.; Hsu, S. Y.; Li, M. H.

2016-12-01

The long-term streamflow prediction is important not only to estimate water-storage of a reservoir but also to the surface water intakes, which supply people's livelihood, agriculture, and industry. Climatology forecasts of streamflow have been traditionally used for calculating the exceedance probability curve of streamflow and water resource management. In this study, we proposed a stochastic approach to predict the exceedance probability curve of long-term streamflow with the seasonal weather outlook from Central Weather Bureau (CWB), Taiwan. The approach incorporates a statistical downscale weather generator and a catchment-scale hydrological model to convert the monthly outlook into daily rainfall and temperature series and to simulate the streamflow based on the outlook information. Moreover, we applied Bayes' theorem to derive a method for calculating the exceedance probability curve of the reservoir inflow based on the seasonal weather outlook and its imperfection. The results show that our approach can give the exceedance probability curves reflecting the three-month weather outlook and its accuracy. We also show how the improvement of the weather outlook affects the predicted exceedance probability curves of the streamflow. Our approach should be useful for the seasonal planning and management of water resource and their risk assessment.

Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data

PubMed Central

Wu, Lun; Zhi, Ye; Sui, Zhengwei; Liu, Yu

2014-01-01

Most existing human mobility literature focuses on exterior characteristics of movements but neglects activities, the driving force that underlies human movements. In this research, we combine activity-based analysis with a movement-based approach to model the intra-urban human mobility observed from about 15 million check-in records during a yearlong period in Shanghai, China. The proposed model is activity-based and includes two parts: the transition of travel demands during a specific time period and the movement between locations. For the first part, we find the transition probability between activities varies over time, and then we construct a temporal transition probability matrix to represent the transition probability of travel demands during a time interval. For the second part, we suggest that the travel demands can be divided into two classes, locationally mandatory activity (LMA) and locationally stochastic activity (LSA), according to whether the demand is associated with fixed location or not. By judging the combination of predecessor activity type and successor activity type we determine three trip patterns, each associated with a different decay parameter. To validate the model, we adopt the mechanism of an agent-based model and compare the simulated results with the observed pattern from the displacement distance distribution, the spatio-temporal distribution of activities, and the temporal distribution of travel demand transitions. The results show that the simulated patterns fit the observed data well, indicating that these findings open new directions for combining activity-based analysis with a movement-based approach using social media check-in data. PMID:24824892

Fire-probability maps for the Brazilian Amazonia

NASA Astrophysics Data System (ADS)

Cardoso, M.; Nobre, C.; Obregon, G.; Sampaio, G.

2009-04-01

Most fires in Amazonia result from the combination between climate and land-use factors. They occur mainly in the dry season and are used as an inexpensive tool for land clearing and management. However, their unintended consequences are of important concern. Fire emissions are the most important sources of greenhouse gases and aerosols in the region, accidental fires are a major threat to protected areas, and frequent fires may lead to permanent conversion of forest areas into savannas. Fire-activity models have thus become important tools for environmental analyses in Amazonia. They are used, for example, in warning systems for monitoring the risk of burnings in protected areas, to improve the description of biogeochemical cycles and vegetation composition in ecosystem models, and to help estimate the long-term potential for savannas in biome models. Previous modeling studies for the whole region were produced in units of satellite fire pixels, which complicate their direct use for environmental applications. By reinterpreting remote-sensing based data using a statistical approach, we were able to calibrate models for the whole region in units of probability, or chance of fires to occur. The application of these models for years 2005 and 2006 provided maps of fire potential at 3-month and 0.25-deg resolution as a function of precipitation and distance from main roads. In both years, the performance of the resulting maps was better for the period July-September. During these months, most of satellite-based fire observations were located in areas with relatively high chance of fire, as determined by the modeled probability maps. In addition to reproduce reasonably well the areas presenting maximum fire activity as detected by remote sensing, the new results in units of probability are easier to apply than previous estimates from fire-pixel models.

Fire-probability maps for the Brazilian Amazonia

NASA Astrophysics Data System (ADS)

Cardoso, Manoel; Sampaio, Gilvan; Obregon, Guillermo; Nobre, Carlos

2010-05-01

Most fires in Amazonia result from the combination between climate and land-use factors. They occur mainly in the dry season and are used as an inexpensive tool for land clearing and management. However, their unintended consequences are of important concern. Fire emissions are the most important sources of greenhouse gases and aerosols in the region, accidental fires are a major threat to protected areas, and frequent fires may lead to permanent conversion of forest areas into savannas. Fire-activity models have thus become important tools for environmental analyses in Amazonia. They are used, for example, in warning systems for monitoring the risk of burnings in protected areas, to improve the description of biogeochemical cycles and vegetation composition in ecosystem models, and to help estimate the long-term potential for savannas in biome models. Previous modeling studies for the whole region were produced in units of satellite fire pixels, which complicate their direct use for environmental applications. By reinterpreting remote-sensing based data using a statistical approach, we were able to calibrate models for the whole region in units of probability, or chance of fires to occur. The application of these models for years 2005 and 2006 provided maps of fire potential at 3-month and 0.25-deg resolution as a function of precipitation and distance from main roads. In both years, the performance of the resulting maps was better for the period July-September. During these months, most of satellite-based fire observations were located in areas with relatively high chance of fire, as determined by the modeled probability maps. In addition to reproduce reasonably well the areas presenting maximum fire activity as detected by remote sensing, the new results in units of probability are easier to apply than previous estimates from fire-pixel models.

Spatial analysis of land use and shallow groundwater vulnerability in the watershed adjacent to Assateague Island National Seashore, Maryland and Virginia, USA

USGS Publications Warehouse

LaMotte, A.E.; Greene, E.A.

2007-01-01

Spatial relations between land use and groundwater quality in the watershed adjacent to Assateague Island National Seashore, Maryland and Virginia, USA were analyzed by the use of two spatial models. One model used a logit analysis and the other was based on geostatistics. The models were developed and compared on the basis of existing concentrations of nitrate as nitrogen in samples from 529 domestic wells. The models were applied to produce spatial probability maps that show areas in the watershed where concentrations of nitrate in groundwater are likely to exceed a predetermined management threshold value. Maps of the watershed generated by logistic regression and probability kriging analysis showing where the probability of nitrate concentrations would exceed 3 mg/L (>0.50) compared favorably. Logistic regression was less dependent on the spatial distribution of sampled wells, and identified an additional high probability area within the watershed that was missed by probability kriging. The spatial probability maps could be used to determine the natural or anthropogenic factors that best explain the occurrence and distribution of elevated concentrations of nitrate (or other constituents) in shallow groundwater. This information can be used by local land-use planners, ecologists, and managers to protect water supplies and identify land-use planning solutions and monitoring programs in vulnerable areas. ?? 2006 Springer-Verlag.

The influence of maximum magnitude on seismic-hazard estimates in the Central and Eastern United States

USGS Publications Warehouse

Mueller, C.S.

2010-01-01

I analyze the sensitivity of seismic-hazard estimates in the central and eastern United States (CEUS) to maximum magnitude (mmax) by exercising the U.S. Geological Survey (USGS) probabilistic hazard model with several mmax alternatives. Seismicity-based sources control the hazard in most of the CEUS, but data seldom provide an objective basis for estimating mmax. The USGS uses preferred mmax values of moment magnitude 7.0 and 7.5 for the CEUS craton and extended margin, respectively, derived from data in stable continental regions worldwide. Other approaches, for example analysis of local seismicity or judgment about a source's seismogenic potential, often lead to much smaller mmax. Alternative models span the mmax ranges from the 1980s Electric Power Research Institute/Seismicity Owners Group (EPRI/SOG) analysis. Results are presented as haz-ard ratios relative to the USGS national seismic hazard maps. One alternative model specifies mmax equal to moment magnitude 5.0 and 5.5 for the craton and margin, respectively, similar to EPRI/SOG for some sources. For 2% probability of exceedance in 50 years (about 0.0004 annual probability), the strong mmax truncation produces hazard ratios equal to 0.35-0.60 for 0.2-sec spectral acceleration, and 0.15-0.35 for 1.0-sec spectral acceleration. Hazard-controlling earthquakes interact with mmax in complex ways. There is a relatively weak dependence on probability level: hazardratios increase 0-15% for 0.002 annual exceedance probability and decrease 5-25% for 0.00001 annual exceedance probability. Although differences at some sites are tempered when faults are added, mmax clearly accounts for some of the discrepancies that are seen in comparisons between USGS-based and EPRI/SOG-based hazard results.

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

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

Li Yupeng, E-mail: yupeng@ualberta.ca; Deutsch, Clayton V.

2012-06-15

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells.more » In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.« less

Log-Linear Models for Gene Association

PubMed Central

Hu, Jianhua; Joshi, Adarsh; Johnson, Valen E.

2009-01-01

We describe a class of log-linear models for the detection of interactions in high-dimensional genomic data. This class of models leads to a Bayesian model selection algorithm that can be applied to data that have been reduced to contingency tables using ranks of observations within subjects, and discretization of these ranks within gene/network components. Many normalization issues associated with the analysis of genomic data are thereby avoided. A prior density based on Ewens’ sampling distribution is used to restrict the number of interacting components assigned high posterior probability, and the calculation of posterior model probabilities is expedited by approximations based on the likelihood ratio statistic. Simulation studies are used to evaluate the efficiency of the resulting algorithm for known interaction structures. Finally, the algorithm is validated in a microarray study for which it was possible to obtain biological confirmation of detected interactions. PMID:19655032

Stochastic modelling of animal movement.

PubMed

Smouse, Peter E; Focardi, Stefano; Moorcroft, Paul R; Kie, John G; Forester, James D; Morales, Juan M

2010-07-27

Modern animal movement modelling derives from two traditions. Lagrangian models, based on random walk behaviour, are useful for multi-step trajectories of single animals. Continuous Eulerian models describe expected behaviour, averaged over stochastic realizations, and are usefully applied to ensembles of individuals. We illustrate three modern research arenas. (i) Models of home-range formation describe the process of an animal 'settling down', accomplished by including one or more focal points that attract the animal's movements. (ii) Memory-based models are used to predict how accumulated experience translates into biased movement choices, employing reinforced random walk behaviour, with previous visitation increasing or decreasing the probability of repetition. (iii) Lévy movement involves a step-length distribution that is over-dispersed, relative to standard probability distributions, and adaptive in exploring new environments or searching for rare targets. Each of these modelling arenas implies more detail in the movement pattern than general models of movement can accommodate, but realistic empiric evaluation of their predictions requires dense locational data, both in time and space, only available with modern GPS telemetry.

A risk-based multi-objective model for optimal placement of sensors in water distribution system

NASA Astrophysics Data System (ADS)

Naserizade, Sareh S.; Nikoo, Mohammad Reza; Montaseri, Hossein

2018-02-01

In this study, a new stochastic model based on Conditional Value at Risk (CVaR) and multi-objective optimization methods is developed for optimal placement of sensors in water distribution system (WDS). This model determines minimization of risk which is caused by simultaneous multi-point contamination injection in WDS using CVaR approach. The CVaR considers uncertainties of contamination injection in the form of probability distribution function and calculates low-probability extreme events. In this approach, extreme losses occur at tail of the losses distribution function. Four-objective optimization model based on NSGA-II algorithm is developed to minimize losses of contamination injection (through CVaR of affected population and detection time) and also minimize the two other main criteria of optimal placement of sensors including probability of undetected events and cost. Finally, to determine the best solution, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), as a subgroup of Multi Criteria Decision Making (MCDM) approach, is utilized to rank the alternatives on the trade-off curve among objective functions. Also, sensitivity analysis is done to investigate the importance of each criterion on PROMETHEE results considering three relative weighting scenarios. The effectiveness of the proposed methodology is examined through applying it to Lamerd WDS in the southwestern part of Iran. The PROMETHEE suggests 6 sensors with suitable distribution that approximately cover all regions of WDS. Optimal values related to CVaR of affected population and detection time as well as probability of undetected events for the best optimal solution are equal to 17,055 persons, 31 mins and 0.045%, respectively. The obtained results of the proposed methodology in Lamerd WDS show applicability of CVaR-based multi-objective simulation-optimization model for incorporating the main uncertainties of contamination injection in order to evaluate extreme value of losses in WDS.

Real Time Data Management for Estimating Probabilities of Incidents and Near Misses

NASA Astrophysics Data System (ADS)

Stanitsas, P. D.; Stephanedes, Y. J.

2011-08-01

Advances in real-time data collection, data storage and computational systems have led to development of algorithms for transport administrators and engineers that improve traffic safety and reduce cost of road operations. Despite these advances, problems in effectively integrating real-time data acquisition, processing, modelling and road-use strategies at complex intersections and motorways remain. These are related to increasing system performance in identification, analysis, detection and prediction of traffic state in real time. This research develops dynamic models to estimate the probability of road incidents, such as crashes and conflicts, and incident-prone conditions based on real-time data. The models support integration of anticipatory information and fee-based road use strategies in traveller information and management. Development includes macroscopic/microscopic probabilistic models, neural networks, and vector autoregressions tested via machine vision at EU and US sites.

Maximum Entropy Principle for Transportation

NASA Astrophysics Data System (ADS)

Bilich, F.; DaSilva, R.

2008-11-01

In this work we deal with modeling of the transportation phenomenon for use in the transportation planning process and policy-impact studies. The model developed is based on the dependence concept, i.e., the notion that the probability of a trip starting at origin i is dependent on the probability of a trip ending at destination j given that the factors (such as travel time, cost, etc.) which affect travel between origin i and destination j assume some specific values. The derivation of the solution of the model employs the maximum entropy principle combining a priori multinomial distribution with a trip utility concept. This model is utilized to forecast trip distributions under a variety of policy changes and scenarios. The dependence coefficients are obtained from a regression equation where the functional form is derived based on conditional probability and perception of factors from experimental psychology. The dependence coefficients encode all the information that was previously encoded in the form of constraints. In addition, the dependence coefficients encode information that cannot be expressed in the form of constraints for practical reasons, namely, computational tractability. The equivalence between the standard formulation (i.e., objective function with constraints) and the dependence formulation (i.e., without constraints) is demonstrated. The parameters of the dependence-based trip-distribution model are estimated, and the model is also validated using commercial air travel data in the U.S. In addition, policy impact analyses (such as allowance of supersonic flights inside the U.S. and user surcharge at noise-impacted airports) on air travel are performed.

An integrated approach coupling physically based models and probabilistic method to assess quantitatively landslide susceptibility at different scale: application to different geomorphological environments

NASA Astrophysics Data System (ADS)

Vandromme, Rosalie; Thiéry, Yannick; Sedan, Olivier; Bernardie, Séverine

2016-04-01

Landslide hazard assessment is the estimation of a target area where landslides of a particular type, volume, runout and intensity may occur within a given period. The first step to analyze landslide hazard consists in assessing the spatial and temporal failure probability (when the information is available, i.e. susceptibility assessment). Two types of approach are generally recommended to achieve this goal: (i) qualitative approach (i.e. inventory based methods and knowledge data driven methods) and (ii) quantitative approach (i.e. data-driven methods or deterministic physically based methods). Among quantitative approaches, deterministic physically based methods (PBM) are generally used at local and/or site-specific scales (1:5,000-1:25,000 and >1:5,000, respectively). The main advantage of these methods is the calculation of probability of failure (safety factor) following some specific environmental conditions. For some models it is possible to integrate the land-uses and climatic change. At the opposite, major drawbacks are the large amounts of reliable and detailed data (especially materials type, their thickness and the geotechnical parameters heterogeneity over a large area) and the fact that only shallow landslides are taking into account. This is why they are often used at site-specific scales (> 1:5,000). Thus, to take into account (i) materials' heterogeneity , (ii) spatial variation of physical parameters, (iii) different landslide types, the French Geological Survey (i.e. BRGM) has developed a physically based model (PBM) implemented in a GIS environment. This PBM couples a global hydrological model (GARDENIA®) including a transient unsaturated/saturated hydrological component with a physically based model computing the stability of slopes (ALICE®, Assessment of Landslides Induced by Climatic Events) based on the Morgenstern-Price method for any slip surface. The variability of mechanical parameters is handled by Monte Carlo approach. The probability to obtain a safety factor below 1 represents the probability of occurrence of a landslide for a given triggering event. The dispersion of the distribution gives the uncertainty of the result. Finally, a map is created, displaying a probability of occurrence for each computing cell of the studied area. In order to take into account the land-uses change, a complementary module integrating the vegetation effects on soil properties has been recently developed. Last years, the model has been applied at different scales for different geomorphological environments: (i) at regional scale (1:50,000-1:25,000) in French West Indies and French Polynesian islands (ii) at local scale (i.e.1:10,000) for two complex mountainous areas; (iii) at the site-specific scale (1:2,000) for one landslide. For each study the 3D geotechnical model has been adapted. The different studies have allowed : (i) to discuss the different factors included in the model especially the initial 3D geotechnical models; (ii) to precise the location of probable failure following different hydrological scenarii; (iii) to test the effects of climatic change and land-use on slopes for two cases. In that way, future changes in temperature, precipitation and vegetation cover can be analyzed, permitting to address the impacts of global change on landslides. Finally, results show that it is possible to obtain reliable information about future slope failures at different scale of work for different scenarii with an integrated approach. The final information about landslide susceptibility (i.e. probability of failure) can be integrated in landslide hazard assessment and could be an essential information source for future land planning. As it has been performed in the ANR Project SAMCO (Society Adaptation for coping with Mountain risks in a global change COntext), this analysis constitutes a first step in the chain for risk assessment for different climate and economical development scenarios, to evaluate the resilience of mountainous areas.

Characterize Framework for Igneous Activity at Yucca Mountain, Nevada

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

F. Perry; B. Youngs

2000-11-06

The purpose of this Analysis/Model (AMR) report is twofold. (1) The first is to present a conceptual framework of igneous activity in the Yucca Mountain region (YMR) consistent with the volcanic and tectonic history of this region and the assessment of this history by experts who participated in the Probabilistic Volcanic Hazard Analysis (PVHA) (CRWMS M&O 1996). Conceptual models presented in the PVHA are summarized and extended in areas in which new information has been presented. Alternative conceptual models are discussed as well as their impact on probability models. The relationship between volcanic source zones defined in the PVHA andmore » structural features of the YMR are described based on discussions in the PVHA and studies presented since the PVHA. (2) The second purpose of the AMR is to present probability calculations based on PVHA outputs. Probability distributions are presented for the length and orientation of volcanic dikes within the repository footprint and for the number of eruptive centers located within the repository footprint (conditional on the dike intersecting the repository). The probability of intersection of a basaltic dike within the repository footprint was calculated in the AMR ''Characterize Framework for Igneous Activity at Yucca Mountain, Nevada'' (CRWMS M&O 2000g) based on the repository footprint known as the Enhanced Design Alternative [EDA II, Design B (CRWMS M&O 1999a; Wilkins and Heath 1999)]. Then, the ''Site Recommendation Design Baseline'' (CRWMS M&O 2000a) initiated a change in the repository design, which is described in the ''Site Recommendation Subsurface Layout'' (CRWMS M&O 2000b). Consequently, the probability of intersection of a basaltic dike within the repository footprint has also been calculated for the current repository footprint, which is called the 70,000 Metric Tons of Uranium (MTU) No-Backfill Layout (CRWMS M&O 2000b). The calculations for both footprints are presented in this AMR. In addition, the probability of an eruptive center(s) forming within the repository footprint is calculated and presented in this AMR for both repository footprint designs. This latter type of calculation was not included in the PVHA.« less

Calibration of micromechanical parameters for DEM simulations by using the particle filter

NASA Astrophysics Data System (ADS)

Cheng, Hongyang; Shuku, Takayuki; Thoeni, Klaus; Yamamoto, Haruyuki

2017-06-01

The calibration of DEM models is typically accomplished by trail and error. However, the procedure lacks of objectivity and has several uncertainties. To deal with these issues, the particle filter is employed as a novel approach to calibrate DEM models of granular soils. The posterior probability distribution of the microparameters that give numerical results in good agreement with the experimental response of a Toyoura sand specimen is approximated by independent model trajectories, referred as `particles', based on Monte Carlo sampling. The soil specimen is modeled by polydisperse packings with different numbers of spherical grains. Prepared in `stress-free' states, the packings are subjected to triaxial quasistatic loading. Given the experimental data, the posterior probability distribution is incrementally updated, until convergence is reached. The resulting `particles' with higher weights are identified as the calibration results. The evolutions of the weighted averages and posterior probability distribution of the micro-parameters are plotted to show the advantage of using a particle filter, i.e., multiple solutions are identified for each parameter with known probabilities of reproducing the experimental response.

Beta Regression Finite Mixture Models of Polarization and Priming

ERIC Educational Resources Information Center

Smithson, Michael; Merkle, Edgar C.; Verkuilen, Jay

2011-01-01

This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture…

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach

PubMed Central

Chen, Yong; Hong, Chuan; Ning, Yang; Su, Xiao

2018-01-01

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach, and has several attractive features compared to the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, since the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration. PMID:26303591

Operational Earthquake Forecasting and Decision-Making in a Low-Probability Environment

NASA Astrophysics Data System (ADS)

Jordan, T. H.; the International Commission on Earthquake ForecastingCivil Protection

2011-12-01

Operational earthquake forecasting (OEF) is the dissemination of authoritative information about the time dependence of seismic hazards to help communities prepare for potentially destructive earthquakes. Most previous work on the public utility of OEF has anticipated that forecasts would deliver high probabilities of large earthquakes; i.e., deterministic predictions with low error rates (false alarms and failures-to-predict) would be possible. This expectation has not been realized. An alternative to deterministic prediction is probabilistic forecasting based on empirical statistical models of aftershock triggering and seismic clustering. During periods of high seismic activity, short-term earthquake forecasts can attain prospective probability gains in excess of 100 relative to long-term forecasts. The utility of such information is by no means clear, however, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing OEF in this sort of "low-probability environment." The need to move more quickly has been underscored by recent seismic crises, such as the 2009 L'Aquila earthquake sequence, in which an anxious public was confused by informal and inaccurate earthquake predictions. After the L'Aquila earthquake, the Italian Department of Civil Protection appointed an International Commission on Earthquake Forecasting (ICEF), which I chaired, to recommend guidelines for OEF utilization. Our report (Ann. Geophys., 54, 4, 2011; doi: 10.4401/ag-5350) concludes: (a) Public sources of information on short-term probabilities should be authoritative, scientific, open, and timely, and need to convey epistemic uncertainties. (b) Earthquake probabilities should be based on operationally qualified, regularly updated forecasting systems. (c) All operational models should be evaluated for reliability and skill by retrospective testing, and the models should be under continuous prospective testing against long-term forecasts and alternative time-dependent models. (d) Short-term models used in operational forecasting should be consistent with the long-term forecasts used in probabilistic seismic hazard analysis. (e) Alert procedures should be standardized to facilitate decisions at different levels of government, based in part on objective analysis of costs and benefits. (f) In establishing alert protocols, consideration should also be given to the less tangible aspects of value-of-information, such as gains in psychological preparedness and resilience. Authoritative statements of increased risk, even when the absolute probability is low, can provide a psychological benefit to the public by filling information vacuums that lead to informal predictions and misinformation. Formal OEF procedures based on probabilistic forecasting appropriately separate hazard estimation by scientists from the decision-making role of civil protection authorities. The prosecution of seven Italian scientists on manslaughter charges stemming from their actions before the L'Aquila earthquake makes clear why this separation should be explicit in defining OEF protocols.

Seismic fragility assessment of low-rise stone masonry buildings

NASA Astrophysics Data System (ADS)

Abo-El-Ezz, Ahmad; Nollet, Marie-José; Nastev, Miroslav

2013-03-01

Many historic buildings in old urban centers in Eastern Canada are made of stone masonry reputed to be highly vulnerable to seismic loads. Seismic risk assessment of stone masonry buildings is therefore the first step in the risk mitigation process to provide adequate planning for retrofit and preservation of historical urban centers. This paper focuses on development of analytical displacement-based fragility curves reflecting the characteristics of existing stone masonry buildings in Eastern Canada. The old historic center of Quebec City has been selected as a typical study area. The standard fragility analysis combines the inelastic spectral displacement, a structure-dependent earthquake intensity measure, and the building damage state correlated to the induced building displacement. The proposed procedure consists of a three-step development process: (1) mechanics-based capacity model, (2) displacement-based damage model and (3) seismic demand model. The damage estimation for a uniform hazard scenario of 2% in 50 years probability of exceedance indicates that slight to moderate damage is the most probable damage experienced by these stone masonry buildings. Comparison is also made with fragility curves implicit in the seismic risk assessment tools Hazus and ELER. Hazus shows the highest probability of the occurrence of no to slight damage, whereas the highest probability of extensive and complete damage is predicted with ELER. This comparison shows the importance of the development of fragility curves specific to the generic construction characteristics in the study area and emphasizes the need for critical use of regional risk assessment tools and generated results.

Effects of long-term representations on free recall of unrelated words

PubMed Central

Katkov, Mikhail; Romani, Sandro

2015-01-01

Human memory stores vast amounts of information. Yet recalling this information is often challenging when specific cues are lacking. Here we consider an associative model of retrieval where each recalled item triggers the recall of the next item based on the similarity between their long-term neuronal representations. The model predicts that different items stored in memory have different probability to be recalled depending on the size of their representation. Moreover, items with high recall probability tend to be recalled earlier and suppress other items. We performed an analysis of a large data set on free recall and found a highly specific pattern of statistical dependencies predicted by the model, in particular negative correlations between the number of words recalled and their average recall probability. Taken together, experimental and modeling results presented here reveal complex interactions between memory items during recall that severely constrain recall capacity. PMID:25593296

A Looping-Based Model for Quenching Repression

PubMed Central

Pollak, Yaroslav; Goldberg, Sarah; Amit, Roee

2017-01-01

We model the regulatory role of proteins bound to looped DNA using a simulation in which dsDNA is represented as a self-avoiding chain, and proteins as spherical protrusions. We simulate long self-avoiding chains using a sequential importance sampling Monte-Carlo algorithm, and compute the probabilities for chain looping with and without a protrusion. We find that a protrusion near one of the chain’s termini reduces the probability of looping, even for chains much longer than the protrusion–chain-terminus distance. This effect increases with protrusion size, and decreases with protrusion-terminus distance. The reduced probability of looping can be explained via an eclipse-like model, which provides a novel inhibitory mechanism. We test the eclipse model on two possible transcription-factor occupancy states of the D. melanogaster eve 3/7 enhancer, and show that it provides a possible explanation for the experimentally-observed eve stripe 3 and 7 expression patterns. PMID:28085884

A Local-Realistic Model of Quantum Mechanics Based on a Discrete Spacetime

NASA Astrophysics Data System (ADS)

Sciarretta, Antonio

2018-01-01

This paper presents a realistic, stochastic, and local model that reproduces nonrelativistic quantum mechanics (QM) results without using its mathematical formulation. The proposed model only uses integer-valued quantities and operations on probabilities, in particular assuming a discrete spacetime under the form of a Euclidean lattice. Individual (spinless) particle trajectories are described as random walks. Transition probabilities are simple functions of a few quantities that are either randomly associated to the particles during their preparation, or stored in the lattice nodes they visit during the walk. QM predictions are retrieved as probability distributions of similarly-prepared ensembles of particles. The scenarios considered to assess the model comprise of free particle, constant external force, harmonic oscillator, particle in a box, the Delta potential, particle on a ring, particle on a sphere and include quantization of energy levels and angular momentum, as well as momentum entanglement.

Assessing performance and validating finite element simulations using probabilistic knowledge

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

Dolin, Ronald M.; Rodriguez, E. A.

Two probabilistic approaches for assessing performance are presented. The first approach assesses probability of failure by simultaneously modeling all likely events. The probability each event causes failure along with the event's likelihood of occurrence contribute to the overall probability of failure. The second assessment method is based on stochastic sampling using an influence diagram. Latin-hypercube sampling is used to stochastically assess events. The overall probability of failure is taken as the maximum probability of failure of all the events. The Likelihood of Occurrence simulation suggests failure does not occur while the Stochastic Sampling approach predicts failure. The Likelihood of Occurrencemore » results are used to validate finite element predictions.« less

A backward Monte Carlo method for efficient computation of runaway probabilities in runaway electron simulation

NASA Astrophysics Data System (ADS)

Zhang, Guannan; Del-Castillo-Negrete, Diego

2017-10-01

Kinetic descriptions of RE are usually based on the bounced-averaged Fokker-Planck model that determines the PDFs of RE. Despite of the simplification involved, the Fokker-Planck equation can rarely be solved analytically and direct numerical approaches (e.g., continuum and particle-based Monte Carlo (MC)) can be time consuming specially in the computation of asymptotic-type observable including the runaway probability, the slowing-down and runaway mean times, and the energy limit probability. Here we present a novel backward MC approach to these problems based on backward stochastic differential equations (BSDEs). The BSDE model can simultaneously describe the PDF of RE and the runaway probabilities by means of the well-known Feynman-Kac theory. The key ingredient of the backward MC algorithm is to place all the particles in a runaway state and simulate them backward from the terminal time to the initial time. As such, our approach can provide much faster convergence than the brute-force MC methods, which can significantly reduce the number of particles required to achieve a prescribed accuracy. Moreover, our algorithm can be parallelized as easy as the direct MC code, which paves the way for conducting large-scale RE simulation. This work is supported by DOE FES and ASCR under the Contract Numbers ERKJ320 and ERAT377.

New estimates of lethality of sea lamprey (Petromyzon marinus) attacks on lake trout (Salvelinus namaycush): Implications for fisheries management

USGS Publications Warehouse

Madenjian, C.P.; Chipman, B.D.; Marsden, J.E.

2008-01-01

Sea lamprey (Petromyzon marinus) control in North America costs millions of dollars each year, and control measures are guided by assessment of lamprey-induced damage to fisheries. The favored prey of sea lamprey in freshwater ecosystems has been lake trout (Salvelinus namaycush). A key parameter in assessing sea lamprey damage, as well as managing lake trout fisheries, is the probability of an adult lake trout surviving a lamprey attack. The conventional value for this parameter has been 0.55, based on laboratory experiments. In contrast, based on catch curve analysis, mark-recapture techniques, and observed wounding rates, we estimated that adult lake trout in Lake Champlain have a 0.74 probability of surviving a lamprey attack. Although sea lamprey growth in Lake Champlain was lower than that observed in Lake Huron, application of an individual-based model to both lakes indicated that the probability of surviving an attack in Lake Champlain was only 1.1 times higher than that in Lake Huron. Thus, we estimated that lake trout survive a lamprey attack in Lake Huron with a probability of 0.66. Therefore, our results suggested that lethality of a sea lamprey attack on lake trout has been overestimated in previous model applications used in fisheries management. ?? 2008 NRC.

Quantum-like dynamics of decision-making in prisoner's dilemma game

NASA Astrophysics Data System (ADS)

Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu

2012-03-01

In cognitive psychology, some experiments of games were reported [1, 2, 3, 4], and these demonstrated that real players did not use the "rational strategy" provided by classical game theory. To discuss probabilities of such "irrational choice", recently, we proposed a decision-making model which is based on the formalism of quantum mechanics [5, 6, 7, 8]. In this paper, we briefly explain the above model and calculate the probability of irrational choice in several prisoner's dilemma (PD) games.

A probabilistic approach to photovoltaic generator performance prediction

NASA Astrophysics Data System (ADS)

Khallat, M. A.; Rahman, S.

1986-09-01

A method for predicting the performance of a photovoltaic (PV) generator based on long term climatological data and expected cell performance is described. The equations for cell model formulation are provided. Use of the statistical model for characterizing the insolation level is discussed. The insolation data is fitted to appropriate probability distribution functions (Weibull, beta, normal). The probability distribution functions are utilized to evaluate the capacity factors of PV panels or arrays. An example is presented revealing the applicability of the procedure.

GIS-based probability assessment of natural hazards in forested landscapes of Central and South-Eastern Europe.

PubMed

Lorz, C; Fürst, C; Galic, Z; Matijasic, D; Podrazky, V; Potocic, N; Simoncic, P; Strauch, M; Vacik, H; Makeschin, F

2010-12-01

We assessed the probability of three major natural hazards--windthrow, drought, and forest fire--for Central and South-Eastern European forests which are major threats for the provision of forest goods and ecosystem services. In addition, we analyzed spatial distribution and implications for a future oriented management of forested landscapes. For estimating the probability of windthrow, we used rooting depth and average wind speed. Probabilities of drought and fire were calculated from climatic and total water balance during growing season. As an approximation to climate change scenarios, we used a simplified approach with a general increase of pET by 20%. Monitoring data from the pan-European forests crown condition program and observed burnt areas and hot spots from the European Forest Fire Information System were used to test the plausibility of probability maps. Regions with high probabilities of natural hazard are identified and management strategies to minimize probability of natural hazards are discussed. We suggest future research should focus on (i) estimating probabilities using process based models (including sensitivity analysis), (ii) defining probability in terms of economic loss, (iii) including biotic hazards, (iv) using more detailed data sets on natural hazards, forest inventories and climate change scenarios, and (v) developing a framework of adaptive risk management.

A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

NASA Astrophysics Data System (ADS)

Zhang, Shaojie; Zhao, Luqiang; Delgado-Tellez, Ricardo; Bao, Hongjun

2018-03-01

Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs < 1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

Cost-effectiveness of external cephalic version for term breech presentation.

PubMed

Tan, Jonathan M; Macario, Alex; Carvalho, Brendan; Druzin, Maurice L; El-Sayed, Yasser Y

2010-01-21

External cephalic version (ECV) is recommended by the American College of Obstetricians and Gynecologists to convert a breech fetus to vertex position and reduce the need for cesarean delivery. The goal of this study was to determine the incremental cost-effectiveness ratio, from society's perspective, of ECV compared to scheduled cesarean for term breech presentation. A computer-based decision model (TreeAge Pro 2008, Tree Age Software, Inc.) was developed for a hypothetical base case parturient presenting with a term singleton breech fetus with no contraindications for vaginal delivery. The model incorporated actual hospital costs (e.g., $8,023 for cesarean and $5,581 for vaginal delivery), utilities to quantify health-related quality of life, and probabilities based on analysis of published literature of successful ECV trial, spontaneous reversion, mode of delivery, and need for unanticipated emergency cesarean delivery. The primary endpoint was the incremental cost-effectiveness ratio in dollars per quality-adjusted year of life gained. A threshold of $50,000 per quality-adjusted life-years (QALY) was used to determine cost-effectiveness. The incremental cost-effectiveness of ECV, assuming a baseline 58% success rate, equaled $7,900/QALY. If the estimated probability of successful ECV is less than 32%, then ECV costs more to society and has poorer QALYs for the patient. However, as the probability of successful ECV was between 32% and 63%, ECV cost more than cesarean delivery but with greater associated QALY such that the cost-effectiveness ratio was less than $50,000/QALY. If the probability of successful ECV was greater than 63%, the computer modeling indicated that a trial of ECV is less costly and with better QALYs than a scheduled cesarean. The cost-effectiveness of a trial of ECV is most sensitive to its probability of success, and not to the probabilities of a cesarean after ECV, spontaneous reversion to breech, successful second ECV trial, or adverse outcome from emergency cesarean. From society's perspective, ECV trial is cost-effective when compared to a scheduled cesarean for breech presentation provided the probability of successful ECV is > 32%. Improved algorithms are needed to more precisely estimate the likelihood that a patient will have a successful ECV.

BEESCOUT: A model of bee scouting behaviour and a software tool for characterizing nectar/pollen landscapes for BEEHAVE.

PubMed

Becher, M A; Grimm, V; Knapp, J; Horn, J; Twiston-Davies, G; Osborne, J L

2016-11-24

Social bees are central place foragers collecting floral resources from the surrounding landscape, but little is known about the probability of a scouting bee finding a particular flower patch. We therefore developed a software tool, BEESCOUT, to theoretically examine how bees might explore a landscape and distribute their scouting activities over time and space. An image file can be imported, which is interpreted by the model as a "forage map" with certain colours representing certain crops or habitat types as specified by the user. BEESCOUT calculates the size and location of these potential food sources in that landscape relative to a bee colony. An individual-based model then determines the detection probabilities of the food patches by bees, based on parameter values gathered from the flight patterns of radar-tracked honeybees and bumblebees. Various "search modes" describe hypothetical search strategies for the long-range exploration of scouting bees. The resulting detection probabilities of forage patches can be used as input for the recently developed honeybee model BEEHAVE, to explore realistic scenarios of colony growth and death in response to different stressors. In example simulations, we find that detection probabilities for food sources close to the colony fit empirical data reasonably well. However, for food sources further away no empirical data are available to validate model output. The simulated detection probabilities depend largely on the bees' search mode, and whether they exchange information about food source locations. Nevertheless, we show that landscape structure and connectivity of food sources can have a strong impact on the results. We believe that BEESCOUT is a valuable tool to better understand how landscape configurations and searching behaviour of bees affect detection probabilities of food sources. It can also guide the collection of relevant data and the design of experiments to close knowledge gaps, and provides a useful extension to the BEEHAVE honeybee model, enabling future users to explore how landscape structure and food availability affect the foraging decisions and patch visitation rates of the bees and, in consequence, to predict colony development and survival.

Simple Physical Model for the Probability of a Subduction- Zone Earthquake Following Slow Slip Events and Earthquakes: Application to the Hikurangi Megathrust, New Zealand

NASA Astrophysics Data System (ADS)

Kaneko, Yoshihiro; Wallace, Laura M.; Hamling, Ian J.; Gerstenberger, Matthew C.

2018-05-01

Slow slip events (SSEs) have been documented in subduction zones worldwide, yet their implications for future earthquake occurrence are not well understood. Here we develop a relatively simple, simulation-based method for estimating the probability of megathrust earthquakes following tectonic events that induce any transient stress perturbations. This method has been applied to the locked Hikurangi megathrust (New Zealand) surrounded on all sides by the 2016 Kaikoura earthquake and SSEs. Our models indicate the annual probability of a M≥7.8 earthquake over 1 year after the Kaikoura earthquake increases by 1.3-18 times relative to the pre-Kaikoura probability, and the absolute probability is in the range of 0.6-7%. We find that probabilities of a large earthquake are mainly controlled by the ratio of the total stressing rate induced by all nearby tectonic sources to the mean stress drop of earthquakes. Our method can be applied to evaluate the potential for triggering a megathrust earthquake following SSEs in other subduction zones.

Assessing the chances of success: naïve statistics versus kind experience.

PubMed

Hogarth, Robin M; Mukherjee, Kanchan; Soyer, Emre

2013-01-01

Additive integration of information is ubiquitous in judgment and has been shown to be effective even when multiplicative rules of probability theory are prescribed. We explore the generality of these findings in the context of estimating probabilities of success in contests. We first define a normative model of these probabilities that takes account of relative skill levels in contests where only a limited number of entrants can win. We then report 4 experiments using a scenario about a competition. Experiments 1 and 2 both elicited judgments of probabilities, and, although participants' responses demonstrated considerable variability, their mean judgments provide a good fit to a simple linear model. Experiment 3 explored choices. Most participants entered most contests and showed little awareness of appropriate probabilities. Experiment 4 investigated effects of providing aids to calculate probabilities, specifically, access to expert advice and 2 simulation tools. With these aids, estimates were accurate and decisions varied appropriately with economic consequences. We discuss implications by considering when additive decision rules are dysfunctional, the interpretation of overconfidence based on contest-entry behavior, and the use of aids to help people make better decisions.

Statistic inversion of multi-zone transition probability models for aquifer characterization in alluvial fans

DOE PAGES

Zhu, Lin; Dai, Zhenxue; Gong, Huili; ...

2015-06-12

Understanding the heterogeneity arising from the complex architecture of sedimentary sequences in alluvial fans is challenging. This study develops a statistical inverse framework in a multi-zone transition probability approach for characterizing the heterogeneity in alluvial fans. An analytical solution of the transition probability matrix is used to define the statistical relationships among different hydrofacies and their mean lengths, integral scales, and volumetric proportions. A statistical inversion is conducted to identify the multi-zone transition probability models and estimate the optimal statistical parameters using the modified Gauss–Newton–Levenberg–Marquardt method. The Jacobian matrix is computed by the sensitivity equation method, which results in anmore » accurate inverse solution with quantification of parameter uncertainty. We use the Chaobai River alluvial fan in the Beijing Plain, China, as an example for elucidating the methodology of alluvial fan characterization. The alluvial fan is divided into three sediment zones. In each zone, the explicit mathematical formulations of the transition probability models are constructed with optimized different integral scales and volumetric proportions. The hydrofacies distributions in the three zones are simulated sequentially by the multi-zone transition probability-based indicator simulations. Finally, the result of this study provides the heterogeneous structure of the alluvial fan for further study of flow and transport simulations.« less

Estimating soil moisture exceedance probability from antecedent rainfall

NASA Astrophysics Data System (ADS)

Cronkite-Ratcliff, C.; Kalansky, J.; Stock, J. D.; Collins, B. D.

2016-12-01

The first storms of the rainy season in coastal California, USA, add moisture to soils but rarely trigger landslides. Previous workers proposed that antecedent rainfall, the cumulative seasonal rain from October 1 onwards, had to exceed specific amounts in order to trigger landsliding. Recent monitoring of soil moisture upslope of historic landslides in the San Francisco Bay Area shows that storms can cause positive pressure heads once soil moisture values exceed a threshold of volumetric water content (VWC). We propose that antecedent rainfall could be used to estimate the probability that VWC exceeds this threshold. A major challenge to estimating the probability of exceedance is that rain gauge records are frequently incomplete. We developed a stochastic model to impute (infill) missing hourly precipitation data. This model uses nearest neighbor-based conditional resampling of the gauge record using data from nearby rain gauges. Using co-located VWC measurements, imputed data can be used to estimate the probability that VWC exceeds a specific threshold for a given antecedent rainfall. The stochastic imputation model can also provide an estimate of uncertainty in the exceedance probability curve. Here we demonstrate the method using soil moisture and precipitation data from several sites located throughout Northern California. Results show a significant variability between sites in the sensitivity of VWC exceedance probability to antecedent rainfall.

Probabilistic Design Analysis (PDA) Approach to Determine the Probability of Cross-System Failures for a Space Launch Vehicle

NASA Technical Reports Server (NTRS)

Shih, Ann T.; Lo, Yunnhon; Ward, Natalie C.

2010-01-01

Quantifying the probability of significant launch vehicle failure scenarios for a given design, while still in the design process, is critical to mission success and to the safety of the astronauts. Probabilistic risk assessment (PRA) is chosen from many system safety and reliability tools to verify the loss of mission (LOM) and loss of crew (LOC) requirements set by the NASA Program Office. To support the integrated vehicle PRA, probabilistic design analysis (PDA) models are developed by using vehicle design and operation data to better quantify failure probabilities and to better understand the characteristics of a failure and its outcome. This PDA approach uses a physics-based model to describe the system behavior and response for a given failure scenario. Each driving parameter in the model is treated as a random variable with a distribution function. Monte Carlo simulation is used to perform probabilistic calculations to statistically obtain the failure probability. Sensitivity analyses are performed to show how input parameters affect the predicted failure probability, providing insight for potential design improvements to mitigate the risk. The paper discusses the application of the PDA approach in determining the probability of failure for two scenarios from the NASA Ares I project

ERMiT: Estimating Post-Fire Erosion in Probabilistic Terms

NASA Astrophysics Data System (ADS)

Pierson, F. B.; Robichaud, P. R.; Elliot, W. J.; Hall, D. E.; Moffet, C. A.

2006-12-01

Mitigating the impact of post-wildfire runoff and erosion on life, property, and natural resources have cost the United States government tens of millions of dollars over the past decade. The decision of where, when, and how to apply the most effective mitigation treatments requires land managers to assess the risk of damaging runoff and erosion events occurring after a fire. The Erosion Risk Management Tool (ERMiT) is a web-based application that estimates erosion in probabilistic terms on burned and recovering forest, range, and chaparral lands. Unlike most erosion prediction models, ERMiT does not provide `average annual erosion rates;' rather, it provides a distribution of erosion rates with the likelihood of their occurrence. ERMiT combines rain event variability with spatial and temporal variabilities of hillslope burn severity, soil properties, and ground cover to estimate Water Erosion Prediction Project (WEPP) model input parameter values. Based on 20 to 40 individual WEPP runs, ERMiT produces a distribution of rain event erosion rates with a probability of occurrence for each of five post-fire years. Over the 5 years of modeled recovery, the occurrence probability of the less erodible soil parameters is increased and the occurrence probability of the more erodible soil parameters is decreased. In addition, the occurrence probabilities and the four spatial arrangements of burn severity (arrangements of overland flow elements (OFE's)), are shifted toward lower burn severity with each year of recovery. These yearly adjustments are based on field measurements made through post-fire recovery periods. ERMiT also provides rain event erosion rate distributions for hillslopes that have been treated with seeding, straw mulch, straw wattles and contour-felled log erosion barriers. Such output can help managers make erosion mitigation treatment decisions based on the probability of high sediment yields occurring, the value of resources at risk for damage, cost, and other management considerations.

Probabilistic Evaluation of Competing Climate Models

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Chatterjee, S.; Heyman, M.; Cressie, N.

2017-12-01

A standard paradigm for assessing the quality of climate model simulations is to compare what these models produce for past and present time periods, to observations of the past and present. Many of these comparisons are based on simple summary statistics called metrics. Here, we propose an alternative: evaluation of competing climate models through probabilities derived from tests of the hypothesis that climate-model-simulated and observed time sequences share common climate-scale signals. The probabilities are based on the behavior of summary statistics of climate model output and observational data, over ensembles of pseudo-realizations. These are obtained by partitioning the original time sequences into signal and noise components, and using a parametric bootstrap to create pseudo-realizations of the noise sequences. The statistics we choose come from working in the space of decorrelated and dimension-reduced wavelet coefficients. We compare monthly sequences of CMIP5 model output of average global near-surface temperature anomalies to similar sequences obtained from the well-known HadCRUT4 data set, as an illustration.

A functional model of sensemaking in a neurocognitive architecture.

PubMed

Lebiere, Christian; Pirolli, Peter; Thomson, Robert; Paik, Jaehyon; Rutledge-Taylor, Matthew; Staszewski, James; Anderson, John R

2013-01-01

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesis-updating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment.

A Functional Model of Sensemaking in a Neurocognitive Architecture

PubMed Central

Lebiere, Christian; Paik, Jaehyon; Rutledge-Taylor, Matthew; Staszewski, James; Anderson, John R.

2013-01-01

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesis-updating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment. PMID:24302930

Statistical validation of normal tissue complication probability models.

PubMed

Xu, Cheng-Jian; van der Schaaf, Arjen; Van't Veld, Aart A; Langendijk, Johannes A; Schilstra, Cornelis

2012-09-01

To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use. Copyright © 2012 Elsevier Inc. All rights reserved.

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation

PubMed Central

Kong, Zehui; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control. PMID:28671967

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

PubMed

Kong, Zehui; Zou, Yuan; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao; Wang, Wensheng

2017-01-01

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ-connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm. PMID:28587084

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors.

PubMed

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao; Wang, Wensheng

2017-05-25

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ -connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm.

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing.

PubMed

Xu, Jason; Minin, Vladimir N

2015-07-01

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring integration over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multi-type branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for blood cell formation and evolution of self-replicating transposable elements in bacterial genomes.

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing

PubMed Central

Xu, Jason; Minin, Vladimir N.

2016-01-01

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring integration over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multi-type branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for blood cell formation and evolution of self-replicating transposable elements in bacterial genomes. PMID:26949377

Ethnic Group Bias in Intelligence Test Items.

ERIC Educational Resources Information Center

Scheuneman, Janice

In previous studies of ethnic group bias in intelligence test items, the question of bias has been confounded with ability differences between the ethnic group samples compared. The present study is based on a conditional probability model in which an unbiased item is defined as one where the probability of a correct response to an item is the…

Predicting traffic load impact of alternative recreation developments

Treesearch

Gary H. Elsner; Ronald A. Oliveira

1973-01-01

Traffic load changes as a result of expansion of recreation facilities may be predicted through computations based on estimates of (a) drawing power of the recreation attracttions, overnight accommodations, and in- or out-terminals; (b) probable types of travel; (c) probable routes of travel; and (d) total number of cars in the recreation system. Once the basic model...

Assessment of imperfect detection of blister rust in whitebark pine within the Greater Yellowstone Ecosystem

USGS Publications Warehouse

Wright, Wilson J.; Irvine, Kathryn M.

2017-01-01

We examined data on white pine blister rust (blister rust) collected during the monitoring of whitebark pine trees in the Greater Yellowstone Ecosystem (from 2004-2015). Summaries of repeat observations performed by multiple independent observers are reviewed and discussed. These summaries show variability among observers and the potential for errors being made in blister rust status. Based on this assessment, we utilized occupancy models to analyze blister rust prevalence while explicitly accounting for imperfect detection. Available covariates were used to model both the probability of a tree being infected with blister rust and the probability of an observer detecting the infection. The fitted model provided strong evidence that the probability of blister rust infection increases as tree diameter increases and decreases as site elevation increases. Most importantly, we found evidence of heterogeneity in detection probabilities related to tree size and average slope of a transect. These results suggested that detecting the presence of blister rust was more difficult in larger trees. Also, there was evidence that blister rust was easier to detect on transects located on steeper slopes. Our model accounted for potential impacts of observer experience on blister rust detection probabilities and also showed moderate variability among the different observers in their ability to detect blister rust. Based on these model results, we suggest that multiple observer sampling continue in future field seasons in order to allow blister rust prevalence estimates to be corrected for imperfect detection. We suggest that the multiple observer effort be spread out across many transects (instead of concentrated at a few each field season) while retaining the overall proportion of trees with multiple observers around 5-20%. Estimates of prevalence are confounded with detection unless it is explicitly accounted for in an analysis and we demonstrate how an occupancy model can be used to do account for this source of observation error.

The oilspill risk analysis model of the U. S. Geological Survey

USGS Publications Warehouse

Smith, R.A.; Slack, J.R.; Wyant, Timothy; Lanfear, K.J.

1982-01-01

The U.S. Geological Survey has developed an oilspill risk analysis model to aid in estimating the environmental hazards of developing oil resources in Outer Continental Shelf (OCS) lease areas. The large, computerized model analyzes the probability of spill occurrence, as well as the likely paths or trajectories of spills in relation to the locations of recreational and biological resources which may be vulnerable. The analytical methodology can easily incorporate estimates of weathering rates , slick dispersion, and possible mitigating effects of cleanup. The probability of spill occurrence is estimated from information on the anticipated level of oil production and method of route of transport. Spill movement is modeled in Monte Carlo fashion with a sample of 500 spills per season, each transported by monthly surface current vectors and wind velocities sampled from 3-hour wind transition matrices. Transition matrices are based on historic wind records grouped in 41 wind velocity classes, and are constructed seasonally for up to six wind stations. Locations and monthly vulnerabilities of up to 31 categories of environmental resources are digitized within an 800,000 square kilometer study area. Model output includes tables of conditional impact probabilities (that is, the probability of hitting a target, given that a spill has occured), as well as probability distributions for oilspills occurring and contacting environmental resources within preselected vulnerability time horizons. (USGS)

The oilspill risk analysis model of the U. S. Geological Survey

USGS Publications Warehouse

Smith, R.A.; Slack, J.R.; Wyant, T.; Lanfear, K.J.

1980-01-01

The U.S. Geological Survey has developed an oilspill risk analysis model to aid in estimating the environmental hazards of developing oil resources in Outer Continental Shelf (OCS) lease areas. The large, computerized model analyzes the probability of spill occurrence, as well as the likely paths or trajectories of spills in relation to the locations of recreational and biological resources which may be vulnerable. The analytical methodology can easily incorporate estimates of weathering rates , slick dispersion, and possible mitigating effects of cleanup. The probability of spill occurrence is estimated from information on the anticipated level of oil production and method and route of transport. Spill movement is modeled in Monte Carlo fashion with a sample of 500 spills per season, each transported by monthly surface current vectors and wind velocities sampled from 3-hour wind transition matrices. Transition matrices are based on historic wind records grouped in 41 wind velocity classes, and are constructed seasonally for up to six wind stations. Locations and monthly vulnerabilities of up to 31 categories of environmental resources are digitized within an 800,000 square kilometer study area. Model output includes tables of conditional impact probabilities (that is, the probability of hitting a target, given that a spill has occurred), as well as probability distributions for oilspills occurring and contacting environmental resources within preselected vulnerability time horizons. (USGS)

A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.

PubMed

Xu, Qingyang; Zhang, Chengjin; Zhang, Li

2014-01-01

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.

A Fast Elitism Gaussian Estimation of Distribution Algorithm and Application for PID Optimization

PubMed Central

Xu, Qingyang; Zhang, Chengjin; Zhang, Li

2014-01-01

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA. PMID:24892059

A simulation model for probabilistic analysis of Space Shuttle abort modes

NASA Technical Reports Server (NTRS)

Hage, R. T.

1993-01-01

A simulation model which was developed to provide a probabilistic analysis tool to study the various space transportation system abort mode situations is presented. The simulation model is based on Monte Carlo simulation of an event-tree diagram which accounts for events during the space transportation system's ascent and its abort modes. The simulation model considers just the propulsion elements of the shuttle system (i.e., external tank, main engines, and solid boosters). The model was developed to provide a better understanding of the probability of occurrence and successful completion of abort modes during the vehicle's ascent. The results of the simulation runs discussed are for demonstration purposes only, they are not official NASA probability estimates.

Computation of marginal distributions of peak-heights in electropherograms for analysing single source and mixture STR DNA samples.

PubMed

Cowell, Robert G

2018-05-04

Current models for single source and mixture samples, and probabilistic genotyping software based on them used for analysing STR electropherogram data, assume simple probability distributions, such as the gamma distribution, to model the allelic peak height variability given the initial amount of DNA prior to PCR amplification. Here we illustrate how amplicon number distributions, for a model of the process of sample DNA collection and PCR amplification, may be efficiently computed by evaluating probability generating functions using discrete Fourier transforms. Copyright © 2018 Elsevier B.V. All rights reserved.

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

Zhang, Jiangjiang; Li, Weixuan; Lin, Guang

In decision-making for groundwater management and contamination remediation, it is important to accurately evaluate the probability of the occurrence of a failure event. For small failure probability analysis, a large number of model evaluations are needed in the Monte Carlo (MC) simulation, which is impractical for CPU-demanding models. One approach to alleviate the computational cost caused by the model evaluations is to construct a computationally inexpensive surrogate model instead. However, using a surrogate approximation can cause an extra error in the failure probability analysis. Moreover, constructing accurate surrogates is challenging for high-dimensional models, i.e., models containing many uncertain input parameters.more » To address these issues, we propose an efficient two-stage MC approach for small failure probability analysis in high-dimensional groundwater contaminant transport modeling. In the first stage, a low-dimensional representation of the original high-dimensional model is sought with Karhunen–Loève expansion and sliced inverse regression jointly, which allows for the easy construction of a surrogate with polynomial chaos expansion. Then a surrogate-based MC simulation is implemented. In the second stage, the small number of samples that are close to the failure boundary are re-evaluated with the original model, which corrects the bias introduced by the surrogate approximation. The proposed approach is tested with a numerical case study and is shown to be 100 times faster than the traditional MC approach in achieving the same level of estimation accuracy.« less

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

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

Johnson, J. D.; Oberkampf, William Louis; Helton, Jon Craig

2006-10-01

Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a modelmore » is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.« less

A linear programming model for protein inference problem in shotgun proteomics.

PubMed

Huang, Ting; He, Zengyou

2012-11-15

Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is an important issue in shotgun proteomics. The objective of protein inference is to find a subset of proteins that are truly present in the sample. Although many methods have been proposed for protein inference, several issues such as peptide degeneracy still remain unsolved. In this article, we present a linear programming model for protein inference. In this model, we use a transformation of the joint probability that each peptide/protein pair is present in the sample as the variable. Then, both the peptide probability and protein probability can be expressed as a formula in terms of the linear combination of these variables. Based on this simple fact, the protein inference problem is formulated as an optimization problem: minimize the number of proteins with non-zero probabilities under the constraint that the difference between the calculated peptide probability and the peptide probability generated from peptide identification algorithms should be less than some threshold. This model addresses the peptide degeneracy issue by forcing some joint probability variables involving degenerate peptides to be zero in a rigorous manner. The corresponding inference algorithm is named as ProteinLP. We test the performance of ProteinLP on six datasets. Experimental results show that our method is competitive with the state-of-the-art protein inference algorithms. The source code of our algorithm is available at: https://sourceforge.net/projects/prolp/. zyhe@dlut.edu.cn. Supplementary data are available at Bioinformatics Online.

Improving inferences from fisheries capture-recapture studies through remote detection of PIT tags

USGS Publications Warehouse

Hewitt, David A.; Janney, Eric C.; Hayes, Brian S.; Shively, Rip S.

2010-01-01

Models for capture-recapture data are commonly used in analyses of the dynamics of fish and wildlife populations, especially for estimating vital parameters such as survival. Capture-recapture methods provide more reliable inferences than other methods commonly used in fisheries studies. However, for rare or elusive fish species, parameter estimation is often hampered by small probabilities of re-encountering tagged fish when encounters are obtained through traditional sampling methods. We present a case study that demonstrates how remote antennas for passive integrated transponder (PIT) tags can increase encounter probabilities and the precision of survival estimates from capture-recapture models. Between 1999 and 2007, trammel nets were used to capture and tag over 8,400 endangered adult Lost River suckers (Deltistes luxatus) during the spawning season in Upper Klamath Lake, Oregon. Despite intensive sampling at relatively discrete spawning areas, encounter probabilities from Cormack-Jolly-Seber models were consistently low (< 0.2) and the precision of apparent annual survival estimates was poor. Beginning in 2005, remote PIT tag antennas were deployed at known spawning locations to increase the probability of re-encountering tagged fish. We compare results based only on physical recaptures with results based on both physical recaptures and remote detections to demonstrate the substantial improvement in estimates of encounter probabilities (approaching 100%) and apparent annual survival provided by the remote detections. The richer encounter histories provided robust inferences about the dynamics of annual survival and have made it possible to explore more realistic models and hypotheses about factors affecting the conservation and recovery of this endangered species. Recent advances in technology related to PIT tags have paved the way for creative implementation of large-scale tagging studies in systems where they were previously considered impracticable.

Birth/birth-death processes and their computable transition probabilities with biological applications.

PubMed

Ho, Lam Si Tung; Xu, Jason; Crawford, Forrest W; Minin, Vladimir N; Suchard, Marc A

2018-03-01

Birth-death processes track the size of a univariate population, but many biological systems involve interaction between populations, necessitating models for two or more populations simultaneously. A lack of efficient methods for evaluating finite-time transition probabilities of bivariate processes, however, has restricted statistical inference in these models. Researchers rely on computationally expensive methods such as matrix exponentiation or Monte Carlo approximation, restricting likelihood-based inference to small systems, or indirect methods such as approximate Bayesian computation. In this paper, we introduce the birth/birth-death process, a tractable bivariate extension of the birth-death process, where rates are allowed to be nonlinear. We develop an efficient algorithm to calculate its transition probabilities using a continued fraction representation of their Laplace transforms. Next, we identify several exemplary models arising in molecular epidemiology, macro-parasite evolution, and infectious disease modeling that fall within this class, and demonstrate advantages of our proposed method over existing approaches to inference in these models. Notably, the ubiquitous stochastic susceptible-infectious-removed (SIR) model falls within this class, and we emphasize that computable transition probabilities newly enable direct inference of parameters in the SIR model. We also propose a very fast method for approximating the transition probabilities under the SIR model via a novel branching process simplification, and compare it to the continued fraction representation method with application to the 17th century plague in Eyam. Although the two methods produce similar maximum a posteriori estimates, the branching process approximation fails to capture the correlation structure in the joint posterior distribution.

Probabilistic risk model for staphylococcal intoxication from pork-based food dishes prepared in food service establishments in Korea.

PubMed

Kim, Hyun Jung; Griffiths, Mansel W; Fazil, Aamir M; Lammerding, Anna M

2009-09-01

Foodborne illness contracted at food service operations is an important public health issue in Korea. In this study, the probabilities for growth of, and enterotoxin production by, Staphylococcus aureus in pork meat-based foods prepared in food service operations were estimated by the Monte Carlo simulation. Data on the prevalence and concentration of S. aureus as well as compliance to guidelines for time and temperature controls during food service operations were collected. The growth of S. aureus was initially estimated by using the U.S. Department of Agriculture's Pathogen Modeling Program. A second model based on raw pork meat was derived to compare cell number predictions. The correlation between toxin level and cell number as well as minimum toxin dose obtained from published data was adopted to quantify the probability of staphylococcal intoxication. When data gaps were found, assumptions were made based on guidelines for food service practices. Baseline risk model and scenario analyses were performed to indicate possible outcomes of staphylococcal intoxication under the scenarios generated based on these data gaps. Staphylococcal growth was predicted during holding before and after cooking, and the highest estimated concentration (4.59 log CFU/g for the 99.9th percentile value) of S. aureus was observed in raw pork initially contaminated with S. aureus and held before cooking. The estimated probability for staphylococcal intoxication was very low, using currently available data. However, scenario analyses revealed an increased possibility of staphylococcal intoxication when increased levels of initial contamination in the raw meat, andlonger holding time both before and after cooking the meat occurred.

Derivation of low flow frequency distributions under human activities and its implications

NASA Astrophysics Data System (ADS)

Gao, Shida; Liu, Pan; Pan, Zhengke; Ming, Bo; Guo, Shenglian; Xiong, Lihua

2017-06-01

Low flow, refers to a minimum streamflow in dry seasons, is crucial to water supply, agricultural irrigation and navigation. Human activities, such as groundwater pumping, influence low flow severely. In order to derive the low flow frequency distribution functions under human activities, this study incorporates groundwater pumping and return flow as variables in the recession process. Steps are as follows: (1) the original low flow without human activities is assumed to follow a Pearson type three distribution, (2) the probability distribution of climatic dry spell periods is derived based on a base flow recession model, (3) the base flow recession model is updated under human activities, and (4) the low flow distribution under human activities is obtained based on the derived probability distribution of dry spell periods and the updated base flow recession model. Linear and nonlinear reservoir models are used to describe the base flow recession, respectively. The Wudinghe basin is chosen for the case study, with daily streamflow observations during 1958-2000. Results show that human activities change the location parameter of the low flow frequency curve for the linear reservoir model, while alter the frequency distribution function for the nonlinear one. It is indicated that alter the parameters of the low flow frequency distribution is not always feasible to tackle the changing environment.

Survival Predictions of Ceramic Crowns Using Statistical Fracture Mechanics

PubMed Central

Nasrin, S.; Katsube, N.; Seghi, R.R.; Rokhlin, S.I.

2017-01-01

This work establishes a survival probability methodology for interface-initiated fatigue failures of monolithic ceramic crowns under simulated masticatory loading. A complete 3-dimensional (3D) finite element analysis model of a minimally reduced molar crown was developed using commercially available hardware and software. Estimates of material surface flaw distributions and fatigue parameters for 3 reinforced glass-ceramics (fluormica [FM], leucite [LR], and lithium disilicate [LD]) and a dense sintered yttrium-stabilized zirconia (YZ) were obtained from the literature and incorporated into the model. Utilizing the proposed fracture mechanics–based model, crown survival probability as a function of loading cycles was obtained from simulations performed on the 4 ceramic materials utilizing identical crown geometries and loading conditions. The weaker ceramic materials (FM and LR) resulted in lower survival rates than the more recently developed higher-strength ceramic materials (LD and YZ). The simulated 10-y survival rate of crowns fabricated from YZ was only slightly better than those fabricated from LD. In addition, 2 of the model crown systems (FM and LD) were expanded to determine regional-dependent failure probabilities. This analysis predicted that the LD-based crowns were more likely to fail from fractures initiating from margin areas, whereas the FM-based crowns showed a slightly higher probability of failure from fractures initiating from the occlusal table below the contact areas. These 2 predicted fracture initiation locations have some agreement with reported fractographic analyses of failed crowns. In this model, we considered the maximum tensile stress tangential to the interfacial surface, as opposed to the more universally reported maximum principal stress, because it more directly impacts crack propagation. While the accuracy of these predictions needs to be experimentally verified, the model can provide a fundamental understanding of the importance that pre-existing flaws at the intaglio surface have on fatigue failures. PMID:28107637

Landslide Hazard from Coupled Inherent and Dynamic Probabilities

NASA Astrophysics Data System (ADS)

Strauch, R. L.; Istanbulluoglu, E.; Nudurupati, S. S.

2015-12-01

Landslide hazard research has typically been conducted independently from hydroclimate research. We sought to unify these two lines of research to provide regional scale landslide hazard information for risk assessments and resource management decision-making. Our approach couples an empirical inherent landslide probability, based on a frequency ratio analysis, with a numerical dynamic probability, generated by combining subsurface water recharge and surface runoff from the Variable Infiltration Capacity (VIC) macro-scale land surface hydrologic model with a finer resolution probabilistic slope stability model. Landslide hazard mapping is advanced by combining static and dynamic models of stability into a probabilistic measure of geohazard prediction in both space and time. This work will aid resource management decision-making in current and future landscape and climatic conditions. The approach is applied as a case study in North Cascade National Park Complex in northern Washington State.

Supervised variational model with statistical inference and its application in medical image segmentation.

PubMed

Li, Changyang; Wang, Xiuying; Eberl, Stefan; Fulham, Michael; Yin, Yong; Dagan Feng, David

2015-01-01

Automated and general medical image segmentation can be challenging because the foreground and the background may have complicated and overlapping density distributions in medical imaging. Conventional region-based level set algorithms often assume piecewise constant or piecewise smooth for segments, which are implausible for general medical image segmentation. Furthermore, low contrast and noise make identification of the boundaries between foreground and background difficult for edge-based level set algorithms. Thus, to address these problems, we suggest a supervised variational level set segmentation model to harness the statistical region energy functional with a weighted probability approximation. Our approach models the region density distributions by using the mixture-of-mixtures Gaussian model to better approximate real intensity distributions and distinguish statistical intensity differences between foreground and background. The region-based statistical model in our algorithm can intuitively provide better performance on noisy images. We constructed a weighted probability map on graphs to incorporate spatial indications from user input with a contextual constraint based on the minimization of contextual graphs energy functional. We measured the performance of our approach on ten noisy synthetic images and 58 medical datasets with heterogeneous intensities and ill-defined boundaries and compared our technique to the Chan-Vese region-based level set model, the geodesic active contour model with distance regularization, and the random walker model. Our method consistently achieved the highest Dice similarity coefficient when compared to the other methods.

A predictive model to estimate the pretest probability of metastasis in patients with osteosarcoma.

PubMed

Wang, Sisheng; Zheng, Shaoluan; Hu, Kongzu; Sun, Heyan; Zhang, Jinling; Rong, Genxiang; Gao, Jie; Ding, Nan; Gui, Binjie

2017-01-01

Osteosarcomas (OSs) represent a huge challenge to improve the overall survival, especially in metastatic patients. Increasing evidence indicates that both tumor-associated elements but also on host-associated elements are under a remarkable effect on the prognosis of cancer patients, especially systemic inflammatory response. By analyzing a series prognosis of factors, including age, gender, primary tumor size, tumor location, tumor grade, and histological classification, monocyte ratio, and NLR ratio, a clinical predictive model was established by using stepwise logistic regression involved circulating leukocyte to compute the estimated probabilities of metastases for OS patients. The clinical predictive model was described by the following equations: probability of developing metastases = ex/(1 + ex), x = -2.150 +  (1.680 × monocyte ratio) + (1.533 × NLR ratio), where is the base of the natural logarithm, the assignment to each of the 2 variables is 1 if the ratio >1 (otherwise 0). The calculated AUC of the receiver-operating characteristic curve as 0.793 revealed well accuracy of this model (95% CI, 0.740-0.845). The predicted probabilities that we generated with the cross-validation procedure had a similar AUC (0.743; 95% CI, 0.684-0.803). The present model could be used to improve the outcomes of the metastases by developing a predictive model considering circulating leukocyte influence to estimate the pretest probability of developing metastases in patients with OS.

The estimated lifetime probability of acquiring human papillomavirus in the United States.

PubMed

Chesson, Harrell W; Dunne, Eileen F; Hariri, Susan; Markowitz, Lauri E

2014-11-01

Estimates of the lifetime probability of acquiring human papillomavirus (HPV) can help to quantify HPV incidence, illustrate how common HPV infection is, and highlight the importance of HPV vaccination. We developed a simple model, based primarily on the distribution of lifetime numbers of sex partners across the population and the per-partnership probability of acquiring HPV, to estimate the lifetime probability of acquiring HPV in the United States in the time frame before HPV vaccine availability. We estimated the average lifetime probability of acquiring HPV among those with at least 1 opposite sex partner to be 84.6% (range, 53.6%-95.0%) for women and 91.3% (range, 69.5%-97.7%) for men. Under base case assumptions, more than 80% of women and men acquire HPV by age 45 years. Our results are consistent with estimates in the existing literature suggesting a high lifetime probability of HPV acquisition and are supported by cohort studies showing high cumulative HPV incidence over a relatively short period, such as 3 to 5 years.

Capture-recapture studies for multiple strata including non-markovian transitions

USGS Publications Warehouse

Brownie, C.; Hines, J.E.; Nichols, J.D.; Pollock, K.H.; Hestbeck, J.B.

1993-01-01

We consider capture-recapture studies where release and recapture data are available from each of a number of strata on every capture occasion. Strata may, for example, be geographic locations or physiological states. Movement of animals among strata occurs with unknown probabilities, and estimation of these unknown transition probabilities is the objective. We describe a computer routine for carrying out the analysis under a model that assumes Markovian transitions and under reduced parameter versions of this model. We also introduce models that relax the Markovian assumption and allow 'memory' to operate (i.e., allow dependence of the transition probabilities on the previous state). For these models, we sugg st an analysis based on a conditional likelihood approach. Methods are illustrated with data from a large study on Canada geese (Branta canadensis) banded in three geographic regions. The assumption of Markovian transitions is rejected convincingly for these data, emphasizing the importance of the more general models that allow memory.

Understanding interaction effects of climate change and fire management on bird distributions through combined process and habitat models

USGS Publications Warehouse

White, Joseph D.; Gutzwiller, Kevin J.; Barrow, Wylie C.; Johnson-Randall, Lori; Zygo, Lisa; Swint, Pamela

2011-01-01

Avian conservation efforts must account for changes in vegetation composition and structure associated with climate change. We modeled vegetation change and the probability of occurrence of birds to project changes in winter bird distributions associated with climate change and fire management in the northern Chihuahuan Desert (southwestern U.S.A.). We simulated vegetation change in a process-based model (Landscape and Fire Simulator) in which anticipated climate change was associated with doubling of current atmospheric carbon dioxide over the next 50 years. We estimated the relative probability of bird occurrence on the basis of statistical models derived from field observations of birds and data on vegetation type, topography, and roads. We selected 3 focal species, Scaled Quail (Callipepla squamata), Loggerhead Shrike (Lanius ludovicianus), and Rock Wren (Salpinctes obsoletus), that had a range of probabilities of occurrence for our study area. Our simulations projected increases in relative probability of bird occurrence in shrubland and decreases in grassland and Yucca spp. and ocotillo (Fouquieria splendens) vegetation. Generally, the relative probability of occurrence of all 3 species was highest in shrubland because leaf-area index values were lower in shrubland. This high probability of occurrence likely is related to the species' use of open vegetation for foraging. Fire suppression had little effect on projected vegetation composition because as climate changed there was less fuel and burned area. Our results show that if future water limits on plant type are considered, models that incorporate spatial data may suggest how and where different species of birds may respond to vegetation changes.

Understanding interaction effects of climate change and fire management on bird distributions through combined process and habitat models

USGS Publications Warehouse

White, Joseph D.; Gutzwiller, Kevin J.; Barrow, Wylie C.; Johnson-Randall, Lori; Zygo, Lisa; Swint, Pamela

2011-01-01

Avian conservation efforts must account for changes in vegetation composition and structure associated with climate change. We modeled vegetation change and the probability of occurrence of birds to project changes in winter bird distributions associated with climate change and fire management in the northern Chihuahuan Desert (southwestern U.S.A.). We simulated vegetation change in a process-based model (Landscape and Fire Simulator) in which anticipated climate change was associated with doubling of current atmospheric carbon dioxide over the next 50 years. We estimated the relative probability of bird occurrence on the basis of statistical models derived from field observations of birds and data on vegetation type, topography, and roads. We selected 3 focal species, Scaled Quail (Callipepla squamata), Loggerhead Shrike (Lanius ludovicianus), and Rock Wren (Salpinctes obsoletus), that had a range of probabilities of occurrence for our study area. Our simulations projected increases in relative probability of bird occurrence in shrubland and decreases in grassland and Yucca spp. and ocotillo (Fouquieria splendens) vegetation. Generally, the relative probability of occurrence of all 3 species was highest in shrubland because leaf-area index values were lower in shrubland. This high probability of occurrence likely is related to the species' use of open vegetation for foraging. Fire suppression had little effect on projected vegetation composition because as climate changed there was less fuel and burned area. Our results show that if future water limits on plant type are considered, models that incorporate spatial data may suggest how and where different species of birds may respond to vegetation changes. ??2011 Society for Conservation Biology.

Stimulated luminescence emission from localized recombination in randomly distributed defects.

PubMed

Jain, Mayank; Guralnik, Benny; Andersen, Martin Thalbitzer

2012-09-26

We present a new kinetic model describing localized electronic recombination through the excited state of the donor (d) to an acceptor (a) centre in luminescent materials. In contrast to the existing models based on the localized transition model (LTM) of Halperin and Braner (1960 Phys. Rev. 117 408-15) which assumes a fixed d → a tunnelling probability for the entire crystal, our model is based on nearest-neighbour recombination within randomly distributed centres. Such a random distribution can occur through the entire volume or within the defect complexes of the dosimeter, and implies that the tunnelling probability varies with the donor-acceptor (d-a) separation distance. We first develop an 'exact kinetic model' that incorporates this variation in tunnelling probabilities, and evolves both in spatial as well as temporal domains. We then develop a simplified one-dimensional, semi-analytical model that evolves only in the temporal domain. An excellent agreement is observed between thermally and optically stimulated luminescence (TL and OSL) results produced from the two models. In comparison to the first-order kinetic behaviour of the LTM of Halperin and Braner (1960 Phys. Rev. 117 408-15), our model results in a highly asymmetric TL peak; this peak can be understood to derive from a continuum of several first-order TL peaks. Our model also shows an extended power law behaviour for OSL (or prompt luminescence), which is expected from localized recombination mechanisms in materials with random distribution of centres.

Melanoma-specific mortality and competing mortality in patients with non-metastatic malignant melanoma: a population-based analysis.

PubMed

Shen, Weidong; Sakamoto, Naoko; Yang, Limin

2016-07-07

The objectives of this study were to evaluate and model the probability of melanoma-specific death and competing causes of death for patients with melanoma by competing risk analysis, and to build competing risk nomograms to provide individualized and accurate predictive tools. Melanoma data were obtained from the Surveillance Epidemiology and End Results program. All patients diagnosed with primary non-metastatic melanoma during the years 2004-2007 were potentially eligible for inclusion. The cumulative incidence function (CIF) was used to describe the probability of melanoma mortality and competing risk mortality. We used Gray's test to compare differences in CIF between groups. The proportional subdistribution hazard approach by Fine and Gray was used to model CIF. We built competing risk nomograms based on the models that we developed. The 5-year cumulative incidence of melanoma death was 7.1 %, and the cumulative incidence of other causes of death was 7.4 %. We identified that variables associated with an elevated probability of melanoma-specific mortality included older age, male sex, thick melanoma, ulcerated cancer, and positive lymph nodes. The nomograms were well calibrated. C-indexes were 0.85 and 0.83 for nomograms predicting the probability of melanoma mortality and competing risk mortality, which suggests good discriminative ability. This large study cohort enabled us to build a reliable competing risk model and nomogram for predicting melanoma prognosis. Model performance proved to be good. This individualized predictive tool can be used in clinical practice to help treatment-related decision making.

Probability of detection of clinical seizures using heart rate changes.

PubMed

Osorio, Ivan; Manly, B F J

2015-08-01

Heart rate-based seizure detection is a viable complement or alternative to ECoG/EEG. This study investigates the role of various biological factors on the probability of clinical seizure detection using heart rate. Regression models were applied to 266 clinical seizures recorded from 72 subjects to investigate if factors such as age, gender, years with epilepsy, etiology, seizure site origin, seizure class, and data collection centers, among others, shape the probability of EKG-based seizure detection. Clinical seizure detection probability based on heart rate changes, is significantly (p<0.001) shaped by patients' age and gender, seizure class, and years with epilepsy. The probability of detecting clinical seizures (>0.8 in the majority of subjects) using heart rate is highest for complex partial seizures, increases with a patient's years with epilepsy, is lower for females than for males and is unrelated to the side of hemisphere origin. Clinical seizure detection probability using heart rate is multi-factorially dependent and sufficiently high (>0.8) in most cases to be clinically useful. Knowledge of the role that these factors play in shaping said probability will enhance its applicability and usefulness. Heart rate is a reliable and practical signal for extra-cerebral detection of clinical seizures originating from or spreading to central autonomic network structures. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Improving deep convolutional neural networks with mixed maxout units.

PubMed

Zhao, Hui-Zhen; Liu, Fu-Xian; Li, Long-Yue

2017-01-01

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.

A goodness-of-fit test for capture-recapture model M(t) under closure

USGS Publications Warehouse

Stanley, T.R.; Burnham, K.P.

1999-01-01

A new, fully efficient goodness-of-fit test for the time-specific closed-population capture-recapture model M(t) is presented. This test is based on the residual distribution of the capture history data given the maximum likelihood parameter estimates under model M(t), is partitioned into informative components, and is based on chi-square statistics. Comparison of this test with Leslie's test (Leslie, 1958, Journal of Animal Ecology 27, 84- 86) for model M(t), using Monte Carlo simulations, shows the new test generally outperforms Leslie's test. The new test is frequently computable when Leslie's test is not, has Type I error rates that are closer to nominal error rates than Leslie's test, and is sensitive to behavioral variation and heterogeneity in capture probabilities. Leslie's test is not sensitive to behavioral variation in capture probabilities but, when computable, has greater power to detect heterogeneity than the new test.

Probability theory versus simulation of petroleum potential in play analysis

USGS Publications Warehouse

Crovelli, R.A.

1987-01-01

An analytic probabilistic methodology for resource appraisal of undiscovered oil and gas resources in play analysis is presented. This play-analysis methodology is a geostochastic system for petroleum resource appraisal in explored as well as frontier areas. An objective was to replace an existing Monte Carlo simulation method in order to increase the efficiency of the appraisal process. Underlying the two methods is a single geologic model which considers both the uncertainty of the presence of the assessed hydrocarbon and its amount if present. The results of the model are resource estimates of crude oil, nonassociated gas, dissolved gas, and gas for a geologic play in terms of probability distributions. The analytic method is based upon conditional probability theory and a closed form solution of all means and standard deviations, along with the probabilities of occurrence. ?? 1987 J.C. Baltzer A.G., Scientific Publishing Company.

Spatial organization of foreshocks as a tool to forecast large earthquakes.

PubMed

Lippiello, E; Marzocchi, W; de Arcangelis, L; Godano, C

2012-01-01

An increase in the number of smaller magnitude events, retrospectively named foreshocks, is often observed before large earthquakes. We show that the linear density probability of earthquakes occurring before and after small or intermediate mainshocks displays a symmetrical behavior, indicating that the size of the area fractured during the mainshock is encoded in the foreshock spatial organization. This observation can be used to discriminate spatial clustering due to foreshocks from the one induced by aftershocks and is implemented in an alarm-based model to forecast m > 6 earthquakes. A retrospective study of the last 19 years Southern California catalog shows that the daily occurrence probability presents isolated peaks closely located in time and space to the epicenters of five of the six m > 6 earthquakes. We find daily probabilities as high as 25% (in cells of size 0.04 × 0.04deg(2)), with significant probability gains with respect to standard models.

Spatial organization of foreshocks as a tool to forecast large earthquakes

PubMed Central

Lippiello, E.; Marzocchi, W.; de Arcangelis, L.; Godano, C.

2012-01-01

An increase in the number of smaller magnitude events, retrospectively named foreshocks, is often observed before large earthquakes. We show that the linear density probability of earthquakes occurring before and after small or intermediate mainshocks displays a symmetrical behavior, indicating that the size of the area fractured during the mainshock is encoded in the foreshock spatial organization. This observation can be used to discriminate spatial clustering due to foreshocks from the one induced by aftershocks and is implemented in an alarm-based model to forecast m > 6 earthquakes. A retrospective study of the last 19 years Southern California catalog shows that the daily occurrence probability presents isolated peaks closely located in time and space to the epicenters of five of the six m > 6 earthquakes. We find daily probabilities as high as 25% (in cells of size 0.04 × 0.04deg2), with significant probability gains with respect to standard models. PMID:23152938

Probabilistic Nowcasting of Low-Visibility Procedure States at Vienna International Airport During Cold Season

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian J.; Mayr, Georg J.; Zeileis, Achim

2018-04-01

Airport operations are sensitive to visibility conditions. Low-visibility events may lead to capacity reduction, delays and economic losses. Different levels of low-visibility procedures (lvp) are enacted to ensure aviation safety. A nowcast of the probabilities for each of the lvp categories helps decision makers to optimally schedule their operations. An ordered logistic regression (OLR) model is used to forecast these probabilities directly. It is applied to cold season forecasts at Vienna International Airport for lead times of 30-min out to 2 h. Model inputs are standard meteorological measurements. The skill of the forecasts is accessed by the ranked probability score. OLR outperforms persistence, which is a strong contender at the shortest lead times. The ranked probability score of the OLR is even better than the one of nowcasts from human forecasters. The OLR-based nowcasting system is computationally fast and can be updated instantaneously when new data become available.

Probabilistic seismic hazard in the San Francisco Bay area based on a simplified viscoelastic cycle model of fault interactions

USGS Publications Warehouse

Pollitz, F.F.; Schwartz, D.P.

2008-01-01

We construct a viscoelastic cycle model of plate boundary deformation that includes the effect of time-dependent interseismic strain accumulation, coseismic strain release, and viscoelastic relaxation of the substrate beneath the seismogenic crust. For a given fault system, time-averaged stress changes at any point (not on a fault) are constrained to zero; that is, kinematic consistency is enforced for the fault system. The dates of last rupture, mean recurrence times, and the slip distributions of the (assumed) repeating ruptures are key inputs into the viscoelastic cycle model. This simple formulation allows construction of stress evolution at all points in the plate boundary zone for purposes of probabilistic seismic hazard analysis (PSHA). Stress evolution is combined with a Coulomb failure stress threshold at representative points on the fault segments to estimate the times of their respective future ruptures. In our PSHA we consider uncertainties in a four-dimensional parameter space: the rupture peridocities, slip distributions, time of last earthquake (for prehistoric ruptures) and Coulomb failure stress thresholds. We apply this methodology to the San Francisco Bay region using a recently determined fault chronology of area faults. Assuming single-segment rupture scenarios, we find that fature rupture probabilities of area faults in the coming decades are the highest for the southern Hayward, Rodgers Creek, and northern Calaveras faults. This conclusion is qualitatively similar to that of Working Group on California Earthquake Probabilities, but the probabilities derived here are significantly higher. Given that fault rupture probabilities are highly model-dependent, no single model should be used to assess to time-dependent rupture probabilities. We suggest that several models, including the present one, be used in a comprehensive PSHA methodology, as was done by Working Group on California Earthquake Probabilities.

Dynamic prediction of patient outcomes during ongoing cardiopulmonary resuscitation.

PubMed

Kim, Joonghee; Kim, Kyuseok; Callaway, Clifton W; Doh, Kibbeum; Choi, Jungho; Park, Jongdae; Jo, You Hwan; Lee, Jae Hyuk

2017-02-01

The probability of the return of spontaneous circulation (ROSC) and subsequent favourable outcomes changes dynamically during advanced cardiac life support (ACLS). We sought to model these changes using time-to-event analysis in out-of-hospital cardiac arrest (OHCA) patients. Adult (≥18 years old), non-traumatic OHCA patients without prehospital ROSC were included. Utstein variables and initial arterial blood gas measurements were used as predictors. The incidence rate of ROSC during the first 30min of ACLS in the emergency department (ED) was modelled using spline-based parametric survival analysis. Conditional probabilities of subsequent outcomes after ROSC (1-week and 1-month survival and 6-month neurologic recovery) were modelled using multivariable logistic regression. The ROSC and conditional probability models were then combined to estimate the likelihood of achieving ROSC and subsequent outcomes by providing k additional minutes of effort. A total of 727 patients were analyzed. The incidence rate of ROSC increased rapidly until the 10th minute of ED ACLS, and it subsequently decreased. The conditional probabilities of subsequent outcomes after ROSC were also dependent on the duration of resuscitation with odds ratios for 1-week and 1-month survival and neurologic recovery of 0.93 (95% CI: 0.90-0.96, p<0.001), 0.93 (0.88-0.97, p=0.001) and 0.93 (0.87-0.99, p=0.031) per 1-min increase, respectively. Calibration testing of the combined models showed good correlation between mean predicted probability and actual prevalence. The probability of ROSC and favourable subsequent outcomes changed according to a multiphasic pattern over the first 30min of ACLS, and modelling of the dynamic changes was feasible. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Modeling the distribution of colonial species to improve estimation of plankton concentration in ballast water

NASA Astrophysics Data System (ADS)

Rajakaruna, Harshana; VandenByllaardt, Julie; Kydd, Jocelyn; Bailey, Sarah

2018-03-01

The International Maritime Organization (IMO) has set limits on allowable plankton concentrations in ballast water discharge to minimize aquatic invasions globally. Previous guidance on ballast water sampling and compliance decision thresholds was based on the assumption that probability distributions of plankton are Poisson when spatially homogenous, or negative binomial when heterogeneous. We propose a hierarchical probability model, which incorporates distributions at the level of particles (i.e., discrete individuals plus colonies per unit volume) and also within particles (i.e., individuals per particle) to estimate the average plankton concentration in ballast water. We examined the performance of the models using data for plankton in the size class ≥ 10 μm and < 50 μm, collected from five different depths of a ballast tank of a commercial ship in three independent surveys. We show that the data fit to the negative binomial and the hierarchical probability models equally well, with both models performing better than the Poisson model at the scale of our sampling. The hierarchical probability model, which accounts for both the individuals and the colonies in a sample, reduces the uncertainty associated with the concentration estimation, and improves the power of rejecting the decision on ship's compliance when a ship does not truly comply with the standard. We show examples of how to test ballast water compliance using the above models.

Choice-Based Segmentation as an Enrollment Management Tool

ERIC Educational Resources Information Center

Young, Mark R.

2002-01-01

This article presents an approach to enrollment management based on target marketing strategies developed from a choice-based segmentation methodology. Students are classified into "switchable" or "non-switchable" segments based on their probability of selecting specific majors. A modified multinomial logit choice model is used to identify…

A utility-based design for randomized comparative trials with ordinal outcomes and prognostic subgroups.

PubMed

Murray, Thomas A; Yuan, Ying; Thall, Peter F; Elizondo, Joan H; Hofstetter, Wayne L

2018-01-22

A design is proposed for randomized comparative trials with ordinal outcomes and prognostic subgroups. The design accounts for patient heterogeneity by allowing possibly different comparative conclusions within subgroups. The comparative testing criterion is based on utilities for the levels of the ordinal outcome and a Bayesian probability model. Designs based on two alternative models that include treatment-subgroup interactions are considered, the proportional odds model and a non-proportional odds model with a hierarchical prior that shrinks toward the proportional odds model. A third design that assumes homogeneity and ignores possible treatment-subgroup interactions also is considered. The three approaches are applied to construct group sequential designs for a trial of nutritional prehabilitation versus standard of care for esophageal cancer patients undergoing chemoradiation and surgery, including both untreated patients and salvage patients whose disease has recurred following previous therapy. A simulation study is presented that compares the three designs, including evaluation of within-subgroup type I and II error probabilities under a variety of scenarios including different combinations of treatment-subgroup interactions. © 2018, The International Biometric Society.

Q-Learning-Based Adjustable Fixed-Phase Quantum Grover Search Algorithm

NASA Astrophysics Data System (ADS)

Guo, Ying; Shi, Wensha; Wang, Yijun; Hu, Jiankun

2017-02-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one.

Application of a multistate model to estimate culvert effects on movement of small fishes

USGS Publications Warehouse

Norman, J.R.; Hagler, M.M.; Freeman, Mary C.; Freeman, B.J.

2009-01-01

While it is widely acknowledged that culverted road-stream crossings may impede fish passage, effects of culverts on movement of nongame and small-bodied fishes have not been extensively studied and studies generally have not accounted for spatial variation in capture probabilities. We estimated probabilities for upstream and downstream movement of small (30-120 mm standard length) benthic and water column fishes across stream reaches with and without culverts at four road-stream crossings over a 4-6-week period. Movement and reach-specific capture probabilities were estimated using multistate capture-recapture models. Although none of the culverts were complete barriers to passage, only a bottomless-box culvert appeared to permit unrestricted upstream and downstream movements by benthic fishes based on model estimates of movement probabilities. At two box culverts that were perched above the water surface at base flow, observed movements were limited to water column fishes and to intervals when runoff from storm events raised water levels above the perched level. Only a single fish was observed to move through a partially embedded pipe culvert. Estimates for probabilities of movement over distances equal to at least the length of one culvert were low (e.g., generally ???0.03, estimated for 1-2-week intervals) and had wide 95% confidence intervals as a consequence of few observed movements to nonadjacent reaches. Estimates of capture probabilities varied among reaches by a factor of 2 to over 10, illustrating the importance of accounting for spatially variable capture rates when estimating movement probabilities with capture-recapture data. Longer-term studies are needed to evaluate temporal variability in stream fish passage at culverts (e.g., in relation to streamflow variability) and to thereby better quantify the degree of population fragmentation caused by road-stream crossings with culverts. ?? American Fisheries Society 2009.

Monte Carlo based protocol for cell survival and tumour control probability in BNCT.

PubMed

Ye, S J

1999-02-01

A mathematical model to calculate the theoretical cell survival probability (nominally, the cell survival fraction) is developed to evaluate preclinical treatment conditions for boron neutron capture therapy (BNCT). A treatment condition is characterized by the neutron beam spectra, single or bilateral exposure, and the choice of boron carrier drug (boronophenylalanine (BPA) or boron sulfhydryl hydride (BSH)). The cell survival probability defined from Poisson statistics is expressed with the cell-killing yield, the 10B(n,alpha)7Li reaction density, and the tolerable neutron fluence. The radiation transport calculation from the neutron source to tumours is carried out using Monte Carlo methods: (i) reactor-based BNCT facility modelling to yield the neutron beam library at an irradiation port; (ii) dosimetry to limit the neutron fluence below a tolerance dose (10.5 Gy-Eq); (iii) calculation of the 10B(n,alpha)7Li reaction density in tumours. A shallow surface tumour could be effectively treated by single exposure producing an average cell survival probability of 10(-3)-10(-5) for probable ranges of the cell-killing yield for the two drugs, while a deep tumour will require bilateral exposure to achieve comparable cell kills at depth. With very pure epithermal beams eliminating thermal, low epithermal and fast neutrons, the cell survival can be decreased by factors of 2-10 compared with the unmodified neutron spectrum. A dominant effect of cell-killing yield on tumour cell survival demonstrates the importance of choice of boron carrier drug. However, these calculations do not indicate an unambiguous preference for one drug, due to the large overlap of tumour cell survival in the probable ranges of the cell-killing yield for the two drugs. The cell survival value averaged over a bulky tumour volume is used to predict the overall BNCT therapeutic efficacy, using a simple model of tumour control probability (TCP).

Decision making generalized by a cumulative probability weighting function

NASA Astrophysics Data System (ADS)

dos Santos, Lindomar Soares; Destefano, Natália; Martinez, Alexandre Souto

2018-01-01

Typical examples of intertemporal decision making involve situations in which individuals must choose between a smaller reward, but more immediate, and a larger one, delivered later. Analogously, probabilistic decision making involves choices between options whose consequences differ in relation to their probability of receiving. In Economics, the expected utility theory (EUT) and the discounted utility theory (DUT) are traditionally accepted normative models for describing, respectively, probabilistic and intertemporal decision making. A large number of experiments confirmed that the linearity assumed by the EUT does not explain some observed behaviors, as nonlinear preference, risk-seeking and loss aversion. That observation led to the development of new theoretical models, called non-expected utility theories (NEUT), which include a nonlinear transformation of the probability scale. An essential feature of the so-called preference function of these theories is that the probabilities are transformed by decision weights by means of a (cumulative) probability weighting function, w(p) . We obtain in this article a generalized function for the probabilistic discount process. This function has as particular cases mathematical forms already consecrated in the literature, including discount models that consider effects of psychophysical perception. We also propose a new generalized function for the functional form of w. The limiting cases of this function encompass some parametric forms already proposed in the literature. Far beyond a mere generalization, our function allows the interpretation of probabilistic decision making theories based on the assumption that individuals behave similarly in the face of probabilities and delays and is supported by phenomenological models.

A model of hygiene practices and consumption patterns in the consumer phase.

PubMed

Christensen, Bjarke B; Rosenquist, Hanne; Sommer, Helle M; Nielsen, Niels L; Fagt, Sisse; Andersen, Niels L; Nørrung, Birgit

2005-02-01

A mathematical model is presented, which addresses individual hygiene practices during food preparation and consumption patterns in private homes. Further, the model links food preparers and consumers based on their relationship to household types. For different age and gender groups, the model estimates (i) the probability of ingesting a meal where precautions have not been taken to avoid the transfer of microorganisms from raw food to final meal (a risk meal), exemplified by the event that the cutting board was not washed during food preparation, and (ii) the probability of ingesting a risk meal in a private home, where chicken was the prepared food item (a chicken risk meal). Chicken was included in the model, as chickens are believed to be the major source of human exposure to the foodborne pathogen Campylobacter. Monte Carlo simulations showed that the probability of ingesting a risk meal was highest for young males (aged 18-29 years) and lowest for the elderly above 60 years of age. Children aged 0-4 years had a higher probability of ingesting a risk meal than children aged 5-17 years. This difference between age and gender groups was ascribed to the variations in the hygiene levels of food preparers. By including the probability of ingesting a chicken meal at home, simulations revealed that all age groups, except the group above 60 years of age, had approximately the same probability of ingesting a chicken risk meal, the probability of females being slightly higher than that of males. The simulated results show that the probability of ingesting a chicken risk meal at home does not only depend on the hygiene practices of the persons preparing the food, but also on the consumption patterns of consumers, and the relationship between people preparing and ingesting food. This finding supports the need of including information on consumer behavior and preparation hygiene in the consumer phase of exposure assessments.

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

Wang, Hesheng, E-mail: hesheng@umich.edu; Feng, Mary; Jackson, Andrew

Purpose: To develop a local and global function model in the liver based on regional and organ function measurements to support individualized adaptive radiation therapy (RT). Methods and Materials: A local and global model for liver function was developed to include both functional volume and the effect of functional variation of subunits. Adopting the assumption of parallel architecture in the liver, the global function was composed of a sum of local function probabilities of subunits, varying between 0 and 1. The model was fit to 59 datasets of liver regional and organ function measures from 23 patients obtained before, during, andmore » 1 month after RT. The local function probabilities of subunits were modeled by a sigmoid function in relating to MRI-derived portal venous perfusion values. The global function was fitted to a logarithm of an indocyanine green retention rate at 15 minutes (an overall liver function measure). Cross-validation was performed by leave-m-out tests. The model was further evaluated by fitting to the data divided according to whether the patients had hepatocellular carcinoma (HCC) or not. Results: The liver function model showed that (1) a perfusion value of 68.6 mL/(100 g · min) yielded a local function probability of 0.5; (2) the probability reached 0.9 at a perfusion value of 98 mL/(100 g · min); and (3) at a probability of 0.03 [corresponding perfusion of 38 mL/(100 g · min)] or lower, the contribution to global function was lost. Cross-validations showed that the model parameters were stable. The model fitted to the data from the patients with HCC indicated that the same amount of portal venous perfusion was translated into less local function probability than in the patients with non-HCC tumors. Conclusions: The developed liver function model could provide a means to better assess individual and regional dose-responses of hepatic functions, and provide guidance for individualized treatment planning of RT.« less

Using a remote sensing/GIS model to predict southwestern Willow Flycatcher breeding habitat along the Rio Grande, New Mexico

USGS Publications Warehouse

Hatten, James R.; Sogge, Mark K.

2007-01-01

Introduction The Southwestern Willow Flycatcher (Empidonax traillii extimus; hereafter SWFL) is a federally endangered bird (USFWS 1995) that breeds in riparian areas in portions of New Mexico, Arizona, southwestern Colorado, extreme southern Utah and Nevada, and southern California (USFWS 2002). Across this range, it uses a variety of plant species as nesting/breeding habitat, but in all cases prefers sites with dense vegetation, high canopy, and proximity to surface water or saturated soils (Sogge and Marshall 2000). As of 2005, the known rangewide breeding population of SWFLs was roughly 1,214 territories, with approximately 393 territories distributed among 36 sites in New Mexico (Durst et al. 2006), primarily along the Rio Grande. One of the key challenges facing the management and conservation of the Southwestern Willow Flycatcher is that riparian areas are dynamic, with individual habitat patches subject to cycles of creation, growth, and loss due to drought, flooding, fire, and other disturbances. Former breeding patches can lose suitability, and new habitat can develop within a matter of only a few years, especially in reservoir drawdown zones. Therefore, measuring and predicting flycatcher habitat - either to discover areas that might support SWFLs, or to identify areas that may develop into appropriate habitat - requires knowledge of recent/current habitat conditions and an understanding of the factors that determine flycatcher use of riparian breeding sites. In the past, much of the determination of whether a riparian site is likely to support breeding flycatchers has been based on qualitative criteria (for example, 'dense vegetation' or 'large patches'). These determinations often require on-the-ground field evaluations by local or regional SWFL experts. While this has proven valuable in locating many of the currently known breeding sites, it is difficult or impossible to apply this approach effectively over large geographic areas (for example, the middle Rio Grande). The SWFL Recovery Plan (USFWS 2002) recognizes the importance of developing new approaches to habitat identification, and recommends the development of drainage-scale, quantitative habitat models. In particular, the plan suggests using models based on remote sensing and Geographic Information System (GIS) technology that can capture the relatively dynamic habitat changes that occur in southwestern riparian systems. In 1999, Arizona Game and Fish Department (AGFD) developed a GIS-based model (Hatten and Paradzick 2003) to identify SWFL breeding habitat from Landsat Thematic Mapper imagery and 30-m resolution digital elevation models (DEMs). The model was developed with presence/absence survey data acquired along the San Pedro and Gila rivers, and from the Salt River and Tonto Creek inlets to Roosevelt Lake in southern Arizona (collectively called the project area). The GIS-based model used a logistic regression equation to divide riparian vegetation into 5 probability classes based upon characteristics of riparian vegetation and floodplain size. This model was tested by predicting SWFL breeding habitat at Alamo Lake, Arizona, located 200 km from the project area (Hatten and Paradzick 2003). The GIS-based model performed as expected by identifying riparian areas with the highest SWFL nest densities, located in the higher probability classes. In 2002, AGFD applied the GIS-based model throughout Arizona, for riparian areas below 1,524 m (5,000 ft) elevation and within 1.6 km of perennial or intermittent waters (Dockens et al. 2004). Overall model accuracy (using probability classes 1-5, with class 5 having the greatest probability of nesting activity) for predicting the location of 2001 nest sites was 96.5 percent; accuracy decreased when fewer probability classes were defined as suitable. Map accuracy, determined from errors of commission, increased in higher probability classes in a fashion similar to errors of omission. Map accuracy, li

Quantum probabilistic logic programming

NASA Astrophysics Data System (ADS)

Balu, Radhakrishnan

2015-05-01

We describe a quantum mechanics based logic programming language that supports Horn clauses, random variables, and covariance matrices to express and solve problems in probabilistic logic. The Horn clauses of the language wrap random variables, including infinite valued, to express probability distributions and statistical correlations, a powerful feature to capture relationship between distributions that are not independent. The expressive power of the language is based on a mechanism to implement statistical ensembles and to solve the underlying SAT instances using quantum mechanical machinery. We exploit the fact that classical random variables have quantum decompositions to build the Horn clauses. We establish the semantics of the language in a rigorous fashion by considering an existing probabilistic logic language called PRISM with classical probability measures defined on the Herbrand base and extending it to the quantum context. In the classical case H-interpretations form the sample space and probability measures defined on them lead to consistent definition of probabilities for well formed formulae. In the quantum counterpart, we define probability amplitudes on Hinterpretations facilitating the model generations and verifications via quantum mechanical superpositions and entanglements. We cast the well formed formulae of the language as quantum mechanical observables thus providing an elegant interpretation for their probabilities. We discuss several examples to combine statistical ensembles and predicates of first order logic to reason with situations involving uncertainty.

Dynamic analysis of pedestrian crossing behaviors on traffic flow at unsignalized mid-block crosswalks

NASA Astrophysics Data System (ADS)

Liu, Gang; He, Jing; Luo, Zhiyong; Yang, Wunian; Zhang, Xiping

2015-05-01

It is important to study the effects of pedestrian crossing behaviors on traffic flow for solving the urban traffic jam problem. Based on the Nagel-Schreckenberg (NaSch) traffic cellular automata (TCA) model, a new one-dimensional TCA model is proposed considering the uncertainty conflict behaviors between pedestrians and vehicles at unsignalized mid-block crosswalks and defining the parallel updating rules of motion states of pedestrians and vehicles. The traffic flow is simulated for different vehicle densities and behavior trigger probabilities. The fundamental diagrams show that no matter what the values of vehicle braking probability, pedestrian acceleration crossing probability, pedestrian backing probability and pedestrian generation probability, the system flow shows the "increasing-saturating-decreasing" trend with the increase of vehicle density; when the vehicle braking probability is lower, it is easy to cause an emergency brake of vehicle and result in great fluctuation of saturated flow; the saturated flow decreases slightly with the increase of the pedestrian acceleration crossing probability; when the pedestrian backing probability lies between 0.4 and 0.6, the saturated flow is unstable, which shows the hesitant behavior of pedestrians when making the decision of backing; the maximum flow is sensitive to the pedestrian generation probability and rapidly decreases with increasing the pedestrian generation probability, the maximum flow is approximately equal to zero when the probability is more than 0.5. The simulations prove that the influence of frequent crossing behavior upon vehicle flow is immense; the vehicle flow decreases and gets into serious congestion state rapidly with the increase of the pedestrian generation probability.

Convex reformulation of biologically-based multi-criteria intensity-modulated radiation therapy optimization including fractionation effects

NASA Astrophysics Data System (ADS)

Hoffmann, Aswin L.; den Hertog, Dick; Siem, Alex Y. D.; Kaanders, Johannes H. A. M.; Huizenga, Henk

2008-11-01

Finding fluence maps for intensity-modulated radiation therapy (IMRT) can be formulated as a multi-criteria optimization problem for which Pareto optimal treatment plans exist. To account for the dose-per-fraction effect of fractionated IMRT, it is desirable to exploit radiobiological treatment plan evaluation criteria based on the linear-quadratic (LQ) cell survival model as a means to balance the radiation benefits and risks in terms of biologic response. Unfortunately, the LQ-model-based radiobiological criteria are nonconvex functions, which make the optimization problem hard to solve. We apply the framework proposed by Romeijn et al (2004 Phys. Med. Biol. 49 1991-2013) to find transformations of LQ-model-based radiobiological functions and establish conditions under which transformed functions result in equivalent convex criteria that do not change the set of Pareto optimal treatment plans. The functions analysed are: the LQ-Poisson-based model for tumour control probability (TCP) with and without inter-patient heterogeneity in radiation sensitivity, the LQ-Poisson-based relative seriality s-model for normal tissue complication probability (NTCP), the equivalent uniform dose (EUD) under the LQ-Poisson model and the fractionation-corrected Probit-based model for NTCP according to Lyman, Kutcher and Burman. These functions differ from those analysed before in that they cannot be decomposed into elementary EUD or generalized-EUD functions. In addition, we show that applying increasing and concave transformations to the convexified functions is beneficial for the piecewise approximation of the Pareto efficient frontier.

Definition and solution of a stochastic inverse problem for the Manning’s n parameter field in hydrodynamic models

DOE PAGES

Butler, Troy; Graham, L.; Estep, D.; ...

2015-02-03

The uncertainty in spatially heterogeneous Manning’s n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented in this paper. Technical details that arise in practice by applying the framework to determine the Manning’s n parameter field in amore » shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of “condition” for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. Finally, this notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning’s n parameter and the effect on model predictions is analyzed.« less

Computing elastic‐rebound‐motivated rarthquake probabilities in unsegmented fault models: a new methodology supported by physics‐based simulators

USGS Publications Warehouse

Field, Edward H.

2015-01-01

A methodology is presented for computing elastic‐rebound‐based probabilities in an unsegmented fault or fault system, which involves computing along‐fault averages of renewal‐model parameters. The approach is less biased and more self‐consistent than a logical extension of that applied most recently for multisegment ruptures in California. It also enables the application of magnitude‐dependent aperiodicity values, which the previous approach does not. Monte Carlo simulations are used to analyze long‐term system behavior, which is generally found to be consistent with that of physics‐based earthquake simulators. Results cast doubt that recurrence‐interval distributions at points on faults look anything like traditionally applied renewal models, a fact that should be considered when interpreting paleoseismic data. We avoid such assumptions by changing the "probability of what" question (from offset at a point to the occurrence of a rupture, assuming it is the next event to occur). The new methodology is simple, although not perfect in terms of recovering long‐term rates in Monte Carlo simulations. It represents a reasonable, improved way to represent first‐order elastic‐rebound predictability, assuming it is there in the first place, and for a system that clearly exhibits other unmodeled complexities, such as aftershock triggering.

Let your fingers do the walking: A simple spectral signature model for "remote" fossil prospecting.

PubMed

Conroy, Glenn C; Emerson, Charles W; Anemone, Robert L; Townsend, K E Beth

2012-07-01

Even with the most meticulous planning, and utilizing the most experienced fossil-hunters, fossil prospecting in remote and/or extensive areas can be time-consuming, expensive, logistically challenging, and often hit or miss. While nothing can predict or guarantee with 100% assurance that fossils will be found in any particular location, any procedures or techniques that might increase the odds of success would be a major benefit to the field. Here we describe, and test, one such technique that we feel has great potential for increasing the probability of finding fossiliferous sediments - a relatively simple spectral signature model using the spatial analysis and image classification functions of ArcGIS(®)10 that creates interactive thematic land cover maps that can be used for "remote" fossil prospecting. Our test case is the extensive Eocene sediments of the Uinta Basin, Utah - a fossil prospecting area encompassing ∼1200 square kilometers. Using Landsat 7 ETM+ satellite imagery, we "trained" the spatial analysis and image classification algorithms using the spectral signatures of known fossil localities discovered in the Uinta Basin prior to 2005 and then created interactive probability models highlighting other regions in the Basin having a high probability of containing fossiliferous sediments based on their spectral signatures. A fortuitous "post-hoc" validation of our model presented itself. Our model identified several paleontological "hotspots", regions that, while not producing any fossil localities prior to 2005, had high probabilities of being fossiliferous based on the similarities of their spectral signatures to those of previously known fossil localities. Subsequent fieldwork found fossils in all the regions predicted by the model. Copyright © 2012 Elsevier Ltd. All rights reserved.

Estimating rates of local extinction and colonization in colonial species and an extension to the metapopulation and community levels

USGS Publications Warehouse

Barbraud, C.; Nichols, J.D.; Hines, J.E.; Hafner, H.

2003-01-01

Coloniality has mainly been studied from an evolutionary perspective, but relatively few studies have developed methods for modelling colony dynamics. Changes in number of colonies over time provide a useful tool for predicting and evaluating the responses of colonial species to management and to environmental disturbance. Probabilistic Markov process models have been recently used to estimate colony site dynamics using presence-absence data when all colonies are detected in sampling efforts. Here, we define and develop two general approaches for the modelling and analysis of colony dynamics for sampling situations in which all colonies are, and are not, detected. For both approaches, we develop a general probabilistic model for the data and then constrain model parameters based on various hypotheses about colony dynamics. We use Akaike's Information Criterion (AIC) to assess the adequacy of the constrained models. The models are parameterised with conditional probabilities of local colony site extinction and colonization. Presence-absence data arising from Pollock's robust capture-recapture design provide the basis for obtaining unbiased estimates of extinction, colonization, and detection probabilities when not all colonies are detected. This second approach should be particularly useful in situations where detection probabilities are heterogeneous among colony sites. The general methodology is illustrated using presence-absence data on two species of herons (Purple Heron, Ardea purpurea and Grey Heron, Ardea cinerea). Estimates of the extinction and colonization rates showed interspecific differences and strong temporal and spatial variations. We were also able to test specific predictions about colony dynamics based on ideas about habitat change and metapopulation dynamics. We recommend estimators based on probabilistic modelling for future work on colony dynamics. We also believe that this methodological framework has wide application to problems in animal ecology concerning metapopulation and community dynamics.

Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance, porosity and lithofacies

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

Passos de Figueiredo, Leandro, E-mail: leandrop.fgr@gmail.com; Grana, Dario; Santos, Marcio

We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and lithofacies within the reservoir conditioned to post-stack seismic and well data. The link between elastic and petrophysical properties is given by a joint prior distribution for the logarithm of impedance and porosity, based on a rock-physics model. The well conditioning is performed through a background model obtained by well log interpolation. Two different approaches are presented: in the first approach, the prior is defined by a single Gaussian distribution, whereas in the second approach it is defined by a Gaussian mixture to represent the well datamore » multimodal distribution and link the Gaussian components to different geological lithofacies. The forward model is based on a linearized convolutional model. For the single Gaussian case, we obtain an analytical expression for the posterior distribution, resulting in a fast algorithm to compute the solution of the inverse problem, i.e. the posterior distribution of acoustic impedance and porosity as well as the facies probability given the observed data. For the Gaussian mixture prior, it is not possible to obtain the distributions analytically, hence we propose a Gibbs algorithm to perform the posterior sampling and obtain several reservoir model realizations, allowing an uncertainty analysis of the estimated properties and lithofacies. Both methodologies are applied to a real seismic dataset with three wells to obtain 3D models of acoustic impedance, porosity and lithofacies. The methodologies are validated through a blind well test and compared to a standard Bayesian inversion approach. Using the probability of the reservoir lithofacies, we also compute a 3D isosurface probability model of the main oil reservoir in the studied field.« less

Fire and Heat Spreading Model Based on Cellular Automata Theory

NASA Astrophysics Data System (ADS)

Samartsev, A. A.; Rezchikov, A. F.; Kushnikov, V. A.; Ivashchenko, V. A.; Bogomolov, A. S.; Filimonyuk, L. Yu; Dolinina, O. N.; Kushnikov, O. V.; Shulga, T. E.; Tverdokhlebov, V. A.; Fominykh, D. S.

2018-05-01

The distinctive feature of the proposed fire and heat spreading model in premises is the reduction of the computational complexity due to the use of the theory of cellular automata with probability rules of behavior. The possibilities and prospects of using this model in practice are noted. The proposed model has a simple mechanism of integration with agent-based evacuation models. The joint use of these models could improve floor plans and reduce the time of evacuation from premises during fires.

Study of a Terrain-Based Motion Estimation Model to Predict the Position of a Moving Target to Enhance Weapon Probability of Kill

DTIC Science & Technology

2017-09-01

target is modeled based on the kinematic constraints for the type of vehicle and the type of path on which it is traveling . The discrete- time position...is modeled based on the kinematic constraints for the type of vehicle and the type of path on which it is traveling . The discrete- time position...49 A. TRAVELING TIME COMPUTATION ............................................. 49 B. CONVERSION TO

A new probability distribution model of turbulent irradiance based on Born perturbation theory

NASA Astrophysics Data System (ADS)

Wang, Hongxing; Liu, Min; Hu, Hao; Wang, Qian; Liu, Xiguo

2010-10-01

The subject of the PDF (Probability Density Function) of the irradiance fluctuations in a turbulent atmosphere is still unsettled. Theory reliably describes the behavior in the weak turbulence regime, but theoretical description in the strong and whole turbulence regimes are still controversial. Based on Born perturbation theory, the physical manifestations and correlations of three typical PDF models (Rice-Nakagami, exponential-Bessel and negative-exponential distribution) were theoretically analyzed. It is shown that these models can be derived by separately making circular-Gaussian, strong-turbulence and strong-turbulence-circular-Gaussian approximations in Born perturbation theory, which denies the viewpoint that the Rice-Nakagami model is only applicable in the extremely weak turbulence regime and provides theoretical arguments for choosing rational models in practical applications. In addition, a common shortcoming of the three models is that they are all approximations. A new model, called the Maclaurin-spread distribution, is proposed without any approximation except for assuming the correlation coefficient to be zero. So, it is considered that the new model can exactly reflect the Born perturbation theory. Simulated results prove the accuracy of this new model.

Modeling the frequency-dependent detective quantum efficiency of photon-counting x-ray detectors.

PubMed

Stierstorfer, Karl

2018-01-01

To find a simple model for the frequency-dependent detective quantum efficiency (DQE) of photon-counting detectors in the low flux limit. Formula for the spatial cross-talk, the noise power spectrum and the DQE of a photon-counting detector working at a given threshold are derived. Parameters are probabilities for types of events like single counts in the central pixel, double counts in the central pixel and a neighboring pixel or single count in a neighboring pixel only. These probabilities can be derived in a simple model by extensive use of Monte Carlo techniques: The Monte Carlo x-ray propagation program MOCASSIM is used to simulate the energy deposition from the x-rays in the detector material. A simple charge cloud model using Gaussian clouds of fixed width is used for the propagation of the electric charge generated by the primary interactions. Both stages are combined in a Monte Carlo simulation randomizing the location of impact which finally produces the required probabilities. The parameters of the charge cloud model are fitted to the spectral response to a polychromatic spectrum measured with our prototype detector. Based on the Monte Carlo model, the DQE of photon-counting detectors as a function of spatial frequency is calculated for various pixel sizes, photon energies, and thresholds. The frequency-dependent DQE of a photon-counting detector in the low flux limit can be described with an equation containing only a small set of probabilities as input. Estimates for the probabilities can be derived from a simple model of the detector physics. © 2017 American Association of Physicists in Medicine.

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach.

PubMed

Chen, Yong; Hong, Chuan; Ning, Yang; Su, Xiao

2016-01-15

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach and has several attractive features compared with the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration. Copyright © 2015 John Wiley & Sons, Ltd.

Interpreting null results from measurements with uncertain correlations: an info-gap approach.

PubMed

Ben-Haim, Yakov

2011-01-01

Null events—not detecting a pernicious agent—are the basis for declaring the agent is absent. Repeated nulls strengthen confidence in the declaration. However, correlations between observations are difficult to assess in many situations and introduce uncertainty in interpreting repeated nulls. We quantify uncertain correlations using an info-gap model, which is an unbounded family of nested sets of possible probabilities. An info-gap model is nonprobabilistic and entails no assumption about a worst case. We then evaluate the robustness, to uncertain correlations, of estimates of the probability of a null event. This is then the basis for evaluating a nonprobabilistic robustness-based confidence interval for the probability of a null. © 2010 Society for Risk Analysis.

Maximum entropy principal for transportation

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

Bilich, F.; Da Silva, R.

In this work we deal with modeling of the transportation phenomenon for use in the transportation planning process and policy-impact studies. The model developed is based on the dependence concept, i.e., the notion that the probability of a trip starting at origin i is dependent on the probability of a trip ending at destination j given that the factors (such as travel time, cost, etc.) which affect travel between origin i and destination j assume some specific values. The derivation of the solution of the model employs the maximum entropy principle combining a priori multinomial distribution with a trip utilitymore » concept. This model is utilized to forecast trip distributions under a variety of policy changes and scenarios. The dependence coefficients are obtained from a regression equation where the functional form is derived based on conditional probability and perception of factors from experimental psychology. The dependence coefficients encode all the information that was previously encoded in the form of constraints. In addition, the dependence coefficients encode information that cannot be expressed in the form of constraints for practical reasons, namely, computational tractability. The equivalence between the standard formulation (i.e., objective function with constraints) and the dependence formulation (i.e., without constraints) is demonstrated. The parameters of the dependence-based trip-distribution model are estimated, and the model is also validated using commercial air travel data in the U.S. In addition, policy impact analyses (such as allowance of supersonic flights inside the U.S. and user surcharge at noise-impacted airports) on air travel are performed.« less

Disentangling sampling and ecological explanations underlying species-area relationships

USGS Publications Warehouse

Cam, E.; Nichols, J.D.; Hines, J.E.; Sauer, J.R.; Alpizar-Jara, R.; Flather, C.H.

2002-01-01

We used a probabilistic approach to address the influence of sampling artifacts on the form of species-area relationships (SARs). We developed a model in which the increase in observed species richness is a function of sampling effort exclusively. We assumed that effort depends on area sampled, and we generated species-area curves under that model. These curves can be realistic looking. We then generated SARs from avian data, comparing SARs based on counts with those based on richness estimates. We used an approach to estimation of species richness that accounts for species detection probability and, hence, for variation in sampling effort. The slopes of SARs based on counts are steeper than those of curves based on estimates of richness, indicating that the former partly reflect failure to account for species detection probability. SARs based on estimates reflect ecological processes exclusively, not sampling processes. This approach permits investigation of ecologically relevant hypotheses. The slope of SARs is not influenced by the slope of the relationship between habitat diversity and area. In situations in which not all of the species are detected during sampling sessions, approaches to estimation of species richness integrating species detection probability should be used to investigate the rate of increase in species richness with area.

Bivariate Rainfall and Runoff Analysis Using Shannon Entropy Theory

NASA Astrophysics Data System (ADS)

Rahimi, A.; Zhang, L.

2012-12-01

Rainfall-Runoff analysis is the key component for many hydrological and hydraulic designs in which the dependence of rainfall and runoff needs to be studied. It is known that the convenient bivariate distribution are often unable to model the rainfall-runoff variables due to that they either have constraints on the range of the dependence or fixed form for the marginal distributions. Thus, this paper presents an approach to derive the entropy-based joint rainfall-runoff distribution using Shannon entropy theory. The distribution derived can model the full range of dependence and allow different specified marginals. The modeling and estimation can be proceeded as: (i) univariate analysis of marginal distributions which includes two steps, (a) using the nonparametric statistics approach to detect modes and underlying probability density, and (b) fitting the appropriate parametric probability density functions; (ii) define the constraints based on the univariate analysis and the dependence structure; (iii) derive and validate the entropy-based joint distribution. As to validate the method, the rainfall-runoff data are collected from the small agricultural experimental watersheds located in semi-arid region near Riesel (Waco), Texas, maintained by the USDA. The results of unviariate analysis show that the rainfall variables follow the gamma distribution, whereas the runoff variables have mixed structure and follow the mixed-gamma distribution. With this information, the entropy-based joint distribution is derived using the first moments, the first moments of logarithm transformed rainfall and runoff, and the covariance between rainfall and runoff. The results of entropy-based joint distribution indicate: (1) the joint distribution derived successfully preserves the dependence between rainfall and runoff, and (2) the K-S goodness of fit statistical tests confirm the marginal distributions re-derived reveal the underlying univariate probability densities which further assure that the entropy-based joint rainfall-runoff distribution are satisfactorily derived. Overall, the study shows the Shannon entropy theory can be satisfactorily applied to model the dependence between rainfall and runoff. The study also shows that the entropy-based joint distribution is an appropriate approach to capture the dependence structure that cannot be captured by the convenient bivariate joint distributions. Joint Rainfall-Runoff Entropy Based PDF, and Corresponding Marginal PDF and Histogram for W12 Watershed The K-S Test Result and RMSE on Univariate Distributions Derived from the Maximum Entropy Based Joint Probability Distribution;

Effects of sampling conditions on DNA-based estimates of American black bear abundance

USGS Publications Warehouse

Laufenberg, Jared S.; Van Manen, Frank T.; Clark, Joseph D.

2013-01-01

DNA-based capture-mark-recapture techniques are commonly used to estimate American black bear (Ursus americanus) population abundance (N). Although the technique is well established, many questions remain regarding study design. In particular, relationships among N, capture probability of heterogeneity mixtures A and B (pA and pB, respectively, or p, collectively), the proportion of each mixture (π), number of capture occasions (k), and probability of obtaining reliable estimates of N are not fully understood. We investigated these relationships using 1) an empirical dataset of DNA samples for which true N was unknown and 2) simulated datasets with known properties that represented a broader array of sampling conditions. For the empirical data analysis, we used the full closed population with heterogeneity data type in Program MARK to estimate N for a black bear population in Great Smoky Mountains National Park, Tennessee. We systematically reduced the number of those samples used in the analysis to evaluate the effect that changes in capture probabilities may have on parameter estimates. Model-averaged N for females and males were 161 (95% CI = 114–272) and 100 (95% CI = 74–167), respectively (pooled N = 261, 95% CI = 192–419), and the average weekly p was 0.09 for females and 0.12 for males. When we reduced the number of samples of the empirical data, support for heterogeneity models decreased. For the simulation analysis, we generated capture data with individual heterogeneity covering a range of sampling conditions commonly encountered in DNA-based capture-mark-recapture studies and examined the relationships between those conditions and accuracy (i.e., probability of obtaining an estimated N that is within 20% of true N), coverage (i.e., probability that 95% confidence interval includes true N), and precision (i.e., probability of obtaining a coefficient of variation ≤20%) of estimates using logistic regression. The capture probability for the larger of 2 mixture proportions of the population (i.e., pA or pB, depending on the value of π) was most important for predicting accuracy and precision, whereas capture probabilities of both mixture proportions (pA and pB) were important to explain variation in coverage. Based on sampling conditions similar to parameter estimates from the empirical dataset (pA = 0.30, pB = 0.05, N = 250, π = 0.15, and k = 10), predicted accuracy and precision were low (60% and 53%, respectively), whereas coverage was high (94%). Increasing pB, the capture probability for the predominate but most difficult to capture proportion of the population, was most effective to improve accuracy under those conditions. However, manipulation of other parameters may be more effective under different conditions. In general, the probabilities of obtaining accurate and precise estimates were best when p≥ 0.2. Our regression models can be used by managers to evaluate specific sampling scenarios and guide development of sampling frameworks or to assess reliability of DNA-based capture-mark-recapture studies.

Naive Probability: Model-Based Estimates of Unique Events.

PubMed

Khemlani, Sangeet S; Lotstein, Max; Johnson-Laird, Philip N

2015-08-01

We describe a dual-process theory of how individuals estimate the probabilities of unique events, such as Hillary Clinton becoming U.S. President. It postulates that uncertainty is a guide to improbability. In its computer implementation, an intuitive system 1 simulates evidence in mental models and forms analog non-numerical representations of the magnitude of degrees of belief. This system has minimal computational power and combines evidence using a small repertoire of primitive operations. It resolves the uncertainty of divergent evidence for single events, for conjunctions of events, and for inclusive disjunctions of events, by taking a primitive average of non-numerical probabilities. It computes conditional probabilities in a tractable way, treating the given event as evidence that may be relevant to the probability of the dependent event. A deliberative system 2 maps the resulting representations into numerical probabilities. With access to working memory, it carries out arithmetical operations in combining numerical estimates. Experiments corroborated the theory's predictions. Participants concurred in estimates of real possibilities. They violated the complete joint probability distribution in the predicted ways, when they made estimates about conjunctions: P(A), P(B), P(A and B), disjunctions: P(A), P(B), P(A or B or both), and conditional probabilities P(A), P(B), P(B|A). They were faster to estimate the probabilities of compound propositions when they had already estimated the probabilities of each of their components. We discuss the implications of these results for theories of probabilistic reasoning. © 2014 Cognitive Science Society, Inc.

Delineating Hydrofacies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics

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

Song, Xuehang; Chen, Xingyuan; Ye, Ming

2015-07-01

This study develops a new framework of facies-based data assimilation for characterizing spatial distribution of hydrofacies and estimating their associated hydraulic properties. This framework couples ensemble data assimilation with transition probability-based geostatistical model via a parameterization based on a level set function. The nature of ensemble data assimilation makes the framework efficient and flexible to be integrated with various types of observation data. The transition probability-based geostatistical model keeps the updated hydrofacies distributions under geological constrains. The framework is illustrated by using a two-dimensional synthetic study that estimates hydrofacies spatial distribution and permeability in each hydrofacies from transient head data.more » Our results show that the proposed framework can characterize hydrofacies distribution and associated permeability with adequate accuracy even with limited direct measurements of hydrofacies. Our study provides a promising starting point for hydrofacies delineation in complex real problems.« less

A hierarchical community occurrence model for North Carolina stream fish

USGS Publications Warehouse

Midway, S.R.; Wagner, Tyler; Tracy, B.H.

2016-01-01

The southeastern USA is home to one of the richest—and most imperiled and threatened—freshwater fish assemblages in North America. For many of these rare and threatened species, conservation efforts are often limited by a lack of data. Drawing on a unique and extensive data set spanning over 20 years, we modeled occurrence probabilities of 126 stream fish species sampled throughout North Carolina, many of which occur more broadly in the southeastern USA. Specifically, we developed species-specific occurrence probabilities from hierarchical Bayesian multispecies models that were based on common land use and land cover covariates. We also used index of biotic integrity tolerance classifications as a second level in the model hierarchy; we identify this level as informative for our work, but it is flexible for future model applications. Based on the partial-pooling property of the models, we were able to generate occurrence probabilities for many imperiled and data-poor species in addition to highlighting a considerable amount of occurrence heterogeneity that supports species-specific investigations whenever possible. Our results provide critical species-level information on many threatened and imperiled species as well as information that may assist with re-evaluation of existing management strategies, such as the use of surrogate species. Finally, we highlight the use of a relatively simple hierarchical model that can easily be generalized for similar situations in which conventional models fail to provide reliable estimates for data-poor groups.

Influence of Climate Change on Flood Hazard using Climate Informed Bayesian Hierarchical Model in Johnson Creek River

NASA Astrophysics Data System (ADS)

Zarekarizi, M.; Moradkhani, H.

2015-12-01

Extreme events are proven to be affected by climate change, influencing hydrologic simulations for which stationarity is usually a main assumption. Studies have discussed that this assumption would lead to large bias in model estimations and higher flood hazard consequently. Getting inspired by the importance of non-stationarity, we determined how the exceedance probabilities have changed over time in Johnson Creek River, Oregon. This could help estimate the probability of failure of a structure that was primarily designed to resist less likely floods according to common practice. Therefore, we built a climate informed Bayesian hierarchical model and non-stationarity was considered in modeling framework. Principle component analysis shows that North Atlantic Oscillation (NAO), Western Pacific Index (WPI) and Eastern Asia (EA) are mostly affecting stream flow in this river. We modeled flood extremes using peaks over threshold (POT) method rather than conventional annual maximum flood (AMF) mainly because it is possible to base the model on more information. We used available threshold selection methods to select a suitable threshold for the study area. Accounting for non-stationarity, model parameters vary through time with climate indices. We developed a couple of model scenarios and chose one which could best explain the variation in data based on performance measures. We also estimated return periods under non-stationarity condition. Results show that ignoring stationarity could increase the flood hazard up to four times which could increase the probability of an in-stream structure being overtopped.

A simple model for the critical mass of a nuclear weapon

NASA Astrophysics Data System (ADS)

Reed, B. Cameron

2018-07-01

A probability-based model for estimating the critical mass of a fissile isotope is developed. The model requires introducing some concepts from nuclear physics and incorporating some approximations, but gives results correct to about a factor of two for uranium-235 and plutonium-239.

Spatially explicit dynamic N-mixture models

USGS Publications Warehouse

Zhao, Qing; Royle, Andy; Boomer, G. Scott

2017-01-01

Knowledge of demographic parameters such as survival, reproduction, emigration, and immigration is essential to understand metapopulation dynamics. Traditionally the estimation of these demographic parameters requires intensive data from marked animals. The development of dynamic N-mixture models makes it possible to estimate demographic parameters from count data of unmarked animals, but the original dynamic N-mixture model does not distinguish emigration and immigration from survival and reproduction, limiting its ability to explain important metapopulation processes such as movement among local populations. In this study we developed a spatially explicit dynamic N-mixture model that estimates survival, reproduction, emigration, local population size, and detection probability from count data under the assumption that movement only occurs among adjacent habitat patches. Simulation studies showed that the inference of our model depends on detection probability, local population size, and the implementation of robust sampling design. Our model provides reliable estimates of survival, reproduction, and emigration when detection probability is high, regardless of local population size or the type of sampling design. When detection probability is low, however, our model only provides reliable estimates of survival, reproduction, and emigration when local population size is moderate to high and robust sampling design is used. A sensitivity analysis showed that our model is robust against the violation of the assumption that movement only occurs among adjacent habitat patches, suggesting wide applications of this model. Our model can be used to improve our understanding of metapopulation dynamics based on count data that are relatively easy to collect in many systems.

Quantum probability ranking principle for ligand-based virtual screening.

PubMed

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

Quantum probability ranking principle for ligand-based virtual screening

NASA Astrophysics Data System (ADS)

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

Development of a methodology for probable maximum precipitation estimation over the American River watershed using the WRF model

NASA Astrophysics Data System (ADS)

Tan, Elcin

A new physically-based methodology for probable maximum precipitation (PMP) estimation is developed over the American River Watershed (ARW) using the Weather Research and Forecast (WRF-ARW) model. A persistent moisture flux convergence pattern, called Pineapple Express, is analyzed for 42 historical extreme precipitation events, and it is found that Pineapple Express causes extreme precipitation over the basin of interest. An average correlation between moisture flux convergence and maximum precipitation is estimated as 0.71 for 42 events. The performance of the WRF model is verified for precipitation by means of calibration and independent validation of the model. The calibration procedure is performed only for the first ranked flood event 1997 case, whereas the WRF model is validated for 42 historical cases. Three nested model domains are set up with horizontal resolutions of 27 km, 9 km, and 3 km over the basin of interest. As a result of Chi-square goodness-of-fit tests, the hypothesis that "the WRF model can be used in the determination of PMP over the ARW for both areal average and point estimates" is accepted at the 5% level of significance. The sensitivities of model physics options on precipitation are determined using 28 microphysics, atmospheric boundary layer, and cumulus parameterization schemes combinations. It is concluded that the best triplet option is Thompson microphysics, Grell 3D ensemble cumulus, and YSU boundary layer (TGY), based on 42 historical cases, and this TGY triplet is used for all analyses of this research. Four techniques are proposed to evaluate physically possible maximum precipitation using the WRF: 1. Perturbations of atmospheric conditions; 2. Shift in atmospheric conditions; 3. Replacement of atmospheric conditions among historical events; and 4. Thermodynamically possible worst-case scenario creation. Moreover, climate change effect on precipitation is discussed by emphasizing temperature increase in order to determine the physically possible upper limits of precipitation due to climate change. The simulation results indicate that the meridional shift in atmospheric conditions is the optimum method to determine maximum precipitation in consideration of cost and efficiency. Finally, exceedance probability analyses of the model results of 42 historical extreme precipitation events demonstrate that the 72-hr basin averaged probable maximum precipitation is 21.72 inches for the exceedance probability of 0.5 percent. On the other hand, the current operational PMP estimation for the American River Watershed is 28.57 inches as published in the hydrometeorological report no. 59 and a previous PMP value was 31.48 inches as published in the hydrometeorological report no. 36. According to the exceedance probability analyses of this proposed method, the exceedance probabilities of these two estimations correspond to 0.036 percent and 0.011 percent, respectively.

The Objective Borderline Method: A Probabilistic Method for Standard Setting

ERIC Educational Resources Information Center

Shulruf, Boaz; Poole, Phillippa; Jones, Philip; Wilkinson, Tim

2015-01-01

A new probability-based standard setting technique, the Objective Borderline Method (OBM), was introduced recently. This was based on a mathematical model of how test scores relate to student ability. The present study refined the model and tested it using 2500 simulated data-sets. The OBM was feasible to use. On average, the OBM performed well…

Analysis of brute-force break-ins of a palmprint authentication system.

PubMed

Kong, Adams W K; Zhang, David; Kamel, Mohamed

2006-10-01

Biometric authentication systems are widely applied because they offer inherent advantages over classical knowledge-based and token-based personal-identification approaches. This has led to the development of products using palmprints as biometric traits and their use in several real applications. However, as biometric systems are vulnerable to replay, database, and brute-force attacks, such potential attacks must be analyzed before biometric systems are massively deployed in security systems. This correspondence proposes a projected multinomial distribution for studying the probability of successfully using brute-force attacks to break into a palmprint system. To validate the proposed model, we have conducted a simulation. Its results demonstrate that the proposed model can accurately estimate the probability. The proposed model indicates that it is computationally infeasible to break into the palmprint system using brute-force attacks.

A synoptic view of the Third Uniform California Earthquake Rupture Forecast (UCERF3)

USGS Publications Warehouse

Field, Edward; Jordan, Thomas H.; Page, Morgan T.; Milner, Kevin R.; Shaw, Bruce E.; Dawson, Timothy E.; Biasi, Glenn; Parsons, Thomas E.; Hardebeck, Jeanne L.; Michael, Andrew J.; Weldon, Ray; Powers, Peter; Johnson, Kaj M.; Zeng, Yuehua; Bird, Peter; Felzer, Karen; van der Elst, Nicholas; Madden, Christopher; Arrowsmith, Ramon; Werner, Maximillan J.; Thatcher, Wayne R.

2017-01-01

Probabilistic forecasting of earthquake‐producing fault ruptures informs all major decisions aimed at reducing seismic risk and improving earthquake resilience. Earthquake forecasting models rely on two scales of hazard evolution: long‐term (decades to centuries) probabilities of fault rupture, constrained by stress renewal statistics, and short‐term (hours to years) probabilities of distributed seismicity, constrained by earthquake‐clustering statistics. Comprehensive datasets on both hazard scales have been integrated into the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3). UCERF3 is the first model to provide self‐consistent rupture probabilities over forecasting intervals from less than an hour to more than a century, and it is the first capable of evaluating the short‐term hazards that result from multievent sequences of complex faulting. This article gives an overview of UCERF3, illustrates the short‐term probabilities with aftershock scenarios, and draws some valuable scientific conclusions from the modeling results. In particular, seismic, geologic, and geodetic data, when combined in the UCERF3 framework, reject two types of fault‐based models: long‐term forecasts constrained to have local Gutenberg–Richter scaling, and short‐term forecasts that lack stress relaxation by elastic rebound.

Variation Among Internet Based Calculators in Predicting Spontaneous Resolution of Vesicoureteral Reflux

PubMed Central

Routh, Jonathan C.; Gong, Edward M.; Cannon, Glenn M.; Yu, Richard N.; Gargollo, Patricio C.; Nelson, Caleb P.

2010-01-01

Purpose An increasing number of parents and practitioners use the Internet for health related purposes, and an increasing number of models are available on the Internet for predicting spontaneous resolution rates for children with vesi-coureteral reflux. We sought to determine whether currently available Internet based calculators for vesicoureteral reflux resolution produce systematically different results. Materials and Methods Following a systematic Internet search we identified 3 Internet based calculators of spontaneous resolution rates for children with vesicoureteral reflux, of which 2 were academic affiliated and 1 was industry affiliated. We generated a random cohort of 100 hypothetical patients with a wide range of clinical characteristics and entered the data on each patient into each calculator. We then compared the results from the calculators in terms of mean predicted resolution probability and number of cases deemed likely to resolve at various cutoff probabilities. Results Mean predicted resolution probabilities were 41% and 36% (range 31% to 41%) for the 2 academic affiliated calculators and 33% for the industry affiliated calculator (p = 0.02). For some patients the calculators produced markedly different probabilities of spontaneous resolution, in some instances ranging from 24% to 89% for the same patient. At thresholds greater than 5%, 10% and 25% probability of spontaneous resolution the calculators differed significantly regarding whether cases would resolve (all p < 0.0001). Conclusions Predicted probabilities of spontaneous resolution of vesicoureteral reflux differ significantly among Internet based calculators. For certain patients, particularly those with a lower probability of spontaneous resolution, these differences can significantly influence clinical decision making. PMID:20172550

An improved probit method for assessment of domino effect to chemical process equipment caused by overpressure.

PubMed

Mingguang, Zhang; Juncheng, Jiang

2008-10-30

Overpressure is one important cause of domino effect in accidents of chemical process equipments. Damage probability and relative threshold value are two necessary parameters in QRA of this phenomenon. Some simple models had been proposed based on scarce data or oversimplified assumption. Hence, more data about damage to chemical process equipments were gathered and analyzed, a quantitative relationship between damage probability and damage degrees of equipment was built, and reliable probit models were developed associated to specific category of chemical process equipments. Finally, the improvements of present models were evidenced through comparison with other models in literatures, taking into account such parameters: consistency between models and data, depth of quantitativeness in QRA.

A collision model for safety evaluation of autonomous intelligent cruise control.

PubMed

Touran, A; Brackstone, M A; McDonald, M

1999-09-01

This paper describes a general framework for safety evaluation of autonomous intelligent cruise control in rear-end collisions. Using data and specifications from prototype devices, two collision models are developed. One model considers a train of four cars, one of which is equipped with autonomous intelligent cruise control. This model considers the car in front and two cars following the equipped car. In the second model, none of the cars is equipped with the device. Each model can predict the possibility of rear-end collision between cars under various conditions by calculating the remaining distance between cars after the front car brakes. Comparing the two collision models allows one to evaluate the effectiveness of autonomous intelligent cruise control in preventing collisions. The models are then subjected to Monte Carlo simulation to calculate the probability of collision. Based on crash probabilities, an expected value is calculated for the number of cars involved in any collision. It is found that given the model assumptions, while equipping a car with autonomous intelligent cruise control can significantly reduce the probability of the collision with the car ahead, it may adversely affect the situation for the following cars.

A global logrank test for adaptive treatment strategies based on observational studies.

PubMed

Li, Zhiguo; Valenstein, Marcia; Pfeiffer, Paul; Ganoczy, Dara

2014-02-28

In studying adaptive treatment strategies, a natural question that is of paramount interest is whether there is any significant difference among all possible treatment strategies. When the outcome variable of interest is time-to-event, we propose an inverse probability weighted logrank test for testing the equivalence of a fixed set of pre-specified adaptive treatment strategies based on data from an observational study. The weights take into account both the possible selection bias in an observational study and the fact that the same subject may be consistent with more than one treatment strategy. The asymptotic distribution of the weighted logrank statistic under the null hypothesis is obtained. We show that, in an observational study where the treatment selection probabilities need to be estimated, the estimation of these probabilities does not have an effect on the asymptotic distribution of the weighted logrank statistic, as long as the estimation of the parameters in the models for these probabilities is n-consistent. Finite sample performance of the test is assessed via a simulation study. We also show in the simulation that the test can be pretty robust to misspecification of the models for the probabilities of treatment selection. The method is applied to analyze data on antidepressant adherence time from an observational database maintained at the Department of Veterans Affairs' Serious Mental Illness Treatment Research and Evaluation Center. Copyright © 2013 John Wiley & Sons, Ltd.

Automated side-chain model building and sequence assignment by template matching.

PubMed

Terwilliger, Thomas C

2003-01-01

An algorithm is described for automated building of side chains in an electron-density map once a main-chain model is built and for alignment of the protein sequence to the map. The procedure is based on a comparison of electron density at the expected side-chain positions with electron-density templates. The templates are constructed from average amino-acid side-chain densities in 574 refined protein structures. For each contiguous segment of main chain, a matrix with entries corresponding to an estimate of the probability that each of the 20 amino acids is located at each position of the main-chain model is obtained. The probability that this segment corresponds to each possible alignment with the sequence of the protein is estimated using a Bayesian approach and high-confidence matches are kept. Once side-chain identities are determined, the most probable rotamer for each side chain is built into the model. The automated procedure has been implemented in the RESOLVE software. Combined with automated main-chain model building, the procedure produces a preliminary model suitable for refinement and extension by an experienced crystallographer.

A probabilistic NF2 relational algebra for integrated information retrieval and database systems

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

Fuhr, N.; Roelleke, T.

The integration of information retrieval (IR) and database systems requires a data model which allows for modelling documents as entities, representing uncertainty and vagueness and performing uncertain inference. For this purpose, we present a probabilistic data model based on relations in non-first-normal-form (NF2). Here, tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Thus, the set of weighted index terms of a document are represented as a probabilistic subrelation. In a similar way, imprecise attribute values are modelled as a set-valued attribute. We redefine the relational operators for this type of relations such thatmore » the result of each operator is again a probabilistic NF2 relation, where the weight of a tuple gives the probability that this tuple belongs to the result. By ordering the tuples according to decreasing probabilities, the model yields a ranking of answers like in most IR models. This effect also can be used for typical database queries involving imprecise attribute values as well as for combinations of database and IR queries.« less

Propagating Mixed Uncertainties in Cyber Attacker Payoffs: Exploration of Two-Phase Monte Carlo Sampling and Probability Bounds Analysis

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

Chatterjee, Samrat; Tipireddy, Ramakrishna; Oster, Matthew R.

Securing cyber-systems on a continual basis against a multitude of adverse events is a challenging undertaking. Game-theoretic approaches, that model actions of strategic decision-makers, are increasingly being applied to address cybersecurity resource allocation challenges. Such game-based models account for multiple player actions and represent cyber attacker payoffs mostly as point utility estimates. Since a cyber-attacker’s payoff generation mechanism is largely unknown, appropriate representation and propagation of uncertainty is a critical task. In this paper we expand on prior work and focus on operationalizing the probabilistic uncertainty quantification framework, for a notional cyber system, through: 1) representation of uncertain attacker andmore » system-related modeling variables as probability distributions and mathematical intervals, and 2) exploration of uncertainty propagation techniques including two-phase Monte Carlo sampling and probability bounds analysis.« less

Fixation of slightly beneficial mutations: effects of life history.

PubMed

Vindenes, Yngvild; Lee, Aline Magdalena; Engen, Steinar; Saether, Bernt-Erik

2010-04-01

Recent studies of rates of evolution have revealed large systematic differences among organisms with different life histories, both within and among taxa. Here, we consider how life history may affect the rate of evolution via its influence on the fixation probability of slightly beneficial mutations. Our approach is based on diffusion modeling for a finite, stage-structured population with stochastic population dynamics. The results, which are verified by computer simulations, demonstrate that even with complex population structure just two demographic parameters are sufficient to give an accurate approximation of the fixation probability of a slightly beneficial mutation. These are the reproductive value of the stage in which the mutation first occurs and the demographic variance of the population. The demographic variance also determines what influence population size has on the fixation probability. This model represents a substantial generalization of earlier models, covering a large range of life histories.

Sequential Probability Ratio Test for Spacecraft Collision Avoidance Maneuver Decisions

NASA Technical Reports Server (NTRS)

Carpenter, J. Russell; Markley, F. Landis

2013-01-01

A document discusses sequential probability ratio tests that explicitly allow decision-makers to incorporate false alarm and missed detection risks, and are potentially less sensitive to modeling errors than a procedure that relies solely on a probability of collision threshold. Recent work on constrained Kalman filtering has suggested an approach to formulating such a test for collision avoidance maneuver decisions: a filter bank with two norm-inequality-constrained epoch-state extended Kalman filters. One filter models the null hypotheses that the miss distance is inside the combined hard body radius at the predicted time of closest approach, and one filter models the alternative hypothesis. The epoch-state filter developed for this method explicitly accounts for any process noise present in the system. The method appears to work well using a realistic example based on an upcoming, highly elliptical orbit formation flying mission.

Spatial patterns of breeding success of grizzly bears derived from hierarchical multistate models.

PubMed

Fisher, Jason T; Wheatley, Matthew; Mackenzie, Darryl

2014-10-01

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low-elevation wetlands or mid-elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy-herbaceous alpine ecotones-were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. © 2014 Society for Conservation Biology.

Unit-Sphere Anisotropic Multiaxial Stochastic-Strength Model Probability Density Distribution for the Orientation of Critical Flaws

NASA Technical Reports Server (NTRS)

Nemeth, Noel

2013-01-01

Models that predict the failure probability of monolithic glass and ceramic components under multiaxial loading have been developed by authors such as Batdorf, Evans, and Matsuo. These "unit-sphere" failure models assume that the strength-controlling flaws are randomly oriented, noninteracting planar microcracks of specified geometry but of variable size. This report develops a formulation to describe the probability density distribution of the orientation of critical strength-controlling flaws that results from an applied load. This distribution is a function of the multiaxial stress state, the shear sensitivity of the flaws, the Weibull modulus, and the strength anisotropy. Examples are provided showing the predicted response on the unit sphere for various stress states for isotropic and transversely isotropic (anisotropic) materials--including the most probable orientation of critical flaws for offset uniaxial loads with strength anisotropy. The author anticipates that this information could be used to determine anisotropic stiffness degradation or anisotropic damage evolution for individual brittle (or quasi-brittle) composite material constituents within finite element or micromechanics-based software

Second look at the spread of epidemics on networks

NASA Astrophysics Data System (ADS)

Kenah, Eben; Robins, James M.

2007-09-01

In an important paper, Newman [Phys. Rev. E66, 016128 (2002)] claimed that a general network-based stochastic Susceptible-Infectious-Removed (SIR) epidemic model is isomorphic to a bond percolation model, where the bonds are the edges of the contact network and the bond occupation probability is equal to the marginal probability of transmission from an infected node to a susceptible neighbor. In this paper, we show that this isomorphism is incorrect and define a semidirected random network we call the epidemic percolation network that is exactly isomorphic to the SIR epidemic model in any finite population. In the limit of a large population, (i) the distribution of (self-limited) outbreak sizes is identical to the size distribution of (small) out-components, (ii) the epidemic threshold corresponds to the phase transition where a giant strongly connected component appears, (iii) the probability of a large epidemic is equal to the probability that an initial infection occurs in the giant in-component, and (iv) the relative final size of an epidemic is equal to the proportion of the network contained in the giant out-component. For the SIR model considered by Newman, we show that the epidemic percolation network predicts the same mean outbreak size below the epidemic threshold, the same epidemic threshold, and the same final size of an epidemic as the bond percolation model. However, the bond percolation model fails to predict the correct outbreak size distribution and probability of an epidemic when there is a nondegenerate infectious period distribution. We confirm our findings by comparing predictions from percolation networks and bond percolation models to the results of simulations. In the Appendix, we show that an isomorphism to an epidemic percolation network can be defined for any time-homogeneous stochastic SIR model.

Operational Earthquake Forecasting: Proposed Guidelines for Implementation (Invited)

NASA Astrophysics Data System (ADS)

Jordan, T. H.

2010-12-01

The goal of operational earthquake forecasting (OEF) is to provide the public with authoritative information about how seismic hazards are changing with time. During periods of high seismic activity, short-term earthquake forecasts based on empirical statistical models can attain nominal probability gains in excess of 100 relative to the long-term forecasts used in probabilistic seismic hazard analysis (PSHA). Prospective experiments are underway by the Collaboratory for the Study of Earthquake Predictability (CSEP) to evaluate the reliability and skill of these seismicity-based forecasts in a variety of tectonic environments. How such information should be used for civil protection is by no means clear, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing formal procedures for OEF in this sort of “low-probability environment.” Nevertheless, the need to move more quickly towards OEF has been underscored by recent experiences, such as the 2009 L’Aquila earthquake sequence and other seismic crises in which an anxious public has been confused by informal, inconsistent earthquake forecasts. Whether scientists like it or not, rising public expectations for real-time information, accelerated by the use of social media, will require civil protection agencies to develop sources of authoritative information about the short-term earthquake probabilities. In this presentation, I will discuss guidelines for the implementation of OEF informed by my experience on the California Earthquake Prediction Evaluation Council, convened by CalEMA, and the International Commission on Earthquake Forecasting, convened by the Italian government following the L’Aquila disaster. (a) Public sources of information on short-term probabilities should be authoritative, scientific, open, and timely, and they need to convey the epistemic uncertainties in the operational forecasts. (b) Earthquake probabilities should be based on operationally qualified, regularly updated forecasting systems. All operational procedures should be rigorously reviewed by experts in the creation, delivery, and utility of earthquake forecasts. (c) The quality of all operational models should be evaluated for reliability and skill by retrospective testing, and the models should be under continuous prospective testing in a CSEP-type environment against established long-term forecasts and a wide variety of alternative, time-dependent models. (d) Short-term models used in operational forecasting should be consistent with the long-term forecasts used in PSHA. (e) Alert procedures should be standardized to facilitate decisions at different levels of government and among the public, based in part on objective analysis of costs and benefits. (f) In establishing alert procedures, consideration should also be made of the less tangible aspects of value-of-information, such as gains in psychological preparedness and resilience. Authoritative statements of increased risk, even when the absolute probability is low, can provide a psychological benefit to the public by filling information vacuums that can lead to informal predictions and misinformation.

Development and Validation of a Calculator for Estimating the Probability of Urinary Tract Infection in Young Febrile Children.

PubMed

Shaikh, Nader; Hoberman, Alejandro; Hum, Stephanie W; Alberty, Anastasia; Muniz, Gysella; Kurs-Lasky, Marcia; Landsittel, Douglas; Shope, Timothy

2018-06-01

Accurately estimating the probability of urinary tract infection (UTI) in febrile preverbal children is necessary to appropriately target testing and treatment. To develop and test a calculator (UTICalc) that can first estimate the probability of UTI based on clinical variables and then update that probability based on laboratory results. Review of electronic medical records of febrile children aged 2 to 23 months who were brought to the emergency department of Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania. An independent training database comprising 1686 patients brought to the emergency department between January 1, 2007, and April 30, 2013, and a validation database of 384 patients were created. Five multivariable logistic regression models for predicting risk of UTI were trained and tested. The clinical model included only clinical variables; the remaining models incorporated laboratory results. Data analysis was performed between June 18, 2013, and January 12, 2018. Documented temperature of 38°C or higher in children aged 2 months to less than 2 years. With the use of culture-confirmed UTI as the main outcome, cutoffs for high and low UTI risk were identified for each model. The resultant models were incorporated into a calculation tool, UTICalc, which was used to evaluate medical records. A total of 2070 children were included in the study. The training database comprised 1686 children, of whom 1216 (72.1%) were female and 1167 (69.2%) white. The validation database comprised 384 children, of whom 291 (75.8%) were female and 200 (52.1%) white. Compared with the American Academy of Pediatrics algorithm, the clinical model in UTICalc reduced testing by 8.1% (95% CI, 4.2%-12.0%) and decreased the number of UTIs that were missed from 3 cases to none. Compared with empirically treating all children with a leukocyte esterase test result of 1+ or higher, the dipstick model in UTICalc would have reduced the number of treatment delays by 10.6% (95% CI, 0.9%-20.4%). UTICalc estimates the probability of UTI by evaluating the risk factors present in the individual child. As a result, testing and treatment can be tailored, thereby improving outcomes for children with UTI.

A physically-based earthquake recurrence model for estimation of long-term earthquake probabilities

USGS Publications Warehouse

Ellsworth, William L.; Matthews, Mark V.; Nadeau, Robert M.; Nishenko, Stuart P.; Reasenberg, Paul A.; Simpson, Robert W.

1999-01-01

A physically-motivated model for earthquake recurrence based on the Brownian relaxation oscillator is introduced. The renewal process defining this point process model can be described by the steady rise of a state variable from the ground state to failure threshold as modulated by Brownian motion. Failure times in this model follow the Brownian passage time (BPT) distribution, which is specified by the mean time to failure, μ, and the aperiodicity of the mean, α (equivalent to the familiar coefficient of variation). Analysis of 37 series of recurrent earthquakes, M -0.7 to 9.2, suggests a provisional generic value of α = 0.5. For this value of α, the hazard function (instantaneous failure rate of survivors) exceeds the mean rate for times > μ⁄2, and is ~ ~ 2 ⁄ μ for all times > μ. Application of this model to the next M 6 earthquake on the San Andreas fault at Parkfield, California suggests that the annual probability of the earthquake is between 1:10 and 1:13.

Data Base for a National Mineral-Resource Assessment of Undiscovered Deposits of Gold, Silver, Copper, Lead, and Zinc in the Conterminous United States

USGS Publications Warehouse

Ludington, S.D.; Cox, D.P.; McCammon, R.B.

1996-01-01

For this assessment, the conterminous United States was divided into 12 regions Adirondack Mountains, Central and Southern Rocky Mountains, Colorado Plateau, East Central, Great Basin, Great Plains, Lake Superior, Northern Appalachians, Northern Rocky Mountains, Pacific Coast, Southern Appalachians, and Southern Basin and Range. The assessment, which was conducted by regional assessment teams of scientists from the USGS, was based on the concepts of permissive tracts and deposit models. Permissive tracts are discrete areas of the United States for which estimates of numbers of undiscovered deposits of a particular deposit type were made. A permissive tract is defined by its geographic boundaries such that the probability of deposits of the type delineated occurring outside the boundary is neglible. Deposit models, which are based on a compilation of worldwide literature and on observation, are sets of data in a convenient form that describe a group of deposits which have similar characteristics and that contain information on the common geologic attributes of the deposits and the environments in which they are found. Within each region, the assessment teams delineated permissive tracts for those deposit models that were judged to be appropriate and, when the amount of information warranted, estimated the number of undiscovered deposits. A total of 46 deposit models were used to assess 236 separate permissive tracts. Estimates of undiscovered deposits were limited to a depth of 1 km beneath the surface of the Earth. The estimates of the number of undiscovered deposits of gold, silver, copper, lead, and zinc were expressed in the form of a probability distribution. Commonly, the number of undiscovered deposits was estimated at the 90th, 50th, and 10th percentiles. A Monte Carlo simulation computer program was used to combine the probability distribution of the number of undiscovered deposits with the grade and tonnage data sets associated with each deposit model to obtain the probability distribution for undiscovered metal.

Methods for estimating annual exceedance probability discharges for streams in Arkansas, based on data through water year 2013

USGS Publications Warehouse

Wagner, Daniel M.; Krieger, Joshua D.; Veilleux, Andrea G.

2016-08-04

In 2013, the U.S. Geological Survey initiated a study to update regional skew, annual exceedance probability discharges, and regional regression equations used to estimate annual exceedance probability discharges for ungaged locations on streams in the study area with the use of recent geospatial data, new analytical methods, and available annual peak-discharge data through the 2013 water year. An analysis of regional skew using Bayesian weighted least-squares/Bayesian generalized-least squares regression was performed for Arkansas, Louisiana, and parts of Missouri and Oklahoma. The newly developed constant regional skew of -0.17 was used in the computation of annual exceedance probability discharges for 281 streamgages used in the regional regression analysis. Based on analysis of covariance, four flood regions were identified for use in the generation of regional regression models. Thirty-nine basin characteristics were considered as potential explanatory variables, and ordinary least-squares regression techniques were used to determine the optimum combinations of basin characteristics for each of the four regions. Basin characteristics in candidate models were evaluated based on multicollinearity with other basin characteristics (variance inflation factor < 2.5) and statistical significance at the 95-percent confidence level (p ≤ 0.05). Generalized least-squares regression was used to develop the final regression models for each flood region. Average standard errors of prediction of the generalized least-squares models ranged from 32.76 to 59.53 percent, with the largest range in flood region D. Pseudo coefficients of determination of the generalized least-squares models ranged from 90.29 to 97.28 percent, with the largest range also in flood region D. The regional regression equations apply only to locations on streams in Arkansas where annual peak discharges are not substantially affected by regulation, diversion, channelization, backwater, or urbanization. The applicability and accuracy of the regional regression equations depend on the basin characteristics measured for an ungaged location on a stream being within range of those used to develop the equations.

Multi-model ensembles for assessment of flood losses and associated uncertainty

NASA Astrophysics Data System (ADS)

Figueiredo, Rui; Schröter, Kai; Weiss-Motz, Alexander; Martina, Mario L. V.; Kreibich, Heidi

2018-05-01

Flood loss modelling is a crucial part of risk assessments. However, it is subject to large uncertainty that is often neglected. Most models available in the literature are deterministic, providing only single point estimates of flood loss, and large disparities tend to exist among them. Adopting any one such model in a risk assessment context is likely to lead to inaccurate loss estimates and sub-optimal decision-making. In this paper, we propose the use of multi-model ensembles to address these issues. This approach, which has been applied successfully in other scientific fields, is based on the combination of different model outputs with the aim of improving the skill and usefulness of predictions. We first propose a model rating framework to support ensemble construction, based on a probability tree of model properties, which establishes relative degrees of belief between candidate models. Using 20 flood loss models in two test cases, we then construct numerous multi-model ensembles, based both on the rating framework and on a stochastic method, differing in terms of participating members, ensemble size and model weights. We evaluate the performance of ensemble means, as well as their probabilistic skill and reliability. Our results demonstrate that well-designed multi-model ensembles represent a pragmatic approach to consistently obtain more accurate flood loss estimates and reliable probability distributions of model uncertainty.

Modeling evaporation of Jet A, JP-7 and RP-1 drops at 1 to 15 bars

NASA Technical Reports Server (NTRS)

Harstad, K.; Bellan, J.

2003-01-01

A model describing the evaportion of an isolated drop of a multicomponent fuel containing hundreds of species has been developed. The model is based on Continuous Thermodynamics concepts wherein the composition of a fuel is statistically described using a Probability Distribution Function (PDF).

On the use of the energy probability distribution zeros in the study of phase transitions

NASA Astrophysics Data System (ADS)

Mól, L. A. S.; Rodrigues, R. G. M.; Stancioli, R. A.; Rocha, J. C. S.; Costa, B. V.

2018-04-01

This contribution is devoted to cover some technical aspects related to the use of the recently proposed energy probability distribution zeros in the study of phase transitions. This method is based on the partial knowledge of the partition function zeros and has been shown to be extremely efficient to precisely locate phase transition temperatures. It is based on an iterative method in such a way that the transition temperature can be approached at will. The iterative method will be detailed and some convergence issues that has been observed in its application to the 2D Ising model and to an artificial spin ice model will be shown, together with ways to circumvent them.

Model-based clustering for RNA-seq data.

PubMed

Si, Yaqing; Liu, Peng; Li, Pinghua; Brutnell, Thomas P

2014-01-15

RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis. In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models. An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org

Species Distribution Models and Ecological Suitability Analysis for Potential Tick Vectors of Lyme Disease in Mexico

PubMed Central

Illoldi-Rangel, Patricia; Rivaldi, Chissa-Louise; Sissel, Blake; Trout Fryxell, Rebecca; Gordillo-Pérez, Guadalupe; Rodríguez-Moreno, Angel; Williamson, Phillip; Montiel-Parra, Griselda; Sánchez-Cordero, Víctor; Sarkar, Sahotra

2012-01-01

Species distribution models were constructed for ten Ixodes species and Amblyomma cajennense for a region including Mexico and Texas. The model was based on a maximum entropy algorithm that used environmental layers to predict the relative probability of presence for each taxon. For Mexico, species geographic ranges were predicted by restricting the models to cells which have a higher probability than the lowest probability of the cells in which a presence record was located. There was spatial nonconcordance between the distributions of Amblyomma cajennense and the Ixodes group with the former restricted to lowlands and mainly the eastern coast of Mexico and the latter to montane regions with lower temperature. The risk of Lyme disease is, therefore, mainly present in the highlands where some Ixodes species are known vectors; if Amblyomma cajennense turns out to be a competent vector, the area of risk also extends to the lowlands and the east coast. PMID:22518171

Species distribution models and ecological suitability analysis for potential tick vectors of lyme disease in Mexico.

PubMed

Illoldi-Rangel, Patricia; Rivaldi, Chissa-Louise; Sissel, Blake; Trout Fryxell, Rebecca; Gordillo-Pérez, Guadalupe; Rodríguez-Moreno, Angel; Williamson, Phillip; Montiel-Parra, Griselda; Sánchez-Cordero, Víctor; Sarkar, Sahotra

2012-01-01

Species distribution models were constructed for ten Ixodes species and Amblyomma cajennense for a region including Mexico and Texas. The model was based on a maximum entropy algorithm that used environmental layers to predict the relative probability of presence for each taxon. For Mexico, species geographic ranges were predicted by restricting the models to cells which have a higher probability than the lowest probability of the cells in which a presence record was located. There was spatial nonconcordance between the distributions of Amblyomma cajennense and the Ixodes group with the former restricted to lowlands and mainly the eastern coast of Mexico and the latter to montane regions with lower temperature. The risk of Lyme disease is, therefore, mainly present in the highlands where some Ixodes species are known vectors; if Amblyomma cajennense turns out to be a competent vector, the area of risk also extends to the lowlands and the east coast.

Extinction time of a stochastic predator-prey model by the generalized cell mapping method

NASA Astrophysics Data System (ADS)

Han, Qun; Xu, Wei; Hu, Bing; Huang, Dongmei; Sun, Jian-Qiao

2018-03-01

The stochastic response and extinction time of a predator-prey model with Gaussian white noise excitations are studied by the generalized cell mapping (GCM) method based on the short-time Gaussian approximation (STGA). The methods for stochastic response probability density functions (PDFs) and extinction time statistics are developed. The Taylor expansion is used to deal with non-polynomial nonlinear terms of the model for deriving the moment equations with Gaussian closure, which are needed for the STGA in order to compute the one-step transition probabilities. The work is validated with direct Monte Carlo simulations. We have presented the transient responses showing the evolution from a Gaussian initial distribution to a non-Gaussian steady-state one. The effects of the model parameter and noise intensities on the steady-state PDFs are discussed. It is also found that the effects of noise intensities on the extinction time statistics are opposite to the effects on the limit probability distributions of the survival species.

Administrative data measured surgical site infection probability within 30 days of surgery in elderly patients.

PubMed

van Walraven, Carl; Jackson, Timothy D; Daneman, Nick

2016-09-01

Elderly patients are inordinately affected by surgical site infections (SSIs). This study derived and internally validated a model that used routinely collected health administrative data to measure the probability of SSI in elderly patients within 30 days of surgery. All people exceeding 65 years undergoing surgery from two hospitals with known SSI status were linked to population-based administrative data sets in Ontario, Canada. We used bootstrap methods to create a multivariate model that used health administrative data to predict the probability of SSI. Of 3,436 patients, 177 (5.1%) had an SSI. The Elderly SSI Risk Model included six covariates: number of distinct physician fee codes within 30 days of surgery; presence or absence of a postdischarge prescription for an antibiotic; presence or absence of three diagnostic codes; and a previously derived score that gauged SSI risk based on procedure codes. The model was highly explanatory (Nagelkerke's R 2 , 0.458), strongly discriminative (C statistic, 0.918), and well calibrated (calibration slope, 1). Health administrative data can effectively determine 30-day risk of SSI risk in elderly patients undergoing a broad assortment of surgeries. External validation is necessary before this can be routinely used to monitor SSIs in the elderly. Copyright © 2016 Elsevier Inc. All rights reserved.

Probability concepts in quality risk management.

PubMed

Claycamp, H Gregg

2012-01-01

Essentially any concept of risk is built on fundamental concepts of chance, likelihood, or probability. Although risk is generally a probability of loss of something of value, given that a risk-generating event will occur or has occurred, it is ironic that the quality risk management literature and guidelines on quality risk management tools are relatively silent on the meaning and uses of "probability." The probability concept is typically applied by risk managers as a combination of frequency-based calculation and a "degree of belief" meaning of probability. Probability as a concept that is crucial for understanding and managing risk is discussed through examples from the most general, scenario-defining and ranking tools that use probability implicitly to more specific probabilistic tools in risk management. A rich history of probability in risk management applied to other fields suggests that high-quality risk management decisions benefit from the implementation of more thoughtful probability concepts in both risk modeling and risk management. Essentially any concept of risk is built on fundamental concepts of chance, likelihood, or probability. Although "risk" generally describes a probability of loss of something of value, given that a risk-generating event will occur or has occurred, it is ironic that the quality risk management literature and guidelines on quality risk management methodologies and respective tools focus on managing severity but are relatively silent on the in-depth meaning and uses of "probability." Pharmaceutical manufacturers are expanding their use of quality risk management to identify and manage risks to the patient that might occur in phases of the pharmaceutical life cycle from drug development to manufacture, marketing to product discontinuation. A probability concept is typically applied by risk managers as a combination of data-based measures of probability and a subjective "degree of belief" meaning of probability. Probability as a concept that is crucial for understanding and managing risk is discussed through examples from the most general, scenario-defining and ranking tools that use probability implicitly to more specific probabilistic tools in risk management.

Monte Carlo decision curve analysis using aggregate data.

PubMed

Hozo, Iztok; Tsalatsanis, Athanasios; Djulbegovic, Benjamin

2017-02-01

Decision curve analysis (DCA) is an increasingly used method for evaluating diagnostic tests and predictive models, but its application requires individual patient data. The Monte Carlo (MC) method can be used to simulate probabilities and outcomes of individual patients and offers an attractive option for application of DCA. We constructed a MC decision model to simulate individual probabilities of outcomes of interest. These probabilities were contrasted against the threshold probability at which a decision-maker is indifferent between key management strategies: treat all, treat none or use predictive model to guide treatment. We compared the results of DCA with MC simulated data against the results of DCA based on actual individual patient data for three decision models published in the literature: (i) statins for primary prevention of cardiovascular disease, (ii) hospice referral for terminally ill patients and (iii) prostate cancer surgery. The results of MC DCA and patient data DCA were identical. To the extent that patient data DCA were used to inform decisions about statin use, referral to hospice or prostate surgery, the results indicate that MC DCA could have also been used. As long as the aggregate parameters on distribution of the probability of outcomes and treatment effects are accurately described in the published reports, the MC DCA will generate indistinguishable results from individual patient data DCA. We provide a simple, easy-to-use model, which can facilitate wider use of DCA and better evaluation of diagnostic tests and predictive models that rely only on aggregate data reported in the literature. © 2017 Stichting European Society for Clinical Investigation Journal Foundation.

Time-dependent earthquake probabilities

USGS Publications Warehouse

Gomberg, J.; Belardinelli, M.E.; Cocco, M.; Reasenberg, P.

2005-01-01

We have attempted to provide a careful examination of a class of approaches for estimating the conditional probability of failure of a single large earthquake, particularly approaches that account for static stress perturbations to tectonic loading as in the approaches of Stein et al. (1997) and Hardebeck (2004). We have loading as in the framework based on a simple, generalized rate change formulation and applied it to these two approaches to show how they relate to one another. We also have attempted to show the connection between models of seismicity rate changes applied to (1) populations of independent faults as in background and aftershock seismicity and (2) changes in estimates of the conditional probability of failures of different members of a the notion of failure rate corresponds to successive failures of different members of a population of faults. The latter application requires specification of some probability distribution (density function of PDF) that describes some population of potential recurrence times. This PDF may reflect our imperfect knowledge of when past earthquakes have occurred on a fault (epistemic uncertainty), the true natural variability in failure times, or some combination of both. We suggest two end-member conceptual single-fault models that may explain natural variability in recurrence times and suggest how they might be distinguished observationally. When viewed deterministically, these single-fault patch models differ significantly in their physical attributes, and when faults are immature, they differ in their responses to stress perturbations. Estimates of conditional failure probabilities effectively integrate over a range of possible deterministic fault models, usually with ranges that correspond to mature faults. Thus conditional failure probability estimates usually should not differ significantly for these models. Copyright 2005 by the American Geophysical Union.

Drought forecasting in Luanhe River basin involving climatic indices

NASA Astrophysics Data System (ADS)

Ren, Weinan; Wang, Yixuan; Li, Jianzhu; Feng, Ping; Smith, Ronald J.

2017-11-01

Drought is regarded as one of the most severe natural disasters globally. This is especially the case in Tianjin City, Northern China, where drought can affect economic development and people's livelihoods. Drought forecasting, the basis of drought management, is an important mitigation strategy. In this paper, we evolve a probabilistic forecasting model, which forecasts transition probabilities from a current Standardized Precipitation Index (SPI) value to a future SPI class, based on conditional distribution of multivariate normal distribution to involve two large-scale climatic indices at the same time, and apply the forecasting model to 26 rain gauges in the Luanhe River basin in North China. The establishment of the model and the derivation of the SPI are based on the hypothesis of aggregated monthly precipitation that is normally distributed. Pearson correlation and Shapiro-Wilk normality tests are used to select appropriate SPI time scale and large-scale climatic indices. Findings indicated that longer-term aggregated monthly precipitation, in general, was more likely to be considered normally distributed and forecasting models should be applied to each gauge, respectively, rather than to the whole basin. Taking Liying Gauge as an example, we illustrate the impact of the SPI time scale and lead time on transition probabilities. Then, the controlled climatic indices of every gauge are selected by Pearson correlation test and the multivariate normality of SPI, corresponding climatic indices for current month and SPI 1, 2, and 3 months later are demonstrated using Shapiro-Wilk normality test. Subsequently, we illustrate the impact of large-scale oceanic-atmospheric circulation patterns on transition probabilities. Finally, we use a score method to evaluate and compare the performance of the three forecasting models and compare them with two traditional models which forecast transition probabilities from a current to a future SPI class. The results show that the three proposed models outperform the two traditional models and involving large-scale climatic indices can improve the forecasting accuracy.

There Once Was a 9-Block ...--A Middle-School Design for Probability and Statistics

ERIC Educational Resources Information Center

Abrahamson, Dor; Janusz, Ruth M.; Wilensky, Uri

2006-01-01

ProbLab is a probability-and-statistics unit developed at the Center for Connected Learning and Computer-Based Modeling, Northwestern University. Students analyze the combinatorial space of the 9-block, a 3-by-3 grid of squares, in which each square can be either green or blue. All 512 possible 9-blocks are constructed and assembled in a "bar…

Comparison of methods for estimating density of forest songbirds from point counts

Treesearch

Jennifer L. Reidy; Frank R. Thompson; J. Wesley. Bailey

2011-01-01

New analytical methods have been promoted for estimating the probability of detection and density of birds from count data but few studies have compared these methods using real data. We compared estimates of detection probability and density from distance and time-removal models and survey protocols based on 5- or 10-min counts and outer radii of 50 or 100 m. We...

A Bayesian Approach to Evaluating Consistency between Climate Model Output and Observations

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Cressie, N.; Teixeira, J.

2010-12-01

Like other scientific and engineering problems that involve physical modeling of complex systems, climate models can be evaluated and diagnosed by comparing their output to observations of similar quantities. Though the global remote sensing data record is relatively short by climate research standards, these data offer opportunities to evaluate model predictions in new ways. For example, remote sensing data are spatially and temporally dense enough to provide distributional information that goes beyond simple moments to allow quantification of temporal and spatial dependence structures. In this talk, we propose a new method for exploiting these rich data sets using a Bayesian paradigm. For a collection of climate models, we calculate posterior probabilities its members best represent the physical system each seeks to reproduce. The posterior probability is based on the likelihood that a chosen summary statistic, computed from observations, would be obtained when the model's output is considered as a realization from a stochastic process. By exploring how posterior probabilities change with different statistics, we may paint a more quantitative and complete picture of the strengths and weaknesses of the models relative to the observations. We demonstrate our method using model output from the CMIP archive, and observations from NASA's Atmospheric Infrared Sounder.

Bayesics

NASA Astrophysics Data System (ADS)

Skilling, John

2005-11-01

This tutorial gives a basic overview of Bayesian methodology, from its axiomatic foundation through the conventional development of data analysis and model selection to its rôle in quantum mechanics, and ending with some comments on inference in general human affairs. The central theme is that probability calculus is the unique language within which we can develop models of our surroundings that have predictive capability. These models are patterns of belief; there is no need to claim external reality. 1. Logic and probability 2. Probability and inference 3. Probability and model selection 4. Prior probabilities 5. Probability and frequency 6. Probability and quantum mechanics 7. Probability and fundamentalism 8. Probability and deception 9. Prediction and truth

Can we expect to predict climate if we cannot shadow weather?

NASA Astrophysics Data System (ADS)

Smith, Leonard

2010-05-01

What limits our ability to predict (or project) useful statistics of future climate? And how might we quantify those limits? In the early 1960s, Ed Lorenz illustrated one constraint on point forecasts of the weather (chaos) while noting another (model imperfections). In the mid-sixties he went on to discuss climate prediction, noting that chaos, per se, need not limit accurate forecasts of averages and the distributions that define climate. In short, chaos might place draconian limits on what we can say about a particular summer day in 2010 (or 2040), but it need not limit our ability to make accurate and informative statements about the weather over this summer as a whole, or climate distributions of the 2040's. If not chaos, what limits our ability to produce decision relevant probability distribution functions (PDFs)? Is this just a question of technology (raw computer power) and uncertain boundary conditions (emission scenarios)? Arguably, current model simulations of the Earth's climate are limited by model inadequacy: not that the initial or boundary conditions are unknown but that state-of-the-art models would not yield decision-relevant probability distributions even if they were known. Or to place this statement in an empirically falsifiable format: that in 2100 when the boundary conditions are known and computer power is (hopefully) sufficient to allow exhaustive exploration of today's state-of-the-art models: we will find today's models do not admit a trajectory consistent with our knowledge of the state of the earth in 2009 which would prove of decision support relevance for, say, 25 km, hourly resolution. In short: today's models cannot shadow the weather of this century even after the fact. Restating this conjecture in a more positive frame: a 2100 historian of science will be able to determine the highest space and time scales on which 2009 models could have (i) produced trajectories plausibly consistent with the (by then) observed twenty-first century and (ii) produced probability distributions useful as such for decision support. As it will be some time until such conjectures can be refuted, how might we best advise decision makers of the detail (specifically, space and time resolution of a quantity of interest as a function of lead-time) that it is rational to interpret model-based PDFs as decision-relevant probability distributions? Given the nonlinearities already incorporated in our models, how far into the future can one expect a simulation to get the temperature "right" given the simulation has precipitation badly "wrong"? When can biases in local temperature which melt model-ice no longer be dismissed, and neglected by presenting model-anomalies? At what lead times will feedbacks due to model inadequacies cause the 2007 model simulations to drift away from what today's basic science (and 2100 computer power) would suggest? How might one justify quantitative claims regarding "extreme events" (or NUMB weather)? Models are unlikely to forecast things they cannot shadow, or at least track. There is no constraint on rational scientists to take model distributions as their subjective probabilities, unless they believe the model is empirically adequate. How then are we to use today's simulations to inform today's decisions? Two approaches are considered. The first augments the model-based PDF with an explicit subjective-probability of a "Big Surprise". The second is to look not for a PDF but, following Solvency II, consider the risk from any event that cannot be ruled out at, say, the one in 200 level. The fact that neither approach provides the simplicity and apparent confidence of interpreting model-based PDFs as if they were objective probabilities does not contradict the claim that either might lead to better decision-making.

A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds

DOE PAGES

Hagos, Samson; Feng, Zhe; Plant, Robert S.; ...

2018-02-20

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the nonequilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud-base mass flux, given a large-scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii)more » the cloud-base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud-base mass flux is a nonlinear function of convective cell area, the mass flux manifests a recharge-discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud-base mass flux variability under diurnally varying forcing. Finally, in addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to serve as a nonequilibrium closure formulations for spectral mass flux parameterizations.« less

A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds

NASA Astrophysics Data System (ADS)

Hagos, Samson; Feng, Zhe; Plant, Robert S.; Houze, Robert A.; Xiao, Heng

2018-02-01

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the nonequilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud-base mass flux, given a large-scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii) the cloud-base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud-base mass flux is a nonlinear function of convective cell area, the mass flux manifests a recharge-discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud-base mass flux variability under diurnally varying forcing. In addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to serve as a nonequilibrium closure formulations for spectral mass flux parameterizations.

A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds

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

Hagos, Samson; Feng, Zhe; Plant, Robert S.

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the nonequilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud-base mass flux, given a large-scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii)more » the cloud-base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud-base mass flux is a nonlinear function of convective cell area, the mass flux manifests a recharge-discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud-base mass flux variability under diurnally varying forcing. Finally, in addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to serve as a nonequilibrium closure formulations for spectral mass flux parameterizations.« less

Optimizing DNA assembly based on statistical language modelling.

PubMed

Fang, Gang; Zhang, Shemin; Dong, Yafei

2017-12-15

By successively assembling genetic parts such as BioBrick according to grammatical models, complex genetic constructs composed of dozens of functional blocks can be built. However, usually every category of genetic parts includes a few or many parts. With increasing quantity of genetic parts, the process of assembling more than a few sets of these parts can be expensive, time consuming and error prone. At the last step of assembling it is somewhat difficult to decide which part should be selected. Based on statistical language model, which is a probability distribution P(s) over strings S that attempts to reflect how frequently a string S occurs as a sentence, the most commonly used parts will be selected. Then, a dynamic programming algorithm was designed to figure out the solution of maximum probability. The algorithm optimizes the results of a genetic design based on a grammatical model and finds an optimal solution. In this way, redundant operations can be reduced and the time and cost required for conducting biological experiments can be minimized. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Effects of ignition location models on the burn patterns of simulated wildfires

USGS Publications Warehouse

Bar-Massada, A.; Syphard, A.D.; Hawbaker, T.J.; Stewart, S.I.; Radeloff, V.C.

2011-01-01

Fire simulation studies that use models such as FARSITE often assume that ignition locations are distributed randomly, because spatially explicit information about actual ignition locations are difficult to obtain. However, many studies show that the spatial distribution of ignition locations, whether human-caused or natural, is non-random. Thus, predictions from fire simulations based on random ignitions may be unrealistic. However, the extent to which the assumption of ignition location affects the predictions of fire simulation models has never been systematically explored. Our goal was to assess the difference in fire simulations that are based on random versus non-random ignition location patterns. We conducted four sets of 6000 FARSITE simulations for the Santa Monica Mountains in California to quantify the influence of random and non-random ignition locations and normal and extreme weather conditions on fire size distributions and spatial patterns of burn probability. Under extreme weather conditions, fires were significantly larger for non-random ignitions compared to random ignitions (mean area of 344.5 ha and 230.1 ha, respectively), but burn probability maps were highly correlated (r = 0.83). Under normal weather, random ignitions produced significantly larger fires than non-random ignitions (17.5 ha and 13.3 ha, respectively), and the spatial correlations between burn probability maps were not high (r = 0.54), though the difference in the average burn probability was small. The results of the study suggest that the location of ignitions used in fire simulation models may substantially influence the spatial predictions of fire spread patterns. However, the spatial bias introduced by using a random ignition location model may be minimized if the fire simulations are conducted under extreme weather conditions when fire spread is greatest. ?? 2010 Elsevier Ltd.

Large-scale monitoring of shorebird populations using count data and N-mixture models: Black Oystercatcher (Haematopus bachmani) surveys by land and sea

USGS Publications Warehouse

Lyons, James E.; Andrew, Royle J.; Thomas, Susan M.; Elliott-Smith, Elise; Evenson, Joseph R.; Kelly, Elizabeth G.; Milner, Ruth L.; Nysewander, David R.; Andres, Brad A.

2012-01-01

Large-scale monitoring of bird populations is often based on count data collected across spatial scales that may include multiple physiographic regions and habitat types. Monitoring at large spatial scales may require multiple survey platforms (e.g., from boats and land when monitoring coastal species) and multiple survey methods. It becomes especially important to explicitly account for detection probability when analyzing count data that have been collected using multiple survey platforms or methods. We evaluated a new analytical framework, N-mixture models, to estimate actual abundance while accounting for multiple detection biases. During May 2006, we made repeated counts of Black Oystercatchers (Haematopus bachmani) from boats in the Puget Sound area of Washington (n = 55 sites) and from land along the coast of Oregon (n = 56 sites). We used a Bayesian analysis of N-mixture models to (1) assess detection probability as a function of environmental and survey covariates and (2) estimate total Black Oystercatcher abundance during the breeding season in the two regions. Probability of detecting individuals during boat-based surveys was 0.75 (95% credible interval: 0.42–0.91) and was not influenced by tidal stage. Detection probability from surveys conducted on foot was 0.68 (0.39–0.90); the latter was not influenced by fog, wind, or number of observers but was ~35% lower during rain. The estimated population size was 321 birds (262–511) in Washington and 311 (276–382) in Oregon. N-mixture models provide a flexible framework for modeling count data and covariates in large-scale bird monitoring programs designed to understand population change.

Traffic Video Image Segmentation Model Based on Bayesian and Spatio-Temporal Markov Random Field

NASA Astrophysics Data System (ADS)

Zhou, Jun; Bao, Xu; Li, Dawei; Yin, Yongwen

2017-10-01

Traffic video image is a kind of dynamic image and its background and foreground is changed at any time, which results in the occlusion. In this case, using the general method is more difficult to get accurate image segmentation. A segmentation algorithm based on Bayesian and Spatio-Temporal Markov Random Field is put forward, which respectively build the energy function model of observation field and label field to motion sequence image with Markov property, then according to Bayesian' rule, use the interaction of label field and observation field, that is the relationship of label field’s prior probability and observation field’s likelihood probability, get the maximum posterior probability of label field’s estimation parameter, use the ICM model to extract the motion object, consequently the process of segmentation is finished. Finally, the segmentation methods of ST - MRF and the Bayesian combined with ST - MRF were analyzed. Experimental results: the segmentation time in Bayesian combined with ST-MRF algorithm is shorter than in ST-MRF, and the computing workload is small, especially in the heavy traffic dynamic scenes the method also can achieve better segmentation effect.

A spread willingness computing-based information dissemination model.

PubMed

Huang, Haojing; Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.

A Spread Willingness Computing-Based Information Dissemination Model

PubMed Central

Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network. PMID:25110738

Probability density function characterization for aggregated large-scale wind power based on Weibull mixtures

DOE PAGES

Gomez-Lazaro, Emilio; Bueso, Maria C.; Kessler, Mathieu; ...

2016-02-02

Here, the Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power datamore » are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.« less

Uncertainty Analysis and Parameter Estimation For Nearshore Hydrodynamic Models

NASA Astrophysics Data System (ADS)

Ardani, S.; Kaihatu, J. M.

2012-12-01

Numerical models represent deterministic approaches used for the relevant physical processes in the nearshore. Complexity of the physics of the model and uncertainty involved in the model inputs compel us to apply a stochastic approach to analyze the robustness of the model. The Bayesian inverse problem is one powerful way to estimate the important input model parameters (determined by apriori sensitivity analysis) and can be used for uncertainty analysis of the outputs. Bayesian techniques can be used to find the range of most probable parameters based on the probability of the observed data and the residual errors. In this study, the effect of input data involving lateral (Neumann) boundary conditions, bathymetry and off-shore wave conditions on nearshore numerical models are considered. Monte Carlo simulation is applied to a deterministic numerical model (the Delft3D modeling suite for coupled waves and flow) for the resulting uncertainty analysis of the outputs (wave height, flow velocity, mean sea level and etc.). Uncertainty analysis of outputs is performed by random sampling from the input probability distribution functions and running the model as required until convergence to the consistent results is achieved. The case study used in this analysis is the Duck94 experiment, which was conducted at the U.S. Army Field Research Facility at Duck, North Carolina, USA in the fall of 1994. The joint probability of model parameters relevant for the Duck94 experiments will be found using the Bayesian approach. We will further show that, by using Bayesian techniques to estimate the optimized model parameters as inputs and applying them for uncertainty analysis, we can obtain more consistent results than using the prior information for input data which means that the variation of the uncertain parameter will be decreased and the probability of the observed data will improve as well. Keywords: Monte Carlo Simulation, Delft3D, uncertainty analysis, Bayesian techniques, MCMC

Cost-effectiveness of external cephalic version for term breech presentation

PubMed Central

2010-01-01

Background External cephalic version (ECV) is recommended by the American College of Obstetricians and Gynecologists to convert a breech fetus to vertex position and reduce the need for cesarean delivery. The goal of this study was to determine the incremental cost-effectiveness ratio, from society's perspective, of ECV compared to scheduled cesarean for term breech presentation. Methods A computer-based decision model (TreeAge Pro 2008, Tree Age Software, Inc.) was developed for a hypothetical base case parturient presenting with a term singleton breech fetus with no contraindications for vaginal delivery. The model incorporated actual hospital costs (e.g., $8,023 for cesarean and $5,581 for vaginal delivery), utilities to quantify health-related quality of life, and probabilities based on analysis of published literature of successful ECV trial, spontaneous reversion, mode of delivery, and need for unanticipated emergency cesarean delivery. The primary endpoint was the incremental cost-effectiveness ratio in dollars per quality-adjusted year of life gained. A threshold of $50,000 per quality-adjusted life-years (QALY) was used to determine cost-effectiveness. Results The incremental cost-effectiveness of ECV, assuming a baseline 58% success rate, equaled $7,900/QALY. If the estimated probability of successful ECV is less than 32%, then ECV costs more to society and has poorer QALYs for the patient. However, as the probability of successful ECV was between 32% and 63%, ECV cost more than cesarean delivery but with greater associated QALY such that the cost-effectiveness ratio was less than $50,000/QALY. If the probability of successful ECV was greater than 63%, the computer modeling indicated that a trial of ECV is less costly and with better QALYs than a scheduled cesarean. The cost-effectiveness of a trial of ECV is most sensitive to its probability of success, and not to the probabilities of a cesarean after ECV, spontaneous reversion to breech, successful second ECV trial, or adverse outcome from emergency cesarean. Conclusions From society's perspective, ECV trial is cost-effective when compared to a scheduled cesarean for breech presentation provided the probability of successful ECV is > 32%. Improved algorithms are needed to more precisely estimate the likelihood that a patient will have a successful ECV. PMID:20092630

Probabilistic modelling of drought events in China via 2-dimensional joint copula

NASA Astrophysics Data System (ADS)

Ayantobo, Olusola O.; Li, Yi; Song, Songbai; Javed, Tehseen; Yao, Ning

2018-04-01

Probabilistic modelling of drought events is a significant aspect of water resources management and planning. In this study, popularly applied and several relatively new bivariate Archimedean copulas were employed to derive regional and spatial based copula models to appraise drought risk in mainland China over 1961-2013. Drought duration (Dd), severity (Ds), and peak (Dp), as indicated by Standardized Precipitation Evapotranspiration Index (SPEI), were extracted according to the run theory and fitted with suitable marginal distributions. The maximum likelihood estimation (MLE) and curve fitting method (CFM) were used to estimate the copula parameters of nineteen bivariate Archimedean copulas. Drought probabilities and return periods were analysed based on appropriate bivariate copula in sub-region I-VII and entire mainland China. The goodness-of-fit tests as indicated by the CFM showed that copula NN19 in sub-regions III, IV, V, VI and mainland China, NN20 in sub-region I and NN13 in sub-region VII are the best for modeling drought variables. Bivariate drought probability across mainland China is relatively high, and the highest drought probabilities are found mainly in the Northwestern and Southwestern China. Besides, the result also showed that different sub-regions might suffer varying drought risks. The drought risks as observed in Sub-region III, VI and VII, are significantly greater than other sub-regions. Higher probability of droughts of longer durations in the sub-regions also corresponds to shorter return periods with greater drought severity. These results may imply tremendous challenges for the water resources management in different sub-regions, particularly the Northwestern and Southwestern China.

Using Multi-Scenario Tsunami Modelling Results combined with Probabilistic Analyses to provide Hazard Information for the South-WestCoast of Indonesia

NASA Astrophysics Data System (ADS)

Zosseder, K.; Post, J.; Steinmetz, T.; Wegscheider, S.; Strunz, G.

2009-04-01

Indonesia is located at one of the most active geological subduction zones in the world. Following the most recent seaquakes and their subsequent tsunamis in December 2004 and July 2006 it is expected that also in the near future tsunamis are likely to occur due to increased tectonic tensions leading to abrupt vertical seafloor alterations after a century of relative tectonic silence. To face this devastating threat tsunami hazard maps are very important as base for evacuation planning and mitigation strategies. In terms of a tsunami impact the hazard assessment is mostly covered by numerical modelling because the model results normally offer the most precise database for a hazard analysis as they include spatially distributed data and their influence to the hydraulic dynamics. Generally a model result gives a probability for the intensity distribution of a tsunami at the coast (or run up) and the spatial distribution of the maximum inundation area depending on the location and magnitude of the tsunami source used. The boundary condition of the source used for the model is mostly chosen by a worst case approach. Hence the location and magnitude which are likely to occur and which are assumed to generate the worst impact are used to predict the impact at a specific area. But for a tsunami hazard assessment covering a large coastal area, as it is demanded in the GITEWS (German Indonesian Tsunami Early Warning System) project in which the present work is embedded, this approach is not practicable because a lot of tsunami sources can cause an impact at the coast and must be considered. Thus a multi-scenario tsunami model approach is developed to provide a reliable hazard assessment covering large areas. For the Indonesian Early Warning System many tsunami scenarios were modelled by the Alfred Wegener Institute (AWI) at different probable tsunami sources and with different magnitudes along the Sunda Trench. Every modelled scenario delivers the spatial distribution of the inundation for a specific area, the wave height at coast at this area and the estimated times of arrival (ETAs) of the waves, caused by one tsunamigenic source with a specific magnitude. These parameters from the several scenarios can overlap each other along the coast and must be combined to get one comprehensive hazard assessment for all possible future tsunamis at the region under observation. The simplest way to derive the inundation probability along the coast using the multiscenario approach is to overlay all scenario inundation results and to determine how often a point on land will be significantly inundated from the various scenarios. But this does not take into account that the used tsunamigenic sources for the modeled scenarios have different likelihoods of causing a tsunami. Hence a statistical analysis of historical data and geophysical investigation results based on numerical modelling results is added to the hazard assessment, which clearly improves the significance of the hazard assessment. For this purpose the present method is developed and contains a complex logical combination of the diverse probabilities assessed like probability of occurrence for different earthquake magnitudes at different localities, probability of occurrence for a specific wave height at the coast and the probability for every point on land likely to get hit by a tsunami. The values are combined by a logical tree technique and quantified by statistical analysis of historical data and of the tsunami modelling results as mentioned before. This results in a tsunami inundation probability map covering the South West Coast of Indonesia which nevertheless shows a significant spatial diversity offering a good base for evacuation planning and mitigation strategies. Keywords: tsunami hazard assessment, tsunami modelling, probabilistic analysis, early warning

Burst wait time simulation of CALIBAN reactor at delayed super-critical state

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

Humbert, P.; Authier, N.; Richard, B.

2012-07-01

In the past, the super prompt critical wait time probability distribution was measured on CALIBAN fast burst reactor [4]. Afterwards, these experiments were simulated with a very good agreement by solving the non-extinction probability equation [5]. Recently, the burst wait time probability distribution has been measured at CEA-Valduc on CALIBAN at different delayed super-critical states [6]. However, in the delayed super-critical case the non-extinction probability does not give access to the wait time distribution. In this case it is necessary to compute the time dependent evolution of the full neutron count number probability distribution. In this paper we present themore » point model deterministic method used to calculate the probability distribution of the wait time before a prescribed count level taking into account prompt neutrons and delayed neutron precursors. This method is based on the solution of the time dependent adjoint Kolmogorov master equations for the number of detections using the generating function methodology [8,9,10] and inverse discrete Fourier transforms. The obtained results are then compared to the measurements and Monte-Carlo calculations based on the algorithm presented in [7]. (authors)« less

Small-target leak detection for a closed vessel via infrared image sequences

NASA Astrophysics Data System (ADS)

Zhao, Ling; Yang, Hongjiu

2017-03-01

This paper focus on a leak diagnosis and localization method based on infrared image sequences. Some problems on high probability of false warning and negative affect for marginal information are solved by leak detection. An experimental model is established for leak diagnosis and localization on infrared image sequences. The differential background prediction is presented to eliminate the negative affect of marginal information on test vessel based on a kernel regression method. A pipeline filter based on layering voting is designed to reduce probability of leak point false warning. A synthesize leak diagnosis and localization algorithm is proposed based on infrared image sequences. The effectiveness and potential are shown for developed techniques through experimental results.

[Research on engine remaining useful life prediction based on oil spectrum analysis and particle filtering].

PubMed

Sun, Lei; Jia, Yun-xian; Cai, Li-ying; Lin, Guo-yu; Zhao, Jin-song

2013-09-01

The spectrometric oil analysis(SOA) is an important technique for machine state monitoring, fault diagnosis and prognosis, and SOA based remaining useful life(RUL) prediction has an advantage of finding out the optimal maintenance strategy for machine system. Because the complexity of machine system, its health state degradation process can't be simply characterized by linear model, while particle filtering(PF) possesses obvious advantages over traditional Kalman filtering for dealing nonlinear and non-Gaussian system, the PF approach was applied to state forecasting by SOA, and the RUL prediction technique based on SOA and PF algorithm is proposed. In the prediction model, according to the estimating result of system's posterior probability, its prior probability distribution is realized, and the multi-step ahead prediction model based on PF algorithm is established. Finally, the practical SOA data of some engine was analyzed and forecasted by the above method, and the forecasting result was compared with that of traditional Kalman filtering method. The result fully shows the superiority and effectivity of the

Common IED exploitation target set ontology

NASA Astrophysics Data System (ADS)

Russomanno, David J.; Qualls, Joseph; Wowczuk, Zenovy; Franken, Paul; Robinson, William

2010-04-01

The Common IED Exploitation Target Set (CIEDETS) ontology provides a comprehensive semantic data model for capturing knowledge about sensors, platforms, missions, environments, and other aspects of systems under test. The ontology also includes representative IEDs; modeled as explosives, camouflage, concealment objects, and other background objects, which comprise an overall threat scene. The ontology is represented using the Web Ontology Language and the SPARQL Protocol and RDF Query Language, which ensures portability of the acquired knowledge base across applications. The resulting knowledge base is a component of the CIEDETS application, which is intended to support the end user sensor test and evaluation community. CIEDETS associates a system under test to a subset of cataloged threats based on the probability that the system will detect the threat. The associations between systems under test, threats, and the detection probabilities are established based on a hybrid reasoning strategy, which applies a combination of heuristics and simplified modeling techniques. Besides supporting the CIEDETS application, which is focused on efficient and consistent system testing, the ontology can be leveraged in a myriad of other applications, including serving as a knowledge source for mission planning tools.

Modelling spruce bark beetle infestation probability

Treesearch

Paulius Zolubas; Jose Negron; A. Steven Munson

2009-01-01

Spruce bark beetle (Ips typographus L.) risk model, based on pure Norway spruce (Picea abies Karst.) stand characteristics in experimental and control plots was developed using classification and regression tree statistical technique under endemic pest population density. The most significant variable in spruce bark beetle...

Stochastic population dynamic models as probability networks

Treesearch

M.E. and D.C. Lee Borsuk

2009-01-01

The dynamics of a population and its response to environmental change depend on the balance of birth, death and age-at-maturity, and there have been many attempts to mathematically model populations based on these characteristics. Historically, most of these models were deterministic, meaning that the results were strictly determined by the equations of the model and...

A methodology for the transfer of probabilities between accident severity categories

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

Whitlow, J. D.; Neuhauser, K. S.

A methodology has been developed which allows the accident probabilities associated with one accident-severity category scheme to be transferred to another severity category scheme. The methodology requires that the schemes use a common set of parameters to define the categories. The transfer of accident probabilities is based on the relationships between probability of occurrence and each of the parameters used to define the categories. Because of the lack of historical data describing accident environments in engineering terms, these relationships may be difficult to obtain directly for some parameters. Numerical models or experienced judgement are often needed to obtain the relationships.more » These relationships, even if they are not exact, allow the accident probability associated with any severity category to be distributed within that category in a manner consistent with accident experience, which in turn will allow the accident probability to be appropriately transferred to a different category scheme.« less

Bayesian modeling and inference for diagnostic accuracy and probability of disease based on multiple diagnostic biomarkers with and without a perfect reference standard.

PubMed

Jafarzadeh, S Reza; Johnson, Wesley O; Gardner, Ian A

2016-03-15

The area under the receiver operating characteristic (ROC) curve (AUC) is used as a performance metric for quantitative tests. Although multiple biomarkers may be available for diagnostic or screening purposes, diagnostic accuracy is often assessed individually rather than in combination. In this paper, we consider the interesting problem of combining multiple biomarkers for use in a single diagnostic criterion with the goal of improving the diagnostic accuracy above that of an individual biomarker. The diagnostic criterion created from multiple biomarkers is based on the predictive probability of disease, conditional on given multiple biomarker outcomes. If the computed predictive probability exceeds a specified cutoff, the corresponding subject is allocated as 'diseased'. This defines a standard diagnostic criterion that has its own ROC curve, namely, the combined ROC (cROC). The AUC metric for cROC, namely, the combined AUC (cAUC), is used to compare the predictive criterion based on multiple biomarkers to one based on fewer biomarkers. A multivariate random-effects model is proposed for modeling multiple normally distributed dependent scores. Bayesian methods for estimating ROC curves and corresponding (marginal) AUCs are developed when a perfect reference standard is not available. In addition, cAUCs are computed to compare the accuracy of different combinations of biomarkers for diagnosis. The methods are evaluated using simulations and are applied to data for Johne's disease (paratuberculosis) in cattle. Copyright © 2015 John Wiley & Sons, Ltd.

Functional mechanisms of probabilistic inference in feature- and space-based attentional systems.

PubMed

Dombert, Pascasie L; Kuhns, Anna; Mengotti, Paola; Fink, Gereon R; Vossel, Simone

2016-11-15

Humans flexibly attend to features or locations and these processes are influenced by the probability of sensory events. We combined computational modeling of response times with fMRI to compare the functional correlates of (re-)orienting, and the modulation by probabilistic inference in spatial and feature-based attention systems. Twenty-four volunteers performed two task versions with spatial or color cues. Percentage of cue validity changed unpredictably. A hierarchical Bayesian model was used to derive trial-wise estimates of probability-dependent attention, entering the fMRI analysis as parametric regressors. Attentional orienting activated a dorsal frontoparietal network in both tasks, without significant parametric modulation. Spatially invalid trials activated a bilateral frontoparietal network and the precuneus, while invalid feature trials activated the left intraparietal sulcus (IPS). Probability-dependent attention modulated activity in the precuneus, left posterior IPS, middle occipital gyrus, and right temporoparietal junction for spatial attention, and in the left anterior IPS for feature-based and spatial attention. These findings provide novel insights into the generality and specificity of the functional basis of attentional control. They suggest that probabilistic inference can distinctively affect each attentional subsystem, but that there is an overlap in the left IPS, which responds to both spatial and feature-based expectancy violations. Copyright © 2016 Elsevier Inc. All rights reserved.

Calibrating random forests for probability estimation.

PubMed

Dankowski, Theresa; Ziegler, Andreas

2016-09-30

Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating random forests for probability estimation. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so-called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest is first translated to logistic regression models. These are, in turn, used for re-calibration. The two updating strategies were compared in a simulation study and are illustrated with data from the German Stroke Study Collaboration. In most simulation scenarios, both methods led to similar improvements. In the simulation scenario in which the stricter assumptions of Elkan's method were not met, the logistic regression-based re-calibration approach for random forests outperformed Elkan's method. It also performed better on the stroke data than Elkan's method. The strength of Elkan's method is its general applicability to any probability machine. However, if the strict assumptions underlying this approach are not met, the logistic regression-based approach is preferable for updating random forests for probability estimation. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Exploiting Outage and Error Probability of Cooperative Incremental Relaying in Underwater Wireless Sensor Networks

PubMed Central

Nasir, Hina; Javaid, Nadeem; Sher, Muhammad; Qasim, Umar; Khan, Zahoor Ali; Alrajeh, Nabil; Niaz, Iftikhar Azim

2016-01-01

This paper embeds a bi-fold contribution for Underwater Wireless Sensor Networks (UWSNs); performance analysis of incremental relaying in terms of outage and error probability, and based on the analysis proposition of two new cooperative routing protocols. Subject to the first contribution, a three step procedure is carried out; a system model is presented, the number of available relays are determined, and based on cooperative incremental retransmission methodology, closed-form expressions for outage and error probability are derived. Subject to the second contribution, Adaptive Cooperation in Energy (ACE) efficient depth based routing and Enhanced-ACE (E-ACE) are presented. In the proposed model, feedback mechanism indicates success or failure of data transmission. If direct transmission is successful, there is no need for relaying by cooperative relay nodes. In case of failure, all the available relays retransmit the data one by one till the desired signal quality is achieved at destination. Simulation results show that the ACE and E-ACE significantly improves network performance, i.e., throughput, when compared with other incremental relaying protocols like Cooperative Automatic Repeat reQuest (CARQ). E-ACE and ACE achieve 69% and 63% more throughput respectively as compared to CARQ in hard underwater environment. PMID:27420061

Reliability Evaluation of Machine Center Components Based on Cascading Failure Analysis

NASA Astrophysics Data System (ADS)

Zhang, Ying-Zhi; Liu, Jin-Tong; Shen, Gui-Xiang; Long, Zhe; Sun, Shu-Guang

2017-07-01

In order to rectify the problems that the component reliability model exhibits deviation, and the evaluation result is low due to the overlook of failure propagation in traditional reliability evaluation of machine center components, a new reliability evaluation method based on cascading failure analysis and the failure influenced degree assessment is proposed. A direct graph model of cascading failure among components is established according to cascading failure mechanism analysis and graph theory. The failure influenced degrees of the system components are assessed by the adjacency matrix and its transposition, combined with the Pagerank algorithm. Based on the comprehensive failure probability function and total probability formula, the inherent failure probability function is determined to realize the reliability evaluation of the system components. Finally, the method is applied to a machine center, it shows the following: 1) The reliability evaluation values of the proposed method are at least 2.5% higher than those of the traditional method; 2) The difference between the comprehensive and inherent reliability of the system component presents a positive correlation with the failure influenced degree of the system component, which provides a theoretical basis for reliability allocation of machine center system.

Estimation and prediction of maximum daily rainfall at Sagar Island using best fit probability models

NASA Astrophysics Data System (ADS)

Mandal, S.; Choudhury, B. U.

2015-07-01

Sagar Island, setting on the continental shelf of Bay of Bengal, is one of the most vulnerable deltas to the occurrence of extreme rainfall-driven climatic hazards. Information on probability of occurrence of maximum daily rainfall will be useful in devising risk management for sustaining rainfed agrarian economy vis-a-vis food and livelihood security. Using six probability distribution models and long-term (1982-2010) daily rainfall data, we studied the probability of occurrence of annual, seasonal and monthly maximum daily rainfall (MDR) in the island. To select the best fit distribution models for annual, seasonal and monthly time series based on maximum rank with minimum value of test statistics, three statistical goodness of fit tests, viz. Kolmogorove-Smirnov test (K-S), Anderson Darling test ( A 2 ) and Chi-Square test ( X 2) were employed. The fourth probability distribution was identified from the highest overall score obtained from the three goodness of fit tests. Results revealed that normal probability distribution was best fitted for annual, post-monsoon and summer seasons MDR, while Lognormal, Weibull and Pearson 5 were best fitted for pre-monsoon, monsoon and winter seasons, respectively. The estimated annual MDR were 50, 69, 86, 106 and 114 mm for return periods of 2, 5, 10, 20 and 25 years, respectively. The probability of getting an annual MDR of >50, >100, >150, >200 and >250 mm were estimated as 99, 85, 40, 12 and 03 % level of exceedance, respectively. The monsoon, summer and winter seasons exhibited comparatively higher probabilities (78 to 85 %) for MDR of >100 mm and moderate probabilities (37 to 46 %) for >150 mm. For different recurrence intervals, the percent probability of MDR varied widely across intra- and inter-annual periods. In the island, rainfall anomaly can pose a climatic threat to the sustainability of agricultural production and thus needs adequate adaptation and mitigation measures.

On the quantum-channel capacity for orbital angular momentum-based free-space optical communications.

PubMed

Zhang, Yequn; Djordjevic, Ivan B; Gao, Xin

2012-08-01

Inspired by recent demonstrations of orbital angular momentum-(OAM)-based single-photon communications, we propose two quantum-channel models: (i) the multidimensional quantum-key distribution model and (ii) the quantum teleportation model. Both models employ operator-sum representation for Kraus operators derived from OAM eigenkets transition probabilities. These models are highly important for future development of quantum-error correction schemes to extend the transmission distance and improve date rates of OAM quantum communications. By using these models, we calculate corresponding quantum-channel capacities in the presence of atmospheric turbulence.

Explanation of asymmetric dynamics of human water consumption in arid regions: prospect theory versus expected utility theory

NASA Astrophysics Data System (ADS)

Tian, F.; Lu, Y.

2017-12-01

Based on socioeconomic and hydrological data in three arid inland basins and error analysis, the dynamics of human water consumption (HWC) are analyzed to be asymmetric, i.e., HWC increase rapidly in wet periods while maintain or decrease slightly in dry periods. Besides the qualitative analysis that in wet periods great water availability inspires HWC to grow fast but the now expanded economy is managed to sustain by over-exploitation in dry periods, two quantitative models are established and tested, based on expected utility theory (EUT) and prospect theory (PT) respectively. EUT states that humans make decisions based on the total expected utility, namely the sum of utility function multiplied by probability of each result, while PT states that the utility function is defined over gains and losses separately, and probability should be replaced by probability weighting function.

Quantum decision-maker theory and simulation

NASA Astrophysics Data System (ADS)

Zak, Michail; Meyers, Ronald E.; Deacon, Keith S.

2000-07-01

A quantum device simulating the human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and of classical neural nets describing the evolution of probabilities of these processes which represent the mental dynamics. The autonomy of the decision making process is achieved by a feedback from the mental to motor dynamics which changes the stochastic matrix based upon the probability distribution. This feedback replaces unavailable external information by an internal knowledge- base stored in the mental model in the form of probability distributions. As a result, the coupled motor-mental dynamics is described by a nonlinear version of Markov chains which can decrease entropy without an external source of information. Applications to common sense based decisions as well as to evolutionary games are discussed. An example exhibiting self-organization is computed using quantum computer simulation. Force on force and mutual aircraft engagements using the quantum decision maker dynamics are considered.

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao

2016-01-01

Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm. PMID:27618902

Optical detection of chemical warfare agents and toxic industrial chemicals

NASA Astrophysics Data System (ADS)

Webber, Michael E.; Pushkarsky, Michael B.; Patel, C. Kumar N.

2004-12-01

We present an analytical model evaluating the suitability of optical absorption based spectroscopic techniques for detection of chemical warfare agents (CWAs) and toxic industrial chemicals (TICs) in ambient air. The sensor performance is modeled by simulating absorption spectra of a sample containing both the target and multitude of interfering species as well as an appropriate stochastic noise and determining the target concentrations from the simulated spectra via a least square fit (LSF) algorithm. The distribution of the LSF target concentrations determines the sensor sensitivity, probability of false positives (PFP) and probability of false negatives (PFN). The model was applied to CO2 laser based photoacosutic (L-PAS) CWA sensor and predicted single digit ppb sensitivity with very low PFP rates in the presence of significant amount of interferences. This approach will be useful for assessing sensor performance by developers and users alike; it also provides methodology for inter-comparison of different sensing technologies.

Estimation of vegetation cover at subpixel resolution using LANDSAT data

NASA Technical Reports Server (NTRS)

Jasinski, Michael F.; Eagleson, Peter S.

1986-01-01

The present report summarizes the various approaches relevant to estimating canopy cover at subpixel resolution. The approaches are based on physical models of radiative transfer in non-homogeneous canopies and on empirical methods. The effects of vegetation shadows and topography are examined. Simple versions of the model are tested, using the Taos, New Mexico Study Area database. Emphasis has been placed on using relatively simple models requiring only one or two bands. Although most methods require some degree of ground truth, a two-band method is investigated whereby the percent cover can be estimated without ground truth by examining the limits of the data space. Future work is proposed which will incorporate additional surface parameters into the canopy cover algorithm, such as topography, leaf area, or shadows. The method involves deriving a probability density function for the percent canopy cover based on the joint probability density function of the observed radiances.

Simulating Chemical Kinetics Without Differential Equations: A Quantitative Theory Based on Chemical Pathways.

PubMed

Bai, Shirong; Skodje, Rex T

2017-08-17

A new approach is presented for simulating the time-evolution of chemically reactive systems. This method provides an alternative to conventional modeling of mass-action kinetics that involves solving differential equations for the species concentrations. The method presented here avoids the need to solve the rate equations by switching to a representation based on chemical pathways. In the Sum Over Histories Representation (or SOHR) method, any time-dependent kinetic observable, such as concentration, is written as a linear combination of probabilities for chemical pathways leading to a desired outcome. In this work, an iterative method is introduced that allows the time-dependent pathway probabilities to be generated from a knowledge of the elementary rate coefficients, thus avoiding the pitfalls involved in solving the differential equations of kinetics. The method is successfully applied to the model Lotka-Volterra system and to a realistic H 2 combustion model.

Evaluation of the safety performance of highway alignments based on fault tree analysis and safety boundaries.

PubMed

Chen, Yikai; Wang, Kai; Xu, Chengcheng; Shi, Qin; He, Jie; Li, Peiqing; Shi, Ting

2018-05-19

To overcome the limitations of previous highway alignment safety evaluation methods, this article presents a highway alignment safety evaluation method based on fault tree analysis (FTA) and the characteristics of vehicle safety boundaries, within the framework of dynamic modeling of the driver-vehicle-road system. Approaches for categorizing the vehicle failure modes while driving on highways and the corresponding safety boundaries were comprehensively investigated based on vehicle system dynamics theory. Then, an overall crash probability model was formulated based on FTA considering the risks of 3 failure modes: losing steering capability, losing track-holding capability, and rear-end collision. The proposed method was implemented on a highway segment between Bengbu and Nanjing in China. A driver-vehicle-road multibody dynamics model was developed based on the 3D alignments of the Bengbu to Nanjing section of Ning-Luo expressway using Carsim, and the dynamics indices, such as sideslip angle and, yaw rate were obtained. Then, the average crash probability of each road section was calculated with a fixed-length method. Finally, the average crash probability was validated against the crash frequency per kilometer to demonstrate the accuracy of the proposed method. The results of the regression analysis and correlation analysis indicated good consistency between the results of the safety evaluation and the crash data and that it outperformed the safety evaluation methods used in previous studies. The proposed method has the potential to be used in practical engineering applications to identify crash-prone locations and alignment deficiencies on highways in the planning and design phases, as well as those in service.

Binomial probability distribution model-based protein identification algorithm for tandem mass spectrometry utilizing peak intensity information.

PubMed

Xiao, Chuan-Le; Chen, Xiao-Zhou; Du, Yang-Li; Sun, Xuesong; Zhang, Gong; He, Qing-Yu

2013-01-04

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/ .

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

PubMed

Duan, Chong; Kallehauge, Jesper F; Pérez-Torres, Carlos J; Bretthorst, G Larry; Beeman, Scott C; Tanderup, Kari; Ackerman, Joseph J H; Garbow, Joel R

2018-02-01

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

Predicting the dynamics of ascospore maturation of Venturia pirina based on environmental factors.

PubMed

Rossi, V; Salinari, F; Pattori, E; Giosuè, S; Bugiani, R

2009-04-01

Airborne ascospores of Venturia pirina were trapped at two sites in northern Italy in 2002 to 2008. The cumulative proportion of ascospores trapped at each discharge was regressed against the physiological time. The best fit (R(2) = 0.90, standard error of estimates [SEest] = 0.11) was obtained using a Gompertz equation and the degree-days (>0 degrees C) accumulated after the day on which the first ascospore of the season was trapped (biofix day), but only for the days with > or =0.2 mm rain or < or =4 hPa vapor pressure deficit (DDwet). This Italian model performed better than the models developed in Oregon, United States (R(2) = 0.69, SEest = 0.16) or Victoria, Australia (R(2) = 0.74, SEest = 0.18), which consider only the effect of temperature. When the Italian model was evaluated against data not used in its elaboration, it accurately predicted ascospore maturation (R(2) = 0.92, SEest = 0.10). A logistic regression model was also developed to estimate the biofix for initiating the accumulation of degree-days (biofix model). The probability of the first ascospore discharge of the season increased as DDwet (calculated from 1 January) increased. Based on this model, there is low probability of the first ascospore discharge when DDwet < or =268.5 (P = 0.03) and high probability (P = 0.83) of discharge on the first day with >0.2 mm rain after such a DDwet threshold.

The STOP-Bang Equivalent Model and Prediction of Severity of Obstructive Sleep Apnea: Relation to Polysomnographic Measurements of the Apnea/Hypopnea Index

PubMed Central

Farney, Robert J.; Walker, Brandon S.; Farney, Robert M.; Snow, Gregory L.; Walker, James M.

2011-01-01

Background: Various models and questionnaires have been developed for screening specific populations for obstructive sleep apnea (OSA) as defined by the apnea/hypopnea index (AHI); however, almost every method is based upon dichotomizing a population, and none function ideally. We evaluated the possibility of using the STOP-Bang model (SBM) to classify severity of OSA into 4 categories ranging from none to severe. Methods: Anthropomorphic data and the presence of snoring, tiredness/sleepiness, observed apneas, and hypertension were collected from 1426 patients who underwent diagnostic polysomnography. Questionnaire data for each patient was converted to the STOP-Bang equivalent with an ordinal rating of 0 to 8. Proportional odds logistic regression analysis was conducted to predict severity of sleep apnea based upon the AHI: none (AHI < 5/h), mild (AHI ≥ 5 to < 15/h), moderate (≥ 15 to < 30/h), and severe (AHI ≥ 30/h). Results: Linear, curvilinear, and weighted models (R2 = 0.245, 0.251, and 0.269, respectively) were developed that predicted AHI severity. The linear model showed a progressive increase in the probability of severe (4.4% to 81.9%) and progressive decrease in the probability of none (52.5% to 1.1%). The probability of mild or moderate OSA initially increased from 32.9% and 10.3% respectively (SBM score 0) to 39.3% (SBM score 2) and 31.8% (SBM score 4), after which there was a progressive decrease in probabilities as more patients fell into the severe category. Conclusions: The STOP-Bang model may be useful to categorize OSA severity, triage patients for diagnostic evaluation or exclude from harm. Citation: Farney RJ; Walker BS; Farney RM; Snow GL; Walker JM. The STOP-Bang equivalent model and prediction of severity of obstructive sleep apnea: relation to polysomnographic measurements of the apnea/hypopnea index. J Clin Sleep Med 2011;7(5):459-465. PMID:22003340

Watershed Dynamics, with focus on connectivity index and management of water related impacts on road infrastructure

NASA Astrophysics Data System (ADS)

Kalantari, Z.

2015-12-01

In Sweden, spatially explicit approaches have been applied in various disciplines such as landslide modelling based on soil type data and flood risk modelling for large rivers. Regarding flood mapping, most previous studies have focused on complex hydrological modelling on a small scale whereas just a few studies have used a robust GIS-based approach integrating most physical catchment descriptor (PCD) aspects on a larger scale. This study was built on a conceptual framework for looking at SedInConnect model, topography, land use, soil data and other PCDs and climate change in an integrated way to pave the way for more integrated policy making. The aim of the present study was to develop methodology for predicting the spatial probability of flooding on a general large scale. This framework can provide a region with an effective tool to inform a broad range of watershed planning activities within a region. Regional planners, decision-makers, etc. can utilize this tool to identify the most vulnerable points in a watershed and along roads to plan for interventions and actions to alter impacts of high flows and other extreme weather events on roads construction. The application of the model over a large scale can give a realistic spatial characterization of sediment connectivity for the optimal management of debris flow to road structures. The ability of the model to capture flooding probability was determined for different watersheds in central Sweden. Using data from this initial investigation, a method to subtract spatial data for multiple catchments and to produce soft data for statistical analysis was developed. It allowed flood probability to be predicted from spatially sparse data without compromising the significant hydrological features on the landscape. This in turn allowed objective quantification of the probability of floods at the field scale for future model development and watershed management.

Spatial Distribution and Conservation of Speckled Hind and Warsaw Grouper in the Atlantic Ocean off the Southeastern U.S.

PubMed Central

Farmer, Nicholas A.; Karnauskas, Mandy

2013-01-01

There is broad interest in the development of efficient marine protected areas (MPAs) to reduce bycatch and end overfishing of speckled hind (Epinephelus drummondhayi) and warsaw grouper (Hyporthodus nigritus) in the Atlantic Ocean off the southeastern U.S. We assimilated decades of data from many fishery-dependent, fishery-independent, and anecdotal sources to describe the spatial distribution of these data limited stocks. A spatial classification model was developed to categorize depth-grids based on the distribution of speckled hind and warsaw grouper point observations and identified benthic habitats. Logistic regression analysis was used to develop a quantitative model to predict the spatial distribution of speckled hind and warsaw grouper as a function of depth, latitude, and habitat. Models, controlling for sampling gear effects, were selected based on AIC and 10-fold cross validation. The best-fitting model for warsaw grouper included latitude and depth to explain 10.8% of the variability in probability of detection, with a false prediction rate of 28–33%. The best-fitting model for speckled hind, per cross-validation, included latitude and depth to explain 36.8% of the variability in probability of detection, with a false prediction rate of 25–27%. The best-fitting speckled hind model, per AIC, also included habitat, but had false prediction rates up to 36%. Speckled hind and warsaw grouper habitats followed a shelf-edge hardbottom ridge from North Carolina to southeast Florida, with speckled hind more common to the north and warsaw grouper more common to the south. The proportion of habitat classifications and model-estimated stock contained within established and proposed MPAs was computed. Existing MPAs covered 10% of probable shelf-edge habitats for speckled hind and warsaw grouper, protecting 3–8% of speckled hind and 8% of warsaw grouper stocks. Proposed MPAs could add 24% more probable shelf-edge habitat, and protect an additional 14–29% of speckled hind and 20% of warsaw grouper stocks. PMID:24260126

A predictive model for floating leaf vegetation in the St. Louis River Estuary

EPA Science Inventory

In July 2014, USEPA staff was asked by MPCA to develop a predictive model for floating leaf vegetation (FLV) in the St. Louis River Estuary (SLRE). The existing model (Host et al. 2012) greatly overpredicts FLV in St. Louis Bay probably because it was based on a limited number of...

Probability of pregnancy after sterilization: a comparison of hysteroscopic versus laparoscopic sterilization.

PubMed

Gariepy, Aileen M; Creinin, Mitchell D; Smith, Kenneth J; Xu, Xiao

2014-08-01

To compare the expected probability of pregnancy after hysteroscopic versus laparoscopic sterilization based on available data using decision analysis. We developed an evidence-based Markov model to estimate the probability of pregnancy over 10 years after three different female sterilization procedures: hysteroscopic, laparoscopic silicone rubber band application and laparoscopic bipolar coagulation. Parameter estimates for procedure success, probability of completing follow-up testing and risk of pregnancy after different sterilization procedures were obtained from published sources. In the base case analysis at all points in time after the sterilization procedure, the initial and cumulative risk of pregnancy after sterilization is higher in women opting for hysteroscopic than either laparoscopic band or bipolar sterilization. The expected pregnancy rates per 1000 women at 1 year are 57, 7 and 3 for hysteroscopic sterilization, laparoscopic silicone rubber band application and laparoscopic bipolar coagulation, respectively. At 10 years, the cumulative pregnancy rates per 1000 women are 96, 24 and 30, respectively. Sensitivity analyses suggest that the three procedures would have an equivalent pregnancy risk of approximately 80 per 1000 women at 10 years if the probability of successful laparoscopic (band or bipolar) sterilization drops below 90% and successful coil placement on first hysteroscopic attempt increases to 98% or if the probability of undergoing a hysterosalpingogram increases to 100%. Based on available data, the expected population risk of pregnancy is higher after hysteroscopic than laparoscopic sterilization. Consistent with existing contraceptive classification, future characterization of hysteroscopic sterilization should distinguish "perfect" and "typical" use failure rates. Pregnancy probability at 1 year and over 10 years is expected to be higher in women having hysteroscopic as compared to laparoscopic sterilization. Copyright © 2014 Elsevier Inc. All rights reserved.

Praedicere Possumus: An Italian web-based application for predictive microbiology to ensure food safety.

PubMed

Polese, Pierluigi; Torre, Manuela Del; Stecchini, Mara Lucia

2018-03-31

The use of predictive modelling tools, which mainly describe the response of microorganisms to a particular set of environmental conditions, may contribute to a better understanding of microbial behaviour in foods. In this paper, a tertiary model, in the form of a readily available and userfriendly web-based application Praedicere Possumus (PP) is presented with research examples from our laboratories. Through the PP application, users have access to different modules, which apply a set of published models considered reliable for determining the compliance of a food product with EU safety criteria and for optimising processing throughout the identification of critical control points. The application pivots around a growth/no-growth boundary model, coupled with a growth model, and includes thermal and non-thermal inactivation models. Integrated functionalities, such as the fractional contribution of each inhibitory factor to growth probability (f) and the time evolution of the growth probability (P t ), have also been included. The PP application is expected to assist food industry and food safety authorities in their common commitment towards the improvement of food safety.

A capture-recapture survival analysis model for radio-tagged animals

USGS Publications Warehouse

Pollock, K.H.; Bunck, C.M.; Winterstein, S.R.; Chen, C.-L.; North, P.M.; Nichols, J.D.

1995-01-01

In recent years, survival analysis of radio-tagged animals has developed using methods based on the Kaplan-Meier method used in medical and engineering applications (Pollock et al., 1989a,b). An important assumption of this approach is that all tagged animals with a functioning radio can be relocated at each sampling time with probability 1. This assumption may not always be reasonable in practice. In this paper, we show how a general capture-recapture model can be derived which allows for some probability (less than one) for animals to be relocated. This model is not simply a Jolly-Seber model because it is possible to relocate both dead and live animals, unlike when traditional tagging is used. The model can also be viewed as a generalization of the Kaplan-Meier procedure, thus linking the Jolly-Seber and Kaplan-Meier approaches to survival estimation. We present maximum likelihood estimators and discuss testing between submodels. We also discuss model assumptions and their validity in practice. An example is presented based on canvasback data collected by G. M. Haramis of Patuxent Wildlife Research Center, Laurel, Maryland, USA.

Efficient Posterior Probability Mapping Using Savage-Dickey Ratios

PubMed Central

Penny, William D.; Ridgway, Gerard R.

2013-01-01

Statistical Parametric Mapping (SPM) is the dominant paradigm for mass-univariate analysis of neuroimaging data. More recently, a Bayesian approach termed Posterior Probability Mapping (PPM) has been proposed as an alternative. PPM offers two advantages: (i) inferences can be made about effect size thus lending a precise physiological meaning to activated regions, (ii) regions can be declared inactive. This latter facility is most parsimoniously provided by PPMs based on Bayesian model comparisons. To date these comparisons have been implemented by an Independent Model Optimization (IMO) procedure which separately fits null and alternative models. This paper proposes a more computationally efficient procedure based on Savage-Dickey approximations to the Bayes factor, and Taylor-series approximations to the voxel-wise posterior covariance matrices. Simulations show the accuracy of this Savage-Dickey-Taylor (SDT) method to be comparable to that of IMO. Results on fMRI data show excellent agreement between SDT and IMO for second-level models, and reasonable agreement for first-level models. This Savage-Dickey test is a Bayesian analogue of the classical SPM-F and allows users to implement model comparison in a truly interactive manner. PMID:23533640

Bayesian-network-based safety risk assessment for steel construction projects.

PubMed

Leu, Sou-Sen; Chang, Ching-Miao

2013-05-01

There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites. Copyright © 2013 Elsevier Ltd. All rights reserved.

The effects of vent location, event scale and time forecasts on pyroclastic density current hazard maps at Campi Flegrei caldera (Italy)

NASA Astrophysics Data System (ADS)

Bevilacqua, Andrea; Neri, Augusto; Bisson, Marina; Esposti Ongaro, Tomaso; Flandoli, Franco; Isaia, Roberto; Rosi, Mauro; Vitale, Stefano

2017-09-01

This study presents a new method for producing long-term hazard maps for pyroclastic density currents (PDC) originating at Campi Flegrei caldera. Such method is based on a doubly stochastic approach and is able to combine the uncertainty assessments on the spatial location of the volcanic vent, the size of the flow and the expected time of such an event. The results are obtained by using a Monte Carlo approach and adopting a simplified invasion model based on the box model integral approximation. Temporal assessments are modelled through a Cox-type process including self-excitement effects, based on the eruptive record of the last 15 kyr. Mean and percentile maps of PDC invasion probability are produced, exploring their sensitivity to some sources of uncertainty and to the effects of the dependence between PDC scales and the caldera sector where they originated. Conditional maps representative of PDC originating inside limited zones of the caldera, or of PDC with a limited range of scales are also produced. Finally, the effect of assuming different time windows for the hazard estimates is explored, also including the potential occurrence of a sequence of multiple events. Assuming that the last eruption of Monte Nuovo (A.D. 1538) marked the beginning of a new epoch of activity similar to the previous ones, results of the statistical analysis indicate a mean probability of PDC invasion above 5% in the next 50 years on almost the entire caldera (with a probability peak of 25% in the central part of the caldera). In contrast, probability values reduce by a factor of about 3 if the entire eruptive record is considered over the last 15 kyr, i.e. including both eruptive epochs and quiescent periods.

Statistical analysis of the induced Basel 2006 earthquake sequence: introducing a probability-based monitoring approach for Enhanced Geothermal Systems

NASA Astrophysics Data System (ADS)

Bachmann, C. E.; Wiemer, S.; Woessner, J.; Hainzl, S.

2011-08-01

Geothermal energy is becoming an important clean energy source, however, the stimulation of a reservoir for an Enhanced Geothermal System (EGS) is associated with seismic risk due to induced seismicity. Seismicity occurring due to the water injection at depth have to be well recorded and monitored. To mitigate the seismic risk of a damaging event, an appropriate alarm system needs to be in place for each individual experiment. In recent experiments, the so-called traffic-light alarm system, based on public response, local magnitude and peak ground velocity, was used. We aim to improve the pre-defined alarm system by introducing a probability-based approach; we retrospectively model the ongoing seismicity in real time with multiple statistical forecast models and then translate the forecast to seismic hazard in terms of probabilities of exceeding a ground motion intensity level. One class of models accounts for the water injection rate, the main parameter that can be controlled by the operators during an experiment. By translating the models into time-varying probabilities of exceeding various intensity levels, we provide tools which are well understood by the decision makers and can be used to determine thresholds non-exceedance during a reservoir stimulation; this, however, remains an entrepreneurial or political decision of the responsible project coordinators. We introduce forecast models based on the data set of an EGS experiment in the city of Basel. Between 2006 December 2 and 8, approximately 11 500 m3 of water was injected into a 5-km-deep well at high pressures. A six-sensor borehole array, was installed by the company Geothermal Explorers Limited (GEL) at depths between 300 and 2700 m around the well to monitor the induced seismicity. The network recorded approximately 11 200 events during the injection phase, more than 3500 of which were located. With the traffic-light system, actions where implemented after an ML 2.7 event, the water injection was reduced and then stopped after another ML 2.5 event. A few hours later, an earthquake with ML 3.4, felt within the city, occurred, which led to bleed-off of the well. A risk study was later issued with the outcome that the experiment could not be resumed. We analyse the statistical features of the sequence and show that the sequence is well modelled with the Omori-Utsu law following the termination of water injection. Based on this model, the sequence will last 31+29/-14 years to reach the background level. We introduce statistical models based on Reasenberg and Jones and Epidemic Type Aftershock Sequence (ETAS) models, commonly used to model aftershock sequences. We compare and test different model setups to simulate the sequences, varying the number of fixed and free parameters. For one class of the ETAS models, we account for the flow rate at the injection borehole. We test the models against the observed data with standard likelihood tests and find the ETAS model accounting for the on flow rate to perform best. Such a model may in future serve as a valuable tool for designing probabilistic alarm systems for EGS experiments.

A Markovian event-based framework for stochastic spiking neural networks.

PubMed

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

Extraction of decision rules via imprecise probabilities

NASA Astrophysics Data System (ADS)

Abellán, Joaquín; López, Griselda; Garach, Laura; Castellano, Javier G.

2017-05-01

Data analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via decision trees. The decision trees used by the original method were built using classic split criteria. The performance of new split criteria based on imprecise probabilities and uncertainty measures, called credal split criteria, differs significantly from the performance obtained using the classic criteria. This paper extends the IRNV method using two credal split criteria: one based on a mathematical parametric model, and other one based on a non-parametric model. The performance of the method is analyzed using a case study of traffic accident data to identify patterns related to the severity of an accident. We found that a larger number of rules is generated, significantly supplementing the information obtained using the classic split criteria.

Asymptotic Equivalence of Probability Measures and Stochastic Processes

NASA Astrophysics Data System (ADS)

Touchette, Hugo

2018-03-01

Let P_n and Q_n be two probability measures representing two different probabilistic models of some system (e.g., an n-particle equilibrium system, a set of random graphs with n vertices, or a stochastic process evolving over a time n) and let M_n be a random variable representing a "macrostate" or "global observable" of that system. We provide sufficient conditions, based on the Radon-Nikodym derivative of P_n and Q_n, for the set of typical values of M_n obtained relative to P_n to be the same as the set of typical values obtained relative to Q_n in the limit n→ ∞. This extends to general probability measures and stochastic processes the well-known thermodynamic-limit equivalence of the microcanonical and canonical ensembles, related mathematically to the asymptotic equivalence of conditional and exponentially-tilted measures. In this more general sense, two probability measures that are asymptotically equivalent predict the same typical or macroscopic properties of the system they are meant to model.

Hawkes-diffusion process and the conditional probability of defaults in the Eurozone

NASA Astrophysics Data System (ADS)

Kim, Jungmu; Park, Yuen Jung; Ryu, Doojin

2016-05-01

This study examines market information embedded in the European sovereign CDS (credit default swap) market by analyzing the sovereign CDSs of 13 Eurozone countries from January 1, 2008, to February 29, 2012, which includes the recent Eurozone debt crisis period. We design the conditional probability of defaults for the CDS prices based on the Hawkes-diffusion process and obtain the theoretical prices of CDS indexes. To estimate the model parameters, we calibrate the model prices to empirical prices obtained from individual sovereign CDS term structure data. The estimated parameters clearly explain both cross-sectional and time-series data. Our empirical results show that the probability of a huge loss event sharply increased during the Eurozone debt crisis, indicating a contagion effect. Even countries with strong and stable economies, such as Germany and France, suffered from the contagion effect. We also find that the probability of small events is sensitive to the state of the economy, spiking several times due to the global financial crisis and the Greek government debt crisis.

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

Chen, Hang, E-mail: hangchen@mit.edu; Thill, Peter; Cao, Jianshu

In biochemical systems, intrinsic noise may drive the system switch from one stable state to another. We investigate how kinetic switching between stable states in a bistable network is influenced by dynamic disorder, i.e., fluctuations in the rate coefficients. Using the geometric minimum action method, we first investigate the optimal transition paths and the corresponding minimum actions based on a genetic toggle switch model in which reaction coefficients draw from a discrete probability distribution. For the continuous probability distribution of the rate coefficient, we then consider two models of dynamic disorder in which reaction coefficients undergo different stochastic processes withmore » the same stationary distribution. In one, the kinetic parameters follow a discrete Markov process and in the other they follow continuous Langevin dynamics. We find that regulation of the parameters modulating the dynamic disorder, as has been demonstrated to occur through allosteric control in bistable networks in the immune system, can be crucial in shaping the statistics of optimal transition paths, transition probabilities, and the stationary probability distribution of the network.« less

Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate plus nitrate as nitrogen in ground water in the Idaho part of the western Snake River Plain

USGS Publications Warehouse

Donato, Mary M.

2000-01-01

As ground water continues to provide an ever-growing proportion of Idaho?s drinking water, concerns about the quality of that resource are increasing. Pesticides (most commonly, atrazine/desethyl-atrazine, hereafter referred to as atrazine) and nitrite plus nitrate as nitrogen (hereafter referred to as nitrate) have been detected in many aquifers in the State. To provide a sound hydrogeologic basis for atrazine and nitrate management in southern Idaho—the largest region of land and water use in the State—the U.S. Geological Survey produced maps showing the probability of detecting these contaminants in ground water in the upper Snake River Basin (published in a 1998 report) and the western Snake River Plain (published in this report). The atrazine probability map for the western Snake River Plain was constructed by overlaying ground-water quality data with hydrogeologic and anthropogenic data in a geographic information system (GIS). A data set was produced in which each well had corresponding information on land use, geology, precipitation, soil characteristics, regional depth to ground water, well depth, water level, and atrazine use. These data were analyzed by logistic regression using a statistical software package. Several preliminary multivariate models were developed and those that best predicted the detection of atrazine were selected. The multivariate models then were entered into a GIS and the probability maps were produced. Land use, precipitation, soil hydrologic group, and well depth were significantly correlated with atrazine detections in the western Snake River Plain. These variables also were important in the 1998 probability study of the upper Snake River Basin. The effectiveness of the probability models for atrazine might be improved if more detailed data were available for atrazine application. A preliminary atrazine probability map for the entire Snake River Plain in Idaho, based on a data set representing that region, also was produced. In areas where this map overlaps the 1998 map of the upper Snake River Basin, the two maps show broadly similar probabilities of detecting atrazine. Logistic regression also was used to develop a preliminary statistical model that predicts the probability of detecting elevated nitrate in the western Snake River Plain. A nitrate probability map was produced from this model. Results showed that elevated nitrate concentrations were correlated with land use, soil organic content, well depth, and water level. Detailed information on nitrate input, specifically fertilizer application, might have improved the effectiveness of this model.

Efficient Bayesian parameter estimation with implicit sampling and surrogate modeling for a vadose zone hydrological problem

NASA Astrophysics Data System (ADS)

Liu, Y.; Pau, G. S. H.; Finsterle, S.

2015-12-01

Parameter inversion involves inferring the model parameter values based on sparse observations of some observables. To infer the posterior probability distributions of the parameters, Markov chain Monte Carlo (MCMC) methods are typically used. However, the large number of forward simulations needed and limited computational resources limit the complexity of the hydrological model we can use in these methods. In view of this, we studied the implicit sampling (IS) method, an efficient importance sampling technique that generates samples in the high-probability region of the posterior distribution and thus reduces the number of forward simulations that we need to run. For a pilot-point inversion of a heterogeneous permeability field based on a synthetic ponded infiltration experiment simu­lated with TOUGH2 (a subsurface modeling code), we showed that IS with linear map provides an accurate Bayesian description of the parameterized permeability field at the pilot points with just approximately 500 forward simulations. We further studied the use of surrogate models to improve the computational efficiency of parameter inversion. We implemented two reduced-order models (ROMs) for the TOUGH2 forward model. One is based on polynomial chaos expansion (PCE), of which the coefficients are obtained using the sparse Bayesian learning technique to mitigate the "curse of dimensionality" of the PCE terms. The other model is Gaussian process regression (GPR) for which different covariance, likelihood and inference models are considered. Preliminary results indicate that ROMs constructed based on the prior parameter space perform poorly. It is thus impractical to replace this hydrological model by a ROM directly in a MCMC method. However, the IS method can work with a ROM constructed for parameters in the close vicinity of the maximum a posteriori probability (MAP) estimate. We will discuss the accuracy and computational efficiency of using ROMs in the implicit sampling procedure for the hydrological problem considered. This work was supported, in part, by the U.S. Dept. of Energy under Contract No. DE-AC02-05CH11231

Reliability of Laparoscopic Compared With Hysteroscopic Sterilization at One Year: A Decision Analysis

PubMed Central

Gariepy, Aileen M.; Creinin, Mitchell D.; Schwarz, Eleanor B.; Smith, Kenneth J.

2011-01-01

OBJECTIVE To estimate the probability of successful sterilization after hysteroscopic or laparoscopic sterilization procedure. METHODS An evidence-based clinical decision analysis using a Markov model was performed to estimate the probability of a successful sterilization procedure using laparoscopic sterilization, hysteroscopic sterilization in the operating room, and hysteroscopic sterilization in the office. Procedure and follow-up testing probabilities for the model were estimated from published sources. RESULTS In the base case analysis, the proportion of women having a successful sterilization procedure on first attempt is 99% for laparoscopic, 88% for hysteroscopic in the operating room and 87% for hysteroscopic in the office. The probability of having a successful sterilization procedure within one year is 99% with laparoscopic, 95% for hysteroscopic in the operating room, and 94% for hysteroscopic in the office. These estimates for hysteroscopic success include approximately 6% of women who attempt hysteroscopically but are ultimately sterilized laparoscopically. Approximately 5% of women who have a failed hysteroscopic attempt decline further sterilization attempts. CONCLUSIONS Women choosing laparoscopic sterilization are more likely than those choosing hysteroscopic sterilization to have a successful sterilization procedure within one year. However, the risk of failed sterilization and subsequent pregnancy must be considered when choosing a method of sterilization. PMID:21775842

Reliability of laparoscopic compared with hysteroscopic sterilization at 1 year: a decision analysis.

PubMed

Gariepy, Aileen M; Creinin, Mitchell D; Schwarz, Eleanor B; Smith, Kenneth J

2011-08-01

To estimate the probability of successful sterilization after an hysteroscopic or laparoscopic sterilization procedure. An evidence-based clinical decision analysis using a Markov model was performed to estimate the probability of a successful sterilization procedure using laparoscopic sterilization, hysteroscopic sterilization in the operating room, and hysteroscopic sterilization in the office. Procedure and follow-up testing probabilities for the model were estimated from published sources. In the base case analysis, the proportion of women having a successful sterilization procedure on the first attempt is 99% for laparoscopic sterilization, 88% for hysteroscopic sterilization in the operating room, and 87% for hysteroscopic sterilization in the office. The probability of having a successful sterilization procedure within 1 year is 99% with laparoscopic sterilization, 95% for hysteroscopic sterilization in the operating room, and 94% for hysteroscopic sterilization in the office. These estimates for hysteroscopic success include approximately 6% of women who attempt hysteroscopically but are ultimately sterilized laparoscopically. Approximately 5% of women who have a failed hysteroscopic attempt decline further sterilization attempts. Women choosing laparoscopic sterilization are more likely than those choosing hysteroscopic sterilization to have a successful sterilization procedure within 1 year. However, the risk of failed sterilization and subsequent pregnancy must be considered when choosing a method of sterilization.

Statistical physics of medical diagnostics: Study of a probabilistic model.

PubMed

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

Mars Exploration Rovers Landing Dispersion Analysis

NASA Technical Reports Server (NTRS)

Knocke, Philip C.; Wawrzyniak, Geoffrey G.; Kennedy, Brian M.; Desai, Prasun N.; Parker, TImothy J.; Golombek, Matthew P.; Duxbury, Thomas C.; Kass, David M.

2004-01-01

Landing dispersion estimates for the Mars Exploration Rover missions were key elements in the site targeting process and in the evaluation of landing risk. This paper addresses the process and results of the landing dispersion analyses performed for both Spirit and Opportunity. The several contributors to landing dispersions (navigation and atmospheric uncertainties, spacecraft modeling, winds, and margins) are discussed, as are the analysis tools used. JPL's MarsLS program, a MATLAB-based landing dispersion visualization and statistical analysis tool, was used to calculate the probability of landing within hazardous areas. By convolving this with the probability of landing within flight system limits (in-spec landing) for each hazard area, a single overall measure of landing risk was calculated for each landing ellipse. In-spec probability contours were also generated, allowing a more synoptic view of site risks, illustrating the sensitivity to changes in landing location, and quantifying the possible consequences of anomalies such as incomplete maneuvers. Data and products required to support these analyses are described, including the landing footprints calculated by NASA Langley's POST program and JPL's AEPL program, cartographically registered base maps and hazard maps, and flight system estimates of in-spec landing probabilities for each hazard terrain type. Various factors encountered during operations, including evolving navigation estimates and changing atmospheric models, are discussed and final landing points are compared with approach estimates.

Statistical physics of medical diagnostics: Study of a probabilistic model

NASA Astrophysics Data System (ADS)

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

Comparison of output-based approaches used to substantiate bovine tuberculosis free status in Danish cattle herds.

PubMed

Foddai, Alessandro; Nielsen, Liza Rosenbaum; Willeberg, Preben; Alban, Lis

2015-09-01

We compared two published studies based on different output-based surveillance models, which were used for evaluating the performance of two meat inspection systems in cattle and to substantiate freedom from bovine tuberculosis (bTB) in Denmark. The systems were the current meat inspection methods (CMI) vs. the visual-only inspection (VOI). In one study, the surveillance system sensitivity (SSe) was estimated to substantiate the bTB free status. The other study used SSe in the estimation of the probability of freedom (PFree), based on the epidemiological concept of negative predictive value to substantiate the bTB free status. Both studies found that changing from CMI to VOI would markedly decrease the SSe. However, the two studies reported diverging conclusions regarding the effect on the substantiation of Denmark as a bTB free country, if VOI were to be introduced. The objectives of this work were: (a) to investigate the reasons why conclusions based on the two models differed, and (b) to create a hybrid model based on elements from both studies to evaluate the impact of a change from CMI to VOI. The hybrid model was based on the PFree approach to substantiate freedom from bTB and was parametrized with inputs according to the newest available information. The PFree was updated on an annual basis for each of 42 years of test-negative surveillance data (1995-2037), while assuming a low (<1%) annual probability of introduction of bTB into Danish cattle herds. The most important reasons for the difference between the study conclusions were: the approach chosen to substantiate the bTB free status (SSe vs. PFree) and the number of years of surveillance data considered. With the hybrid model, the PFree reached a level >95% after the first year of surveillance and remained ≥96% with both the CMI and VOI systems until the end of the analyzed period. It is appropriate to use the PFree of the surveillance system to substantiate confidence in bTB free status, when test-negative surveillance results can be documented over an extended period of time, while maintaining a low probability of introduction of bTB into the cattle population. For Denmark, the probability of introduction of bTB should be kept <1% on an annual basis to sustain the high confidence in freedom over time. The results could be considered when deciding if the CMI can be replaced by VOI in cattle abattoirs of countries for which bTB freedom can be demonstrated. Copyright © 2015 Elsevier B.V. All rights reserved.

The application of cure models in the presence of competing risks: a tool for improved risk communication in population-based cancer patient survival.

PubMed

Eloranta, Sandra; Lambert, Paul C; Andersson, Therese M-L; Björkholm, Magnus; Dickman, Paul W

2014-09-01

Quantifying cancer patient survival from the perspective of cure is clinically relevant. However, most cure models estimate cure assuming no competing causes of death. We use a relative survival framework to demonstrate how flexible parametric cure models can be used in combination with competing-risks theory to incorporate noncancer deaths. Under a model that incorporates statistical cure, we present the probabilities that cancer patients (1) have died from their cancer, (2) have died from other causes, (3) will eventually die from their cancer, or (4) will eventually die from other causes, all as a function of time since diagnosis. We further demonstrate how conditional probabilities can be used to update the prognosis among survivors (eg, at 1 or 5 years after diagnosis) by summarizing the proportion of patients who will not die from their cancer. The proposed method is applied to Swedish population-based data for persons diagnosed with melanoma, colon cancer, or acute myeloid leukemia between 1973 and 2007.

Improving deep convolutional neural networks with mixed maxout units

PubMed Central

Liu, Fu-xian; Li, Long-yue

2017-01-01

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that “non-maximal features are unable to deliver” and “feature mapping subspace pooling is insufficient,” we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance. PMID:28727737

Estimating the personal cure rate of cancer patients using population-based grouped cancer survival data.

PubMed

Binbing Yu; Tiwari, Ram C; Feuer, Eric J

2011-06-01

Cancer patients are subject to multiple competing risks of death and may die from causes other than the cancer diagnosed. The probability of not dying from the cancer diagnosed, which is one of the patients' main concerns, is sometimes called the 'personal cure' rate. Two approaches of modelling competing-risk survival data, namely the cause-specific hazards approach and the mixture model approach, have been used to model competing-risk survival data. In this article, we first show the connection and differences between crude cause-specific survival in the presence of other causes and net survival in the absence of other causes. The mixture survival model is extended to population-based grouped survival data to estimate the personal cure rate. Using the colorectal cancer survival data from the Surveillance, Epidemiology and End Results Programme, we estimate the probabilities of dying from colorectal cancer, heart disease, and other causes by age at diagnosis, race and American Joint Committee on Cancer stage.

Exploiting vibrational resonance in weak-signal detection

NASA Astrophysics Data System (ADS)

Ren, Yuhao; Pan, Yan; Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek

2017-08-01

In this paper, we investigate the first exploitation of the vibrational resonance (VR) effect to detect weak signals in the presence of strong background noise. By injecting a series of sinusoidal interference signals of the same amplitude but with different frequencies into a generalized correlation detector, we show that the detection probability can be maximized at an appropriate interference amplitude. Based on a dual-Dirac probability density model, we compare the VR method with the stochastic resonance approach via adding dichotomous noise. The compared results indicate that the VR method can achieve a higher detection probability for a wider variety of noise distributions.

Exploiting vibrational resonance in weak-signal detection.

PubMed

Ren, Yuhao; Pan, Yan; Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek

2017-08-01

In this paper, we investigate the first exploitation of the vibrational resonance (VR) effect to detect weak signals in the presence of strong background noise. By injecting a series of sinusoidal interference signals of the same amplitude but with different frequencies into a generalized correlation detector, we show that the detection probability can be maximized at an appropriate interference amplitude. Based on a dual-Dirac probability density model, we compare the VR method with the stochastic resonance approach via adding dichotomous noise. The compared results indicate that the VR method can achieve a higher detection probability for a wider variety of noise distributions.

Investigation of reliability indicators of information analysis systems based on Markov’s absorbing chain model

NASA Astrophysics Data System (ADS)

Gilmanshin, I. R.; Kirpichnikov, A. P.

2017-09-01

In the result of study of the algorithm of the functioning of the early detection module of excessive losses, it is proven the ability to model it by using absorbing Markov chains. The particular interest is in the study of probability characteristics of early detection module functioning algorithm of losses in order to identify the relationship of indicators of reliability of individual elements, or the probability of occurrence of certain events and the likelihood of transmission of reliable information. The identified relations during the analysis allow to set thresholds reliability characteristics of the system components.

The External Validity of Prediction Models for the Diagnosis of Obstructive Coronary Artery Disease in Patients With Stable Chest Pain: Insights From the PROMISE Trial.

PubMed

Genders, Tessa S S; Coles, Adrian; Hoffmann, Udo; Patel, Manesh R; Mark, Daniel B; Lee, Kerry L; Steyerberg, Ewout W; Hunink, M G Myriam; Douglas, Pamela S

2018-03-01

This study sought to externally validate prediction models for the presence of obstructive coronary artery disease (CAD). A better assessment of the probability of CAD may improve the identification of patients who benefit from noninvasive testing. Stable chest pain patients from the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial with computed tomography angiography (CTA) or invasive coronary angiography (ICA) were included. The authors assumed that patients with CTA showing 0% stenosis and a coronary artery calcium (CAC) score of 0 were free of obstructive CAD (≥50% stenosis) on ICA, and they multiply imputed missing ICA results based on clinical variables and CTA results. Predicted CAD probabilities were calculated using published coefficients for 3 models: basic model (age, sex, chest pain type), clinical model (basic model + diabetes, hypertension, dyslipidemia, and smoking), and clinical + CAC score model. The authors assessed discrimination and calibration, and compared published effects with observed predictor effects. In 3,468 patients (1,805 women; mean 60 years of age; 779 [23%] with obstructive CAD on CTA), the models demonstrated moderate-good discrimination, with C-statistics of 0.69 (95% confidence interval [CI]: 0.67 to 0.72), 0.72 (95% CI: 0.69 to 0.74), and 0.86 (95% CI: 0.85 to 0.88) for the basic, clinical, and clinical + CAC score models, respectively. Calibration was satisfactory although typical chest pain and diabetes were less predictive and CAC score was more predictive than was suggested by the models. Among the 31% of patients for whom the clinical model predicted a low (≤10%) probability of CAD, actual prevalence was 7%; among the 48% for whom the clinical + CAC score model predicted a low probability the observed prevalence was 2%. In 2 sensitivity analyses excluding imputed data, similar results were obtained using CTA as the outcome, whereas in those who underwent ICA the models significantly underestimated CAD probability. Existing clinical prediction models can identify patients with a low probability of obstructive CAD. Obstructive CAD on ICA was imputed for 61% of patients; hence, further validation is necessary. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Stochastic von Bertalanffy models, with applications to fish recruitment.

PubMed

Lv, Qiming; Pitchford, Jonathan W

2007-02-21

We consider three individual-based models describing growth in stochastic environments. Stochastic differential equations (SDEs) with identical von Bertalanffy deterministic parts are formulated, with a stochastic term which decreases, remains constant, or increases with organism size, respectively. Probability density functions for hitting times are evaluated in the context of fish growth and mortality. Solving the hitting time problem analytically or numerically shows that stochasticity can have a large positive impact on fish recruitment probability. It is also demonstrated that the observed mean growth rate of surviving individuals always exceeds the mean population growth rate, which itself exceeds the growth rate of the equivalent deterministic model. The consequences of these results in more general biological situations are discussed.

Elastic K-means using posterior probability.

PubMed

Zheng, Aihua; Jiang, Bo; Li, Yan; Zhang, Xuehan; Ding, Chris

2017-01-01

The widely used K-means clustering is a hard clustering algorithm. Here we propose a Elastic K-means clustering model (EKM) using posterior probability with soft capability where each data point can belong to multiple clusters fractionally and show the benefit of proposed Elastic K-means. Furthermore, in many applications, besides vector attributes information, pairwise relations (graph information) are also available. Thus we integrate EKM with Normalized Cut graph clustering into a single clustering formulation. Finally, we provide several useful matrix inequalities which are useful for matrix formulations of learning models. Based on these results, we prove the correctness and the convergence of EKM algorithms. Experimental results on six benchmark datasets demonstrate the effectiveness of proposed EKM and its integrated model.

SURE reliability analysis: Program and mathematics

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; White, Allan L.

1988-01-01

The SURE program is a new reliability analysis tool for ultrareliable computer system architectures. The computational methods on which the program is based provide an efficient means for computing accurate upper and lower bounds for the death state probabilities of a large class of semi-Markov models. Once a semi-Markov model is described using a simple input language, the SURE program automatically computes the upper and lower bounds on the probability of system failure. A parameter of the model can be specified as a variable over a range of values directing the SURE program to perform a sensitivity analysis automatically. This feature, along with the speed of the program, makes it especially useful as a design tool.

Effects of weather on survival in populations of boreal toads in Colorado

USGS Publications Warehouse

Scherer, R. D.; Muths, E.; Lambert, B.A.

2008-01-01

Understanding the relationships between animal population demography and the abiotic and biotic elements of the environments in which they live is a central objective in population ecology. For example, correlations between weather variables and the probability of survival in populations of temperate zone amphibians may be broadly applicable to several species if such correlations can be validated for multiple situations. This study focuses on the probability of survival and evaluates hypotheses based on six weather variables in three populations of Boreal Toads (Bufo boreas) from central Colorado over eight years. In addition to suggesting a relationship between some weather variables and survival probability in Boreal Toad populations, this study uses robust methods and highlights the need for demographic estimates that are precise and have minimal bias. Capture-recapture methods were used to collect the data, and the Cormack-Jolly-Seber model in program MARK was used for analysis. The top models included minimum daily winter air temperature, and the sum of the model weights for these models was 0.956. Weaker support was found for the importance of snow depth and the amount of environmental moisture in winter in modeling survival probability. Minimum daily winter air temperature was positively correlated with the probability of survival in Boreal Toads at other sites in Colorado and has been identified as an important covariate in studies in other parts of the world. If air temperatures are an important component of survival for Boreal Toads or other amphibians, changes in climate may have profound impacts on populations. Copyright 2008 Society for the Study of Amphibians and Reptiles.

Structural reliability analysis under evidence theory using the active learning kriging model

NASA Astrophysics Data System (ADS)

Yang, Xufeng; Liu, Yongshou; Ma, Panke

2017-11-01

Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush-Kuhn-Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.

Stochastic damage evolution in textile laminates

NASA Technical Reports Server (NTRS)

Dzenis, Yuris A.; Bogdanovich, Alexander E.; Pastore, Christopher M.

1993-01-01

A probabilistic model utilizing random material characteristics to predict damage evolution in textile laminates is presented. Model is based on a division of each ply into two sublaminas consisting of cells. The probability of cell failure is calculated using stochastic function theory and maximal strain failure criterion. Three modes of failure, i.e. fiber breakage, matrix failure in transverse direction, as well as matrix or interface shear cracking, are taken into account. Computed failure probabilities are utilized in reducing cell stiffness based on the mesovolume concept. A numerical algorithm is developed predicting the damage evolution and deformation history of textile laminates. Effect of scatter of fiber orientation on cell properties is discussed. Weave influence on damage accumulation is illustrated with the help of an example of a Kevlar/epoxy laminate.

Determination of riverbank erosion probability using Locally Weighted Logistic Regression

NASA Astrophysics Data System (ADS)

Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos

2015-04-01

Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding the independent variables tested. The most straightforward measure for goodness of fit is the G statistic. It is a simple and effective way to study and evaluate the Logistic Regression model efficiency and the reliability of each independent variable. The developed statistical model is applied to the Koiliaris River Basin on the island of Crete, Greece. Two datasets of river bank slope, river cross-section width and indications of erosion were available for the analysis (12 and 8 locations). Two different types of spatial dependence functions, exponential and tricubic, were examined to determine the local spatial dependence of the independent variables at the measurement locations. The results show a significant improvement when the tricubic function is applied as the erosion probability is accurately predicted at all eight validation locations. Results for the model deviance show that cross-section width is more important than bank slope in the estimation of erosion probability along the Koiliaris riverbanks. The proposed statistical model is a useful tool that quantifies the erosion probability along the riverbanks and can be used to assist managing erosion and flooding events. Acknowledgements This work is part of an on-going THALES project (CYBERSENSORS - High Frequency Monitoring System for Integrated Water Resources Management of Rivers). The project has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.

Risk Assessment of Bone Fracture During Space Exploration Missions to the Moon and Mars

NASA Technical Reports Server (NTRS)

Lewandowski, Beth E.; Myers, Jerry G.; Nelson, Emily S.; Licatta, Angelo; Griffin, Devon

2007-01-01

The possibility of a traumatic bone fracture in space is a concern due to the observed decrease in astronaut bone mineral density (BMD) during spaceflight and because of the physical demands of the mission. The Bone Fracture Risk Module (BFxRM) was developed to quantify the probability of fracture at the femoral neck and lumbar spine during space exploration missions. The BFxRM is scenario-based, providing predictions for specific activities or events during a particular space mission. The key elements of the BFxRM are the mission parameters, the biomechanical loading models, the bone loss and fracture models and the incidence rate of the activity or event. Uncertainties in the model parameters arise due to variations within the population and unknowns associated with the effects of the space environment. Consequently, parameter distributions were used in Monte Carlo simulations to obtain an estimate of fracture probability under real mission scenarios. The model predicts an increase in the probability of fracture as the mission length increases and fracture is more likely in the higher gravitational field of Mars than on the moon. The resulting probability predictions and sensitivity analyses of the BFxRM can be used as an engineering tool for mission operation and resource planning in order to mitigate the risk of bone fracture in space.

Risk Assessment of Bone Fracture During Space Exploration Missions to the Moon and Mars

NASA Technical Reports Server (NTRS)

Lewandowski, Beth E.; Myers, Jerry G.; Nelson, Emily S.; Griffin, Devon

2008-01-01

The possibility of a traumatic bone fracture in space is a concern due to the observed decrease in astronaut bone mineral density (BMD) during spaceflight and because of the physical demands of the mission. The Bone Fracture Risk Module (BFxRM) was developed to quantify the probability of fracture at the femoral neck and lumbar spine during space exploration missions. The BFxRM is scenario-based, providing predictions for specific activities or events during a particular space mission. The key elements of the BFxRM are the mission parameters, the biomechanical loading models, the bone loss and fracture models and the incidence rate of the activity or event. Uncertainties in the model parameters arise due to variations within the population and unknowns associated with the effects of the space environment. Consequently, parameter distributions were used in Monte Carlo simulations to obtain an estimate of fracture probability under real mission scenarios. The model predicts an increase in the probability of fracture as the mission length increases and fracture is more likely in the higher gravitational field of Mars than on the moon. The resulting probability predictions and sensitivity analyses of the BFxRM can be used as an engineering tool for mission operation and resource planning in order to mitigate the risk of bone fracture in space.

Kepler Planet Reliability Metrics: Astrophysical Positional Probabilities for Data Release 25

NASA Technical Reports Server (NTRS)

Bryson, Stephen T.; Morton, Timothy D.

2017-01-01

This document is very similar to KSCI-19092-003, Planet Reliability Metrics: Astrophysical Positional Probabilities, which describes the previous release of the astrophysical positional probabilities for Data Release 24. The important changes for Data Release 25 are:1. The computation of the astrophysical positional probabilities uses the Data Release 25 processed pixel data for all Kepler Objects of Interest.2. Computed probabilities now have associated uncertainties, whose computation is described in x4.1.3.3. The scene modeling described in x4.1.2 uses background stars detected via ground-based high-resolution imaging, described in x5.1, that are not in the Kepler Input Catalog or UKIRT catalog. These newly detected stars are presented in Appendix B. Otherwise the text describing the algorithms and examples is largely unchanged from KSCI-19092-003.

Prediction of the 10-year probability of gastric cancer occurrence in the Japanese population: the JPHC study cohort II.

PubMed

Charvat, Hadrien; Sasazuki, Shizuka; Inoue, Manami; Iwasaki, Motoki; Sawada, Norie; Shimazu, Taichi; Yamaji, Taiki; Tsugane, Shoichiro

2016-01-15

Gastric cancer is a particularly important issue in Japan, where incidence rates are among the highest observed. In this work, we provide a risk prediction model allowing the estimation of the 10-year cumulative probability of gastric cancer occurrence. The study population consisted of 19,028 individuals from the Japanese Public Health Center cohort II who were followed-up from 1993 to 2009. A parametric survival model was used to assess the impact on the probability of gastric cancer of clinical and lifestyle-related risk factors in combination with serum anti-Helicobacter pylori antibody titres and pepsinogen I and pepsinogen II levels. Based on the resulting model, cumulative probability estimates were calculated and a simple risk scoring system was developed. A total of 412 cases of gastric cancer occurred during 270,854 person-years of follow-up. The final model included (besides the biological markers) age, gender, smoking status, family history of gastric cancer and consumption of highly salted food. The developed prediction model showed good predictive performance in terms of discrimination (optimism-corrected c-index: 0.768) and calibration (Nam and d'Agostino's χ(2) test: 14.78; p values = 0.06). Estimates of the 10-year probability of gastric cancer occurrence ranged from 0.04% (0.02, 0.1) to 14.87% (8.96, 24.14) for men and from 0.03% (0.02, 0.07) to 4.91% (2.71, 8.81) for women. In conclusion, we developed a risk prediction model for gastric cancer that combines clinical and biological markers. It might prompt individuals to modify their lifestyle habits, attend regular check-up visits or participate in screening programmes. © 2015 UICC.

Comparative Risk Analysis of Two Culicoides-Borne Diseases in Horses: Equine Encephalosis More Likely to Enter France than African Horse Sickness.

PubMed

Faverjon, C; Leblond, A; Lecollinet, S; Bødker, R; de Koeijer, A A; Fischer, E A J

2017-12-01

African horse sickness (AHS) and equine encephalosis (EE) are Culicoides-borne viral diseases that could have the potential to spread across Europe if introduced, thus being potential threats for the European equine industry. Both share similar epidemiology, transmission patterns and geographical distribution. Using stochastic spatiotemporal models of virus entry, we assessed and compared the probabilities of both viruses entering France via two pathways: importation of live-infected animals or importation of infected vectors. Analyses were performed for three consecutive years (2010-2012). Seasonal and regional differences in virus entry probabilities were the same for both diseases. However, the probability of EE entry was much higher than the probability of AHS entry. Interestingly, the most likely entry route differed between AHS and EE: AHS has a higher probability to enter through an infected vector and EE has a higher probability to enter through an infectious host. Consequently, different effective protective measures were identified by 'what-if' scenarios for the two diseases. The implementation of vector protection on all animals (equine and bovine) coming from low-risk regions before their importation was the most effective in reducing the probability of AHS entry. On the other hand, the most significant reduction in the probability of EE entry was obtained by the implementation of quarantine before import for horses coming from both EU and non-EU countries. The developed models can be useful to implement risk-based surveillance. © 2016 Blackwell Verlag GmbH.

Application of Archimedean copulas to the impact assessment of hydro-climatic variables in semi-arid aquifers of western India

NASA Astrophysics Data System (ADS)

Wable, Pawan S.; Jha, Madan K.

2018-02-01

The effects of rainfall and the El Niño Southern Oscillation (ENSO) on groundwater in a semi-arid basin of India were analyzed using Archimedean copulas considering 17 years of data for monsoon rainfall, post-monsoon groundwater level (PMGL) and ENSO Index. The evaluated dependence among these hydro-climatic variables revealed that PMGL-Rainfall and PMGL-ENSO Index pairs have significant dependence. Hence, these pairs were used for modeling dependence by employing four types of Archimedean copulas: Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard, and Frank. For the copula modeling, the results of probability distributions fitting to these hydro-climatic variables indicated that the PMGL and rainfall time series are best represented by Weibull and lognormal distributions, respectively, while the non-parametric kernel-based normal distribution is the most suitable for the ENSO Index. Further, the PMGL-Rainfall pair is best modeled by the Clayton copula, and the PMGL-ENSO Index pair is best modeled by the Frank copula. The Clayton copula-based conditional probability of PMGL being less than or equal to its average value at a given mean rainfall is above 70% for 33% of the study area. In contrast, the spatial variation of the Frank copula-based probability of PMGL being less than or equal to its average value is 35-40% in 23% of the study area during El Niño phase, while it is below 15% in 35% of the area during the La Niña phase. This copula-based methodology can be applied under data-scarce conditions for exploring the impacts of rainfall and ENSO on groundwater at basin scales.

A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis with Microbial Compositions.

PubMed

Gao, Xiang; Lin, Huaiying; Dong, Qunfeng

2017-01-01

Dysbiosis of microbial communities is associated with various human diseases, raising the possibility of using microbial compositions as biomarkers for disease diagnosis. We have developed a Bayes classifier by modeling microbial compositions with Dirichlet-multinomial distributions, which are widely used to model multicategorical count data with extra variation. The parameters of the Dirichlet-multinomial distributions are estimated from training microbiome data sets based on maximum likelihood. The posterior probability of a microbiome sample belonging to a disease or healthy category is calculated based on Bayes' theorem, using the likelihood values computed from the estimated Dirichlet-multinomial distribution, as well as a prior probability estimated from the training microbiome data set or previously published information on disease prevalence. When tested on real-world microbiome data sets, our method, called DMBC (for Dirichlet-multinomial Bayes classifier), shows better classification accuracy than the only existing Bayesian microbiome classifier based on a Dirichlet-multinomial mixture model and the popular random forest method. The advantage of DMBC is its built-in automatic feature selection, capable of identifying a subset of microbial taxa with the best classification accuracy between different classes of samples based on cross-validation. This unique ability enables DMBC to maintain and even improve its accuracy at modeling species-level taxa. The R package for DMBC is freely available at https://github.com/qunfengdong/DMBC. IMPORTANCE By incorporating prior information on disease prevalence, Bayes classifiers have the potential to estimate disease probability better than other common machine-learning methods. Thus, it is important to develop Bayes classifiers specifically tailored for microbiome data. Our method shows higher classification accuracy than the only existing Bayesian classifier and the popular random forest method, and thus provides an alternative option for using microbial compositions for disease diagnosis.

Estimating the probability of an extinction or major outbreak for an environmentally transmitted infectious disease.

PubMed

Lahodny, G E; Gautam, R; Ivanek, R

2015-01-01

Indirect transmission through the environment, pathogen shedding by infectious hosts, replication of free-living pathogens within the environment, and environmental decontamination are suspected to play important roles in the spread and control of environmentally transmitted infectious diseases. To account for these factors, the classic Susceptible-Infectious-Recovered-Susceptible epidemic model is modified to include a compartment representing the amount of free-living pathogen within the environment. The model accounts for host demography, direct and indirect transmission, replication of free-living pathogens in the environment, and removal of free-living pathogens by natural death or environmental decontamination. Based on the assumptions of the deterministic model, a continuous-time Markov chain model is developed. An estimate for the probability of disease extinction or a major outbreak is obtained by approximating the Markov chain with a multitype branching process. Numerical simulations illustrate important differences between the deterministic and stochastic counterparts, relevant for outbreak prevention, that depend on indirect transmission, pathogen shedding by infectious hosts, replication of free-living pathogens, and environmental decontamination. The probability of a major outbreak is computed for salmonellosis in a herd of dairy cattle as well as cholera in a human population. An explicit expression for the probability of disease extinction or a major outbreak in terms of the model parameters is obtained for systems with no direct transmission or replication of free-living pathogens.

Protocol for Reliability Assessment of Structural Health Monitoring Systems Incorporating Model-assisted Probability of Detection (MAPOD) Approach

DTIC Science & Technology

2011-09-01

a quality evaluation with limited data, a model -based assessment must be...that affect system performance, a multistage approach to system validation, a modeling and experimental methodology for efficiently addressing a ...affect system performance, a multistage approach to system validation, a modeling and experimental methodology for efficiently addressing a wide range

Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

NASA Technical Reports Server (NTRS)

Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

The analysis of a generic air-to-air missile simulation model

NASA Technical Reports Server (NTRS)

Kaplan, Joseph A.; Chappell, Alan R.; Mcmanus, John W.

1994-01-01

A generic missile model was developed to evaluate the benefits of using a dynamic missile fly-out simulation system versus a static missile launch envelope system for air-to-air combat simulation. This paper examines the performance of a launch envelope model and a missile fly-out model. The launch envelope model bases its probability of killing the target aircraft on the target aircraft's position at the launch time of the weapon. The benefits gained from a launch envelope model are the simplicity of implementation and the minimal computational overhead required. A missile fly-out model takes into account the physical characteristics of the missile as it simulates the guidance, propulsion, and movement of the missile. The missile's probability of kill is based on the missile miss distance (or the minimum distance between the missile and the target aircraft). The problems associated with this method of modeling are a larger computational overhead, the additional complexity required to determine the missile miss distance, and the additional complexity of determining the reason(s) the missile missed the target. This paper evaluates the two methods and compares the results of running each method on a comprehensive set of test conditions.

Space-time modelling of lightning-caused ignitions in the Blue Mountains, Oregon

USGS Publications Warehouse

Diaz-Avalos, Carlos; Peterson, D.L.; Alvarado, Ernesto; Ferguson, Sue A.; Besag, Julian E.

2001-01-01

Generalized linear mixed models (GLMM) were used to study the effect of vegetation cover, elevation, slope, and precipitation on the probability of ignition in the Blue Mountains, Oregon, and to estimate the probability of ignition occurrence at different locations in space and in time. Data on starting location of lightning-caused ignitions in the Blue Mountains between April 1986 and September 1993 constituted the base for the analysis. The study area was divided into a pixela??time array. For each pixela??time location we associated a value of 1 if at least one ignition occurred and 0 otherwise. Covariate information for each pixel was obtained using a geographic information system. The GLMMs were fitted in a Bayesian framework. Higher ignition probabilities were associated with the following cover types: subalpine herbaceous, alpine tundra, lodgepole pine (Pinus contorta Dougl. ex Loud.), whitebark pine (Pinus albicaulis Engelm.), Engelmann spruce (Picea engelmannii Parry ex Engelm.), subalpine fir (Abies lasiocarpa (Hook.) Nutt.), and grand fir (Abies grandis (Dougl.) Lindl.). Within each vegetation type, higher ignition probabilities occurred at lower elevations. Additionally, ignition probabilities are lower in the northern and southern extremes of the Blue Mountains. The GLMM procedure used here is suitable for analysing ignition occurrence in other forested regions where probabilities of ignition are highly variable because of a spatially complex biophysical environment.

Multi-scale occupancy estimation and modelling using multiple detection methods

USGS Publications Warehouse

Nichols, James D.; Bailey, Larissa L.; O'Connell, Allan F.; Talancy, Neil W.; Grant, Evan H. Campbell; Gilbert, Andrew T.; Annand, Elizabeth M.; Husband, Thomas P.; Hines, James E.

2008-01-01

Occupancy estimation and modelling based on detection–nondetection data provide an effective way of exploring change in a species’ distribution across time and space in cases where the species is not always detected with certainty. Today, many monitoring programmes target multiple species, or life stages within a species, requiring the use of multiple detection methods. When multiple methods or devices are used at the same sample sites, animals can be detected by more than one method.We develop occupancy models for multiple detection methods that permit simultaneous use of data from all methods for inference about method-specific detection probabilities. Moreover, the approach permits estimation of occupancy at two spatial scales: the larger scale corresponds to species’ use of a sample unit, whereas the smaller scale corresponds to presence of the species at the local sample station or site.We apply the models to data collected on two different vertebrate species: striped skunks Mephitis mephitis and red salamanders Pseudotriton ruber. For striped skunks, large-scale occupancy estimates were consistent between two sampling seasons. Small-scale occupancy probabilities were slightly lower in the late winter/spring when skunks tend to conserve energy, and movements are limited to males in search of females for breeding. There was strong evidence of method-specific detection probabilities for skunks. As anticipated, large- and small-scale occupancy areas completely overlapped for red salamanders. The analyses provided weak evidence of method-specific detection probabilities for this species.Synthesis and applications. Increasingly, many studies are utilizing multiple detection methods at sampling locations. The modelling approach presented here makes efficient use of detections from multiple methods to estimate occupancy probabilities at two spatial scales and to compare detection probabilities associated with different detection methods. The models can be viewed as another variation of Pollock's robust design and may be applicable to a wide variety of scenarios where species occur in an area but are not always near the sampled locations. The estimation approach is likely to be especially useful in multispecies conservation programmes by providing efficient estimates using multiple detection devices and by providing device-specific detection probability estimates for use in survey design.

Using probability modelling and genetic parentage assignment to test the role of local mate availability in mating system variation.

PubMed

Blyton, Michaela D J; Banks, Sam C; Peakall, Rod; Lindenmayer, David B

2012-02-01

The formal testing of mating system theories with empirical data is important for evaluating the relative importance of different processes in shaping mating systems in wild populations. Here, we present a generally applicable probability modelling framework to test the role of local mate availability in determining a population's level of genetic monogamy. We provide a significance test for detecting departures in observed mating patterns from model expectations based on mate availability alone, allowing the presence and direction of behavioural effects to be inferred. The assessment of mate availability can be flexible and in this study it was based on population density, sex ratio and spatial arrangement. This approach provides a useful tool for (1) isolating the effect of mate availability in variable mating systems and (2) in combination with genetic parentage analyses, gaining insights into the nature of mating behaviours in elusive species. To illustrate this modelling approach, we have applied it to investigate the variable mating system of the mountain brushtail possum (Trichosurus cunninghami) and compared the model expectations with the outcomes of genetic parentage analysis over an 18-year study. The observed level of monogamy was higher than predicted under the model. Thus, behavioural traits, such as mate guarding or selective mate choice, may increase the population level of monogamy. We show that combining genetic parentage data with probability modelling can facilitate an improved understanding of the complex interactions between behavioural adaptations and demographic dynamics in driving mating system variation. © 2011 Blackwell Publishing Ltd.

Robust approaches to quantification of margin and uncertainty for sparse data

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

Hund, Lauren; Schroeder, Benjamin B.; Rumsey, Kelin

Characterizing the tails of probability distributions plays a key role in quantification of margins and uncertainties (QMU), where the goal is characterization of low probability, high consequence events based on continuous measures of performance. When data are collected using physical experimentation, probability distributions are typically fit using statistical methods based on the collected data, and these parametric distributional assumptions are often used to extrapolate about the extreme tail behavior of the underlying probability distribution. In this project, we character- ize the risk associated with such tail extrapolation. Specifically, we conducted a scaling study to demonstrate the large magnitude of themore » risk; then, we developed new methods for communicat- ing risk associated with tail extrapolation from unvalidated statistical models; lastly, we proposed a Bayesian data-integration framework to mitigate tail extrapolation risk through integrating ad- ditional information. We conclude that decision-making using QMU is a complex process that cannot be achieved using statistical analyses alone.« less

Reranking candidate gene models with cross-species comparison for improved gene prediction

PubMed Central

Liu, Qian; Crammer, Koby; Pereira, Fernando CN; Roos, David S

2008-01-01

Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc). Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models. PMID:18854050

Measuring novices' field mapping abilities using an in-class exercise based on expert task analysis

NASA Astrophysics Data System (ADS)

Caulkins, J. L.

2010-12-01

We are interested in developing a model of expert-like behavior for improving the teaching methods of undergraduate field geology. Our aim is to assist students in mastering the process of field mapping more efficiently and effectively and to improve their ability to think creatively in the field. To examine expert-mapping behavior, a cognitive task analysis was conducted with expert geologic mappers in an attempt to define the process of geologic mapping (i.e. to understand how experts carry out geological mapping). The task analysis indicates that expert mappers have a wealth of geologic scenarios at their disposal that they compare against examples seen in the field, experiences that most undergraduate mappers will not have had. While presenting students with many geological examples in class may increase their understanding of geologic processes, novices still struggle when presented with a novel field situation. Based on the task analysis, a short (45-minute) paper-map-based exercise was designed and tested with 14 pairs of 3rd year geology students. The exercise asks students to generate probable geologic models based on a series of four (4) data sets. Each data set represents a day’s worth of data; after the first “day,” new sheets simply include current and previously collected data (e.g. “Day 2” data set includes data from “Day 1” plus the new “Day 2” data). As the geologic complexity increases, students must adapt, reject or generate new geologic models in order to fit the growing data set. Preliminary results of the exercise indicate that students who produced more probable geologic models, and produced higher ratios of probable to improbable models, tended to go on to do better on the mapping exercises at the 3rd year field school. These results suggest that those students with more cognitively available geologic models may be more able to use these models in field settings than those who are unable to draw on these models for whatever reason. Giving students practice at generating geologic models to explain data may be useful in preparing our students for field mapping exercises.