Case studies in Bayesian microbial risk assessments
Directory of Open Access Journals (Sweden)
Turner Joanne
2009-12-01
Full Text Available Abstract Background The quantification of uncertainty and variability is a key component of quantitative risk analysis. Recent advances in Bayesian statistics make it ideal for integrating multiple sources of information, of different types and quality, and providing a realistic estimate of the combined uncertainty in the final risk estimates. Methods We present two case studies related to foodborne microbial risks. In the first, we combine models to describe the sequence of events resulting in illness from consumption of milk contaminated with VTEC O157. We used Monte Carlo simulation to propagate uncertainty in some of the inputs to computer models describing the farm and pasteurisation process. Resulting simulated contamination levels were then assigned to consumption events from a dietary survey. Finally we accounted for uncertainty in the dose-response relationship and uncertainty due to limited incidence data to derive uncertainty about yearly incidences of illness in young children. Options for altering the risk were considered by running the model with different hypothetical policy-driven exposure scenarios. In the second case study we illustrate an efficient Bayesian sensitivity analysis for identifying the most important parameters of a complex computer code that simulated VTEC O157 prevalence within a managed dairy herd. This was carried out in 2 stages, first to screen out the unimportant inputs, then to perform a more detailed analysis on the remaining inputs. The method works by building a Bayesian statistical approximation to the computer code using a number of known code input/output pairs (training runs. Results We estimated that the expected total number of children aged 1.5-4.5 who become ill due to VTEC O157 in milk is 8.6 per year, with 95% uncertainty interval (0,11.5. The most extreme policy we considered was banning on-farm pasteurisation of milk, which reduced the estimate to 6.4 with 95% interval (0,11. In the second
Ott, C. M.; Mena, K. D.; Nickerson, C.A.; Pierson, D. L.
2009-01-01
Historically, microbiological spaceflight requirements have been established in a subjective manner based upon expert opinion of both environmental and clinical monitoring results and the incidence of disease. The limited amount of data, especially from long-duration missions, has created very conservative requirements based primarily on the concentration of microorganisms. Periodic reevaluations of new data from later missions have allowed some relaxation of these stringent requirements. However, the requirements remain very conservative and subjective in nature, and the risk of crew illness due to infectious microorganisms is not well defined. The use of modeling techniques for microbial risk has been applied in the food and potable water industries and has exceptional potential for spaceflight applications. From a productivity standpoint, this type of modeling can (1) decrease unnecessary costs and resource usage and (2) prevent inadequate or inappropriate data for health assessment. In addition, a quantitative model has several advantages for risk management and communication. By identifying the variable components of the model and the knowledge associated with each component, this type of modeling can: (1) Systematically identify and close knowledge gaps, (2) Systematically identify acceptable and unacceptable risks, (3) Improve communication with stakeholders as to the reasons for resource use, and (4) Facilitate external scientific approval of the NASA requirements. The modeling of microbial risk involves the evaluation of several key factors including hazard identification, crew exposure assessment, dose-response assessment, and risk characterization. Many of these factors are similar to conditions found on Earth; however, the spaceflight environment is very specialized as the inhabitants live in a small, semi-closed environment that is often dependent on regenerative life support systems. To further complicate modeling efforts, microbial dose
Bayesian networks for enterprise risk assessment
Bonafede, C E
2006-01-01
According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. In general risk is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover qualitative data must be converted in numerical values to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Network is a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a Bayesian networks in the parti...
Bayesian methods in risk Assessment
International Nuclear Information System (INIS)
The need for a consistent framework for the analysis of large nuclear power plant safety, not provided for by conventional methods of statistics and reliability theory, prompted the present article. The qualification of uncertainties depends crucially on the particular way that the assessor views probability. Two principal schools of thought are the subjectivistic approach advocated by de Finetti, and the frequentist school advocated by von Mises. The point of view of the author is the subjective one. The foundations of the proposed approach and a discussion of several topics relevant to risk assessment follow with applications to the specialization of generic data for site-specific risk studies, the assessment of frequency of fires in nuclear plant compartments, and the use of expert opinion in risk assessments
MATHEMATICAL RISK ANALYSIS: VIA NICHOLAS RISK MODEL AND BAYESIAN ANALYSIS
Directory of Open Access Journals (Sweden)
Anass BAYAGA
2010-07-01
Full Text Available The objective of this second part of a two-phased study was to explorethe predictive power of quantitative risk analysis (QRA method andprocess within Higher Education Institution (HEI. The method and process investigated the use impact analysis via Nicholas risk model and Bayesian analysis, with a sample of hundred (100 risk analysts in a historically black South African University in the greater Eastern Cape Province.The first findings supported and confirmed previous literature (KingIII report, 2009: Nicholas and Steyn, 2008: Stoney, 2007: COSA, 2004 that there was a direct relationship between risk factor, its likelihood and impact, certiris paribus. The second finding in relation to either controlling the likelihood or the impact of occurrence of risk (Nicholas risk model was that to have a brighter risk reward, it was important to control the likelihood ofoccurrence of risks as compared with its impact so to have a direct effect on entire University. On the Bayesian analysis, thus third finding, the impact of risk should be predicted along three aspects. These aspects included the human impact (decisions made, the property impact (students and infrastructural based and the business impact. Lastly, the study revealed that although in most business cases, where as business cycles considerably vary dependingon the industry and or the institution, this study revealed that, most impacts in HEI (University was within the period of one academic.The recommendation was that application of quantitative risk analysisshould be related to current legislative framework that affects HEI.
A Bayesian Networks approach to Operational Risk
Aquaro, V.; Bardoscia, M.; Bellotti, R.; Consiglio, A.; De Carlo, F.; Ferri, G.
2010-04-01
A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters; since the main aim is to understand the role of the correlations among the losses, the assessments of domain experts are not used. The algorithm has been validated on synthetic time series. It should be stressed that the proposed algorithm has been thought for the practical implementation in a mid or small sized bank, since it has a small impact on the organizational structure of a bank and requires an investment in human resources which is limited to the computational area.
Risk Based Maintenance of Offshore Wind Turbines Using Bayesian Networks
DEFF Research Database (Denmark)
Nielsen, Jannie Jessen; Sørensen, John Dalsgaard
2010-01-01
such that the preventive maintenance effort is balanced against the costs to corrective maintenance including indirect costs to reduced production. The basis for the optimization is the risk based Bayesian decision theory. The method is demonstrated through an application example....
A Bayesian Approach to Identifying New Risk Factors for Dementia
Wen, Yen-Hsia; Wu, Shihn-Sheng; Lin, Chun-Hung Richard; Tsai, Jui-Hsiu; Yang, Pinchen; Chang, Yang-Pei; Tseng, Kuan-Hua
2016-01-01
Abstract Dementia is one of the most disabling and burdensome health conditions worldwide. In this study, we identified new potential risk factors for dementia from nationwide longitudinal population-based data by using Bayesian statistics. We first tested the consistency of the results obtained using Bayesian statistics with those obtained using classical frequentist probability for 4 recognized risk factors for dementia, namely severe head injury, depression, diabetes mellitus, and vascular...
Evolution of Subjective Hurricane Risk Perceptions: A Bayesian Approach
David Kelly; David Letson; Forest Nelson; Nolan, David S.; Daniel Solis
2009-01-01
This paper studies how individuals update subjective risk perceptions in response to hurricane track forecast information, using a unique data set from an event market, the Hurricane Futures Market (HFM). We derive a theoretical Bayesian framework which predicts how traders update their perceptions of the probability of a hurricane making landfall in a certain range of coastline. Our results suggest that traders behave in a way consistent with Bayesian updating but this behavior is based on t...
Risk Based Maintenance of Offshore Wind Turbines Using Bayesian Networks
Nielsen, Jannie Jessen; Sørensen, John Dalsgaard
2010-01-01
This paper presents how Bayesian networks can be used to make optimal decisions for repairs of offshore wind turbines. The Bayesian network is an efficient tool for updating a deterioration model whenever new information becomes available from inspections/monitoring. The optimal decision is found such that the preventive maintenance effort is balanced against the costs to corrective maintenance including indirect costs to reduced production. The basis for the optimization is the risk based Ba...
A Bayesian Approach to Identifying New Risk Factors for Dementia
Wen, Yen-Hsia; Wu, Shihn-Sheng; Lin, Chun-Hung Richard; Tsai, Jui-Hsiu; Yang, Pinchen; Chang, Yang-Pei; Tseng, Kuan-Hua
2016-01-01
Abstract Dementia is one of the most disabling and burdensome health conditions worldwide. In this study, we identified new potential risk factors for dementia from nationwide longitudinal population-based data by using Bayesian statistics. We first tested the consistency of the results obtained using Bayesian statistics with those obtained using classical frequentist probability for 4 recognized risk factors for dementia, namely severe head injury, depression, diabetes mellitus, and vascular diseases. Then, we used Bayesian statistics to verify 2 new potential risk factors for dementia, namely hearing loss and senile cataract, determined from the Taiwan's National Health Insurance Research Database. We included a total of 6546 (6.0%) patients diagnosed with dementia. We observed older age, female sex, and lower income as independent risk factors for dementia. Moreover, we verified the 4 recognized risk factors for dementia in the older Taiwanese population; their odds ratios (ORs) ranged from 3.469 to 1.207. Furthermore, we observed that hearing loss (OR = 1.577) and senile cataract (OR = 1.549) were associated with an increased risk of dementia. We found that the results obtained using Bayesian statistics for assessing risk factors for dementia, such as head injury, depression, DM, and vascular diseases, were consistent with those obtained using classical frequentist probability. Moreover, hearing loss and senile cataract were found to be potential risk factors for dementia in the older Taiwanese population. Bayesian statistics could help clinicians explore other potential risk factors for dementia and for developing appropriate treatment strategies for these patients. PMID:27227925
Bayesian probabilistic network approach for managing earthquake risks of cities
DEFF Research Database (Denmark)
Bayraktarli, Yahya; Faber, Michael
2011-01-01
This paper considers the application of Bayesian probabilistic networks (BPNs) to large-scale risk based decision making in regard to earthquake risks. A recently developed risk management framework is outlined which utilises Bayesian probabilistic modelling, generic indicator based risk models and...... geographical information systems. The proposed framework comprises several modules: A module on the probabilistic description of potential future earthquake shaking intensity, a module on the probabilistic assessment of spatial variability of soil liquefaction, a module on damage assessment of buildings and a...... fourth module on the consequences of an earthquake. Each of these modules is integrated into a BPN. Special attention is given to aggregated risk, i.e. the risk contribution from assets at multiple locations in a city subjected to the same earthquake. The application of the methodology is illustrated on...
Bayesian Methods for Measuring Operational Risk
Carol Alexander
2000-01-01
The likely imposition by regulators of minimum standards for capital to cover 'other risks' has been a driving force behind the recent interest in operational risk management. Much discussion has been centered on the form of capital charges for other risks. At the same time major banks are developing models to improve internal management of operational processes, new insurance products for operational risks are being designed and there is growing interest in alternative risk transfer, through...
Bayesian disclosure risk assessment: predicting small frequencies in contingency tables
Forster, Jonathan J.; Webb, Emily L
2007-01-01
We propose an approach for assessing the risk of individual identification in the release of categorical data. This requires the accurate calculation of predictive probabilities for those cells in a contingency table which have small sample frequencies, making the problem somewhat different from usual contingency table estimation, where interest is generally focussed on regions of high probability. Our approach is Bayesian and provides posterior predictive probabilities of identification risk...
Cheese Microbial Risk Assessments - A Review.
Choi, Kyoung-Hee; Lee, Heeyoung; Lee, Soomin; Kim, Sejeong; Yoon, Yohan
2016-03-01
Cheese is generally considered a safe and nutritious food, but foodborne illnesses linked to cheese consumption have occurred in many countries. Several microbial risk assessments related to Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli infections, causing cheese-related foodborne illnesses, have been conducted. Although the assessments of microbial risk in soft and low moisture cheeses such as semi-hard and hard cheeses have been accomplished, it has been more focused on the correlations between pathogenic bacteria and soft cheese, because cheese-associated foodborne illnesses have been attributed to the consumption of soft cheeses. As a part of this microbial risk assessment, predictive models have been developed to describe the relationship between several factors (pH, Aw, starter culture, and time) and the fates of foodborne pathogens in cheese. Predictions from these studies have been used for microbial risk assessment as a part of exposure assessment. These microbial risk assessments have identified that risk increased in cheese with high moisture content, especially for raw milk cheese, but the risk can be reduced by preharvest and postharvest preventions. For accurate quantitative microbial risk assessment, more data including interventions such as curd cooking conditions (temperature and time) and ripening period should be available for predictive models developed with cheese, cheese consumption amounts and cheese intake frequency data as well as more dose-response models. PMID:26950859
Learning Bayesian Network to Explore Connectivity of Risk Factors in Enterprise Risk Management
Paradee Namwongse; Yachai Limpiyakorn
2012-01-01
Enterprise Risk Management provides a holistic top-down view of key risks facing an organization. Developing techniques that can exhibit the inter-connectivity of risks are required to effectively manage risks on an enterprise-wide. This research thus proposed Bayesian Network learning technique to explore the correlated risks in portfolio risk management using the Expressway Authority of Thailand for empirical study. The comparisons of three Bayes Net algorithms for building the risk map wer...
Bayesian-network-based safety risk analysis in construction projects
International Nuclear Information System (INIS)
This paper presents a systemic decision support approach for safety risk analysis under uncertainty in tunnel construction. Fuzzy Bayesian Networks (FBN) is used to investigate causal relationships between tunnel-induced damage and its influential variables based upon the risk/hazard mechanism analysis. Aiming to overcome limitations on the current probability estimation, an expert confidence indicator is proposed to ensure the reliability of the surveyed data for fuzzy probability assessment of basic risk factors. A detailed fuzzy-based inference procedure is developed, which has a capacity of implementing deductive reasoning, sensitivity analysis and abductive reasoning. The “3σ criterion” is adopted to calculate the characteristic values of a triangular fuzzy number in the probability fuzzification process, and the α-weighted valuation method is adopted for defuzzification. The construction safety analysis progress is extended to the entire life cycle of risk-prone events, including the pre-accident, during-construction continuous and post-accident control. A typical hazard concerning the tunnel leakage in the construction of Wuhan Yangtze Metro Tunnel in China is presented as a case study, in order to verify the applicability of the proposed approach. The results demonstrate the feasibility of the proposed approach and its application potential. A comparison of advantages and disadvantages between FBN and fuzzy fault tree analysis (FFTA) as risk analysis tools is also conducted. The proposed approach can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment. - Highlights: • A systemic Bayesian network based approach for safety risk analysis is developed. • An expert confidence indicator for probability fuzzification is proposed. • Safety risk analysis progress is extended to entire life cycle of risk-prone events. • A typical
Risk Analysis of New Product Development Using Bayesian Networks
Directory of Open Access Journals (Sweden)
MohammadRahim Ramezanian
2012-06-01
Full Text Available The process of presenting new product development (NPD to market is of great importance due to variability of competitive rules in the business world. The product development teams face a lot of pressures due to rapid growth of technology, increased risk-taking of world markets and increasing variations in the customers` needs. However, the process of NPD is always associated with high uncertainties and complexities. To be successful in completing NPD project, existing risks should be identified and assessed. On the other hand, the Bayesian networks as a strong approach of decision making modeling of uncertain situations has attracted many researchers in various areas. These networks provide a decision supporting system for problems with uncertainties or probable reasoning. In this paper, the available risk factors in product development have been first identified in an electric company and then, the Bayesian network has been utilized and their interrelationships have been modeled to evaluate the available risk in the process. To determine the primary and conditional probabilities of the nodes, the viewpoints of experts in this area have been applied. The available risks in this process have been divided to High (H, Medium (M and Low (L groups and analyzed by the Agena Risk software. The findings derived from software output indicate that the production of the desired product has relatively high risk. In addition, Predictive support and Diagnostic support have been performed on the model with two different scenarios..
Risk Analysis of New Product Development Using Bayesian Networks
Directory of Open Access Journals (Sweden)
Mohammad Rahim Ramezanian
2012-01-01
Full Text Available The process of presenting new product development (NPD to market is of great importance due to variability of competitive rules in the business world. The product development teams face a lot of pressures due to rapid growth of technology, increased risk-taking of world markets and increasing variations in the customers` needs. However, the process of NPD is always associated with high uncertainties and complexities. To be successful in completing NPD project, existing risks should be identified and assessed. On the other hand, the Bayesian networks as a strong approach of decision making modeling of uncertain situations has attracted many researchers in various areas. These networks provide a decision supporting system for problems with uncertainties or probable reasoning. In this paper, the available risk factors in product development have been first identified in an electric company and then, the Bayesian network has been utilized and their interrelationships have been modeled to evaluate the available risk in the process. To determine the primary and conditional probabilities of the nodes, the viewpoints of experts in this area have been applied. The available risks in this process have been divided to High (H, Medium (M and Low (L groups and analyzed by the Agena Risk software. The findings derived from software output indicate that the production of the desired product has relatively high risk. In addition, Predictive support and Diagnostic support have been performed on the model with two different scenarios.
Modeling operational risks of the nuclear industry with Bayesian networks
International Nuclear Information System (INIS)
Basically, planning a new industrial plant requires information on the industrial management, regulations, site selection, definition of initial and planned capacity, and on the estimation of the potential demand. However, this is far from enough to assure the success of an industrial enterprise. Unexpected and extremely damaging events may occur that deviates from the original plan. The so-called operational risks are not only in the system, equipment, process or human (technical or managerial) failures. They are also in intentional events such as frauds and sabotage, or extreme events like terrorist attacks or radiological accidents and even on public reaction to perceived environmental or future generation impacts. For the nuclear industry, it is a challenge to identify and to assess the operational risks and their various sources. Early identification of operational risks can help in preparing contingency plans, to delay the decision to invest or to approve a project that can, at an extreme, affect the public perception of the nuclear energy. A major problem in modeling operational risk losses is the lack of internal data that are essential, for example, to apply the loss distribution approach. As an alternative, methods that consider qualitative and subjective information can be applied, for example, fuzzy logic, neural networks, system dynamic or Bayesian networks. An advantage of applying Bayesian networks to model operational risk is the possibility to include expert opinions and variables of interest, to structure the model via causal dependencies among these variables, and to specify subjective prior and conditional probabilities distributions at each step or network node. This paper suggests a classification of operational risks in industry and discusses the benefits and obstacles of the Bayesian networks approach to model those risks. (author)
Risk-Based Operation and Maintenance Using Bayesian Networks
DEFF Research Database (Denmark)
Nielsen, Jannie Jessen; Sørensen, John Dalsgaard
2011-01-01
This paper describes how risk-based decision making can be used for maintenance planning of components exposed to degradation such as fatigue in offshore wind turbines. In fatigue models, large epistemic uncertainties are usually present. These can be reduced if monitoring results are used to...... update the models, and hereby a better basis for decision making is obtained. An application example shows how a Bayesian network model can be used as a tool for updating the model and assist in risk-based decision making....
Risk analysis of dust explosion scenarios using Bayesian networks.
Yuan, Zhi; Khakzad, Nima; Khan, Faisal; Amyotte, Paul
2015-02-01
In this study, a methodology has been proposed for risk analysis of dust explosion scenarios based on Bayesian network. Our methodology also benefits from a bow-tie diagram to better represent the logical relationships existing among contributing factors and consequences of dust explosions. In this study, the risks of dust explosion scenarios are evaluated, taking into account common cause failures and dependencies among root events and possible consequences. Using a diagnostic analysis, dust particle properties, oxygen concentration, and safety training of staff are identified as the most critical root events leading to dust explosions. The probability adaptation concept is also used for sequential updating and thus learning from past dust explosion accidents, which is of great importance in dynamic risk assessment and management. We also apply the proposed methodology to a case study to model dust explosion scenarios, to estimate the envisaged risks, and to identify the vulnerable parts of the system that need additional safety measures. PMID:25264172
A Software Risk Analysis Model Using Bayesian Belief Network
Institute of Scientific and Technical Information of China (English)
Yong Hu; Juhua Chen; Mei Liu; Yang Yun; Junbiao Tang
2006-01-01
The uncertainty during the period of software project development often brings huge risks to contractors and clients. Ifwe can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project, we can reduce the risk.Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table. In this paper, we built up network structure by Delphi method for conditional probability table learning, and learn update probability table and nodes' confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately. This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.
MacGeneRisk and MacMedRisk--HyperCard programs which tutor Bayesian risk assessment.
Caster, J H
1991-01-01
Programs have been devised for the Macintosh computer which tutor medical students in the solution of risk assessment problems in human genetics and clinical test interpretation, using Bayesian probability.
Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis
Dezfuli, Homayoon; Kelly, Dana; Smith, Curtis; Vedros, Kurt; Galyean, William
2009-01-01
This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).
Development of a cyber security risk model using Bayesian networks
International Nuclear Information System (INIS)
Cyber security is an emerging safety issue in the nuclear industry, especially in the instrumentation and control (I and C) field. To address the cyber security issue systematically, a model that can be used for cyber security evaluation is required. In this work, a cyber security risk model based on a Bayesian network is suggested for evaluating cyber security for nuclear facilities in an integrated manner. The suggested model enables the evaluation of both the procedural and technical aspects of cyber security, which are related to compliance with regulatory guides and system architectures, respectively. The activity-quality analysis model was developed to evaluate how well people and/or organizations comply with the regulatory guidance associated with cyber security. The architecture analysis model was created to evaluate vulnerabilities and mitigation measures with respect to their effect on cyber security. The two models are integrated into a single model, which is called the cyber security risk model, so that cyber security can be evaluated from procedural and technical viewpoints at the same time. The model was applied to evaluate the cyber security risk of the reactor protection system (RPS) of a research reactor and to demonstrate its usefulness and feasibility. - Highlights: • We developed the cyber security risk model can be find the weak point of cyber security integrated two cyber analysis models by using Bayesian Network. • One is the activity-quality model signifies how people and/or organization comply with the cyber security regulatory guide. • Other is the architecture model represents the probability of cyber-attack on RPS architecture. • The cyber security risk model can provide evidence that is able to determine the key element for cyber security for RPS of a research reactor
Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment
Directory of Open Access Journals (Sweden)
Gregory Koshmak
2014-05-01
Full Text Available Fall incidents among the elderly often occur in the home and can cause serious injuries affecting their independent living. This paper presents an approach where data from wearable sensors integrated in a smart home environment is combined using a dynamic Bayesian network. The smart home environment provides contextual data, obtained from environmental sensors, and contributes to assessing a fall risk probability. The evaluation of the developed system is performed through simulation. Each time step is represented by a single user activity and interacts with a fall sensors located on a mobile device. A posterior probability is calculated for each recognized activity or contextual information. The output of the system provides a total risk assessment of falling given a response from the fall sensor.
Risks Analysis of Logistics Financial Business Based on Evidential Bayesian Network
Bin Suo; Ying Yan
2013-01-01
Risks in logistics financial business are identified and classified. Making the failure of the business as the root node, a Bayesian network is constructed to measure the risk levels in the business. Three importance indexes are calculated to find the most important risks in the business. And more, considering the epistemic uncertainties in the risks, evidence theory associate with Bayesian network is used as an evidential network in the risk analysis of logistics finance. To find how much un...
Bayesian network as a modelling tool for risk management in agriculture
DEFF Research Database (Denmark)
Rasmussen, Svend; Madsen, Anders L.; Lund, Mogens
this paper we use Bayesian networks as an integrated modelling approach for representing uncertainty and analysing risk management in agriculture. It is shown how historical farm account data may be efficiently used to estimate conditional probabilities, which are the core elements in Bayesian network...... models. We further show how the Bayesian network model RiBay is used for stochastic simulation of farm income, and we demonstrate how RiBay can be used to simulate risk management at the farm level. It is concluded that the key strength of a Bayesian network is the transparency of assumptions, and that......The importance of risk management increases as farmers become more exposed to risk. But risk management is a difficult topic because income risk is the result of the complex interaction of multiple risk factors combined with the effect of an increasing array of possible risk management tools. In...
Feature Selection for Bayesian Evaluation of Trauma Death Risk
Jakaite, L
2008-01-01
In the last year more than 70,000 people have been brought to the UK hospitals with serious injuries. Each time a clinician has to urgently take a patient through a screening procedure to make a reliable decision on the trauma treatment. Typically, such procedure comprises around 20 tests; however the condition of a trauma patient remains very difficult to be tested properly. What happens if these tests are ambiguously interpreted, and information about the severity of the injury will come misleading? The mistake in a decision can be fatal: using a mild treatment can put a patient at risk of dying from posttraumatic shock, while using an overtreatment can also cause death. How can we reduce the risk of the death caused by unreliable decisions? It has been shown that probabilistic reasoning, based on the Bayesian methodology of averaging over decision models, allows clinicians to evaluate the uncertainty in decision making. Based on this methodology, in this paper we aim at selecting the most important screeni...
Andrew Sanford; Imad Moosa
2015-01-01
This paper describes the development of a tool, based on a Bayesian network model, that provides posteriori predictions of operational risk events, aggregate operational loss distributions, and Operational Value-at-Risk, for a structured finance operations unit located within one of Australia's major banks. The Bayesian network, based on a previously developed causal framework, has been designed to model the smaller and more frequent, attritional operational loss events. Given the limited ava...
Bayesian network as a modelling tool for risk management in agriculture
Svend Rasmussen; Madsen, Anders L.; Mogens Lund
2013-01-01
The importance of risk management increases as farmers become more exposed to risk. But risk management is a difficult topic because income risk is the result of the complex interaction of multiple risk factors combined with the effect of an increasing array of possible risk management tools. In this paper we use Bayesian networks as an integrated modelling approach for representing uncertainty and analysing risk management in agriculture. It is shown how historical farm account data may be e...
Gerstgrasser, Matthias; Nicholls, Sarah; Stout, Michael; Smart, Katherine; Powell, Chris; Kypraios, Theodore; Stekel, Dov
2016-06-01
Biolog phenotype microarrays (PMs) enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summarizes each time-course as a single datum, so most of the information is not used. However, the time courses can be extremely varied and often contain detailed qualitative (shape of curve) and quantitative (values of parameters) information. We present a novel, Bayesian approach to estimating parameters from Phenotype Microarray data, fitting growth models using Markov Chain Monte Carlo (MCMC) methods to enable high throughput estimation of important information, including length of lag phase, maximal "growth" rate and maximum output. We find that the Baranyi model for microbial growth is useful for fitting Biolog data. Moreover, we introduce a new growth model that allows for diauxic growth with a lag phase, which is particularly useful where Phenotype Microarrays have been applied to cells grown in complex mixtures of substrates, for example in industrial or biotechnological applications, such as worts in brewing. Our approach provides more useful information from Biolog data than existing, competing methods, and allows for valuable comparisons between data series and across different models. PMID:26762475
Application of Bayesian networks for risk analysis of MV air insulated switch operation
International Nuclear Information System (INIS)
Electricity distribution companies regard risk-based approaches as a good philosophy to address their asset management challenges, and there is an increasing trend on developing methods to support decisions where different aspects of risks are taken into consideration. This paper describes a methodology for application of Bayesian networks for risk analysis in electricity distribution system maintenance management. The methodology is used on a case analysing safety risk related to operation of MV air insulated switches. The paper summarises some challenges and benefits of using Bayesian networks as a part of distribution system maintenance management.
Microbial translocation and cardiometabolic risk factors in HIV infection
DEFF Research Database (Denmark)
Trøseid, Marius; Manner, Ingjerd W; Pedersen, Karin K; Haissman, Judith M; Kvale, Dag; Nielsen, Susanne D
2014-01-01
crucial in order to tailor novel strategies for prophylaxis and treatment. This review will focus on advances in the field that possibly link HIV-induced alterations of the gut mucosa and consequent microbial translocation to cardiometabolic risk factors in HIV infection. Recent work suggests that markers...... translocation and cardiovascular risk factors will translate into increased risk of acute events, and whether strategies to target gut microbiota and microbial translocation might reduce such a risk....
Risks Analysis of Logistics Financial Business Based on Evidential Bayesian Network
Directory of Open Access Journals (Sweden)
Ying Yan
2013-01-01
Full Text Available Risks in logistics financial business are identified and classified. Making the failure of the business as the root node, a Bayesian network is constructed to measure the risk levels in the business. Three importance indexes are calculated to find the most important risks in the business. And more, considering the epistemic uncertainties in the risks, evidence theory associate with Bayesian network is used as an evidential network in the risk analysis of logistics finance. To find how much uncertainty in root node is produced by each risk, a new index, epistemic importance, is defined. Numerical examples show that the proposed methods could provide a lot of useful information. With the information, effective approaches could be found to control and avoid these sensitive risks, thus keep logistics financial business working more reliable. The proposed method also gives a quantitative measure of risk levels in logistics financial business, which provides guidance for the selection of financing solutions.
Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network.
Li, Shancang; Tryfonas, Theo; Russell, Gordon; Andriotis, Panagiotis
2016-08-01
Mobile systems are facing a number of application vulnerabilities that can be combined together and utilized to penetrate systems with devastating impact. When assessing the overall security of a mobile system, it is important to assess the security risks posed by each mobile applications (apps), thus gaining a stronger understanding of any vulnerabilities present. This paper aims at developing a three-layer framework that assesses the potential risks which apps introduce within the Android mobile systems. A Bayesian risk graphical model is proposed to evaluate risk propagation in a layered risk architecture. By integrating static analysis, dynamic analysis, and behavior analysis in a hierarchical framework, the risks and their propagation through each layer are well modeled by the Bayesian risk graph, which can quantitatively analyze risks faced to both apps and mobile systems. The proposed hierarchical Bayesian risk graph model offers a novel way to investigate the security risks in mobile environment and enables users and administrators to evaluate the potential risks. This strategy allows to strengthen both app security as well as the security of the entire system. PMID:27076477
Wen, Yen-Hsia; Wu, Shihn-Sheng; Lin, Chun-Hung Richard; Tsai, Jui-Hsiu; Yang, Pinchen; Chang, Yang-Pei; Tseng, Kuan-Hua
2016-05-01
Dementia is one of the most disabling and burdensome health conditions worldwide. In this study, we identified new potential risk factors for dementia from nationwide longitudinal population-based data by using Bayesian statistics.We first tested the consistency of the results obtained using Bayesian statistics with those obtained using classical frequentist probability for 4 recognized risk factors for dementia, namely severe head injury, depression, diabetes mellitus, and vascular diseases. Then, we used Bayesian statistics to verify 2 new potential risk factors for dementia, namely hearing loss and senile cataract, determined from the Taiwan's National Health Insurance Research Database.We included a total of 6546 (6.0%) patients diagnosed with dementia. We observed older age, female sex, and lower income as independent risk factors for dementia. Moreover, we verified the 4 recognized risk factors for dementia in the older Taiwanese population; their odds ratios (ORs) ranged from 3.469 to 1.207. Furthermore, we observed that hearing loss (OR = 1.577) and senile cataract (OR = 1.549) were associated with an increased risk of dementia.We found that the results obtained using Bayesian statistics for assessing risk factors for dementia, such as head injury, depression, DM, and vascular diseases, were consistent with those obtained using classical frequentist probability. Moreover, hearing loss and senile cataract were found to be potential risk factors for dementia in the older Taiwanese population. Bayesian statistics could help clinicians explore other potential risk factors for dementia and for developing appropriate treatment strategies for these patients. PMID:27227925
Bayesian model for strategic level risk assessment in continuing airthworthiness of air transport
Jayakody-Arachchige, Dhanapala
2010-01-01
Continuing airworthiness (CAW) of aircraft is an essential pre-requisite for the safe operation of air transport. Human errors that occur in CAW organizations and processes could undermine the airworthiness and constitute a risk to flight safety. This thesis reports on a generic Bayesian model that has been designed to assess and quantify this risk. The model removes the vagueness inherent in the subjective methods of assessment of risk and its qualitative expression. Instead, relying on a...
Modelling macroeconomic e ects and expert judgements in operational risk : a Bayesian approach
Capa Santos, Holger; Kratz, Marie; Mosquera Munoz, Franklin
2012-01-01
This work presents a contribution on operational risk under a general Bayesian context incorporating information on market risk pro le, experts and operational losses, taking into account the general macroeconomic environment as well. It aims at estimating a characteristic parameter of the distributions of the sources, market risk pro le, experts and operational losses, chosen here at a location parameter. It generalizes under more realistic conditions a study realized by Lambrigger, Shevchen...
Use of limited data to construct Bayesian networks for probabilistic risk assessment.
Energy Technology Data Exchange (ETDEWEB)
Groth, Katrina M.; Swiler, Laura Painton
2013-03-01
Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was to establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.
Risk-based design of process systems using discrete-time Bayesian networks
International Nuclear Information System (INIS)
Temporal Bayesian networks have gained popularity as a robust technique to model dynamic systems in which the components' sequential dependency, as well as their functional dependency, cannot be ignored. In this regard, discrete-time Bayesian networks have been proposed as a viable alternative to solve dynamic fault trees without resort to Markov chains. This approach overcomes the drawbacks of Markov chains such as the state-space explosion and the error-prone conversion procedure from dynamic fault tree. It also benefits from the inherent advantages of Bayesian networks such as probability updating. However, effective mapping of the dynamic gates of dynamic fault trees into Bayesian networks while avoiding the consequent huge multi-dimensional probability tables has always been a matter of concern. In this paper, a new general formalism has been developed to model two important elements of dynamic fault tree, i.e., cold spare gate and sequential enforcing gate, with any arbitrary probability distribution functions. Also, an innovative Neutral Dependency algorithm has been introduced to model dynamic gates such as priority-AND gate, thus reducing the dimension of conditional probability tables by an order of magnitude. The second part of the paper is devoted to the application of discrete-time Bayesian networks in the risk assessment and safety analysis of complex process systems. It has been shown how dynamic techniques can effectively be applied for optimal allocation of safety systems to obtain maximum risk reduction.
A decision‐making framework for flood risk management based on a Bayesian Influence Diagram
DEFF Research Database (Denmark)
Åstrøm, Helena Lisa Alexandra; Madsen, Henrik; Friis-Hansen, Peter;
2014-01-01
We develop a Bayesian Influence Diagram (ID) approach for risk‐based decision‐ making in flood management. We show that it is a flexible decision‐making tool to assess flood risk in a non‐stationary environment and with an ability to test different adaptation measures in order to agree on the best...
Risk-Based Operation and Maintenance of Offshore Wind Turbines using Bayesian Networks
DEFF Research Database (Denmark)
Nielsen, Jannie Jessen; Sørensen, John Dalsgaard
2011-01-01
For offshore wind farms, the costs due to operation and maintenance are large, and more optimal planning has the potential of reducing these costs. This paper presents how Bayesian networks can be used for risk-based inspection planning, where the inspection plans are updated each year through the...
Risk analysis of drinking water microbial contamination versus disinfection by-products (DBPs)
International Nuclear Information System (INIS)
Managing the provision of safe drinking water has a renewed focus in light of the new World Health Organization (WHO) water safety plans. Risk analysis is a necessary component to assist in selecting priority hazards and identifying hazardous scenarios, be they qualitative to quantitative assessments. For any approach, acute diarrhoeal pathogens are often the higher risk issue for municipal water supplies, no matter how health burden is assessed. Furthermore, potential sequellae (myocarditis, diabetes, reactive arthritis and cancers) only further increase the potential health burden of pathogens; despite the enormous uncertainties in determining pathogen exposures and chemical dose-responses within respective microbial and chemical analyses. These interpretations are currently being improved by Bayesian and bootstrapping approaches to estimate parameters for stochastic assessments. A case example, covering the health benefits of ozonation for Cryptosporidium inactivation versus potential cancers from bromate exposures, illustrated the higher risks from a pathogen than one of the most likely disinfection by-products (DBPs). Such analyses help justify the industries long-held view of the benefits of multiple barriers to hazards and that microbial contamination of water supplies pose a clear public health risk when treatment is inadequate. Therefore, efforts to reduce potential health risks from DBP must not compromise pathogen control, despite socio-political issues
A BAYESIAN NETWORKS APPROACH TO MODELING FINANCIAL RISKS OF E-LOGISTICS INVESTMENTS
CHIEN-WEN SHEN
2009-01-01
To evaluate whether the investments of e-logistics systems may increase financial risks, models of Bayesian networks are constructed in this study with the mechanism of structural learning and parameter learning. Empirical findings from the transport and logistics sectors suggest that the e-logistics investments generally do not increase the financial risks of companies except the implementation of computer aided picking systems and radio frequency identification. Meanwhile, only the investme...
Research on Risk Manage of Power Construction Project Based on Bayesian Network
Jia, Zhengyuan; Fan, Zhou; Li, Yong
With China's changing economic structure and increasingly fierce competition in the market, the uncertainty and risk factors in the projects of electric power construction are increasingly complex, the projects will face huge risks or even fail if we don't consider or ignore these risk factors. Therefore, risk management in the projects of electric power construction plays an important role. The paper emphatically elaborated the influence of cost risk in electric power projects through study overall risk management and the behavior of individual in risk management, and introduced the Bayesian network to the project risk management. The paper obtained the order of key factors according to both scene analysis and causal analysis for effective risk management.
Microbial Risk and Control During Long Duration Space Exploration Missions
Ott, C. Mark
2016-01-01
As human explore space, they will be accompanied by microorganisms. Historically, the approach to microbial control on spacecraft has been to minimize the number of detectable organisms, relying heavily on preventative measures, including appropriate vehicle design, crew quarantine prior to flight, and extensive microbial monitoring. Preflight monitoring targets have included the astronauts, spaceflight foods, potable water systems, the vehicle air and surfaces, and the cargo carried aboard the spacecraft. This approach has been very successful for short duration missions; however, habitation of the International Space Station (ISS) has reinforced the importance of inflight microbial monitoring. Current ISS inflight monitoring evaluates potable water sources and vehicle air and surfaces. The hardware uses media-based growth and is designed for microbial enumeration during spaceflight followed by microbial identification after return of samples to Earth. For future missions beyond Earth orbit, microbial monitoring capabilities will need to be improved to enable rapid inflight identification of viable microorganisms. Inflight monitoring may also need to be expanded beyond ISS targets to include food grown in the habitat, food preparation areas, and/or clinical samples. Establishing requirements for many of these samples remains a major gap in implementing this type of next-generation approach to risk assessment and control. An additional consideration in assessment of microbial risk is research that indicates that the spaceflight environment alters crewmember immune function, microbial diversity, and molecular-genetic and phenotypic responses of microorganism, including virulence and antibiotic resistance. Taken together, in preparation for long duration space exploration missions, NASA faces new challenges in microbial risk assessment, monitoring and control that must be understood and addressed to mitigate threats to crew health and mission success.
Software Delivery Risk Management: Application of Bayesian Networks in Agile Software Development
Directory of Open Access Journals (Sweden)
Ancveire Ieva
2015-12-01
Full Text Available The information technology industry cannot be imagined without large- or small-scale projects. They are implemented to develop systems enabling key business processes and improving performance and enterprise resource management. However, projects often experience various difficulties during their execution. These problems are usually related to the three objectives of the project – costs, quality and deadline. A way these challenges can be solved is project risk management. However, not always the main problems and their influencing factors can be easily identified. Usually there is a need for a more profound analysis of the problem situation. In this paper, we propose the use of a Bayesian Network concept for quantitative risk management in agile projects. The Bayesian Network is explored using a case study focusing on a project that faces difficulties during the software delivery process. We explain why an agile risk analysis is needed and assess the potential risk factors, which may occur during the project. Thereafter, we design the Bayesian Network to capture the actual problem situation and make suggestions how to improve the delivery process based on the measures to be taken to reduce the occurrence of project risks.
Bayesian Safety Risk Modeling of Human-Flightdeck Automation Interaction
Ancel, Ersin; Shih, Ann T.
2015-01-01
Usage of automatic systems in airliners has increased fuel efficiency, added extra capabilities, enhanced safety and reliability, as well as provide improved passenger comfort since its introduction in the late 80's. However, original automation benefits, including reduced flight crew workload, human errors or training requirements, were not achieved as originally expected. Instead, automation introduced new failure modes, redistributed, and sometimes increased workload, brought in new cognitive and attention demands, and increased training requirements. Modern airliners have numerous flight modes, providing more flexibility (and inherently more complexity) to the flight crew. However, the price to pay for the increased flexibility is the need for increased mode awareness, as well as the need to supervise, understand, and predict automated system behavior. Also, over-reliance on automation is linked to manual flight skill degradation and complacency in commercial pilots. As a result, recent accidents involving human errors are often caused by the interactions between humans and the automated systems (e.g., the breakdown in man-machine coordination), deteriorated manual flying skills, and/or loss of situational awareness due to heavy dependence on automated systems. This paper describes the development of the increased complexity and reliance on automation baseline model, named FLAP for FLightdeck Automation Problems. The model development process starts with a comprehensive literature review followed by the construction of a framework comprised of high-level causal factors leading to an automation-related flight anomaly. The framework was then converted into a Bayesian Belief Network (BBN) using the Hugin Software v7.8. The effects of automation on flight crew are incorporated into the model, including flight skill degradation, increased cognitive demand and training requirements along with their interactions. Besides flight crew deficiencies, automation system
Product Innovation Risk Management based on Bayesian Decision Theory
Yingchun Guo
2012-01-01
Innovation is an inexhaustible force for the prosperity of one nation, and also the life source of enterprises. Product innovation is an important aspect of innovation. However, the product innovation activities has high-risk characteristics. Enterprises have to perform scientific and effective product innovation risk management. Based on a general introduction of Bayestian Decision Theory principle, the author studied the practices of product innovation in enterprises. The paper discussed ho...
Microbial ecology of terrestrial Antarctica: Are microbial systems at risk from human activities?
Energy Technology Data Exchange (ETDEWEB)
White, G.J.
1996-08-01
Many of the ecological systems found in continental Antarctica are comprised entirely of microbial species. Concerns have arisen that these microbial systems might be at risk either directly through the actions of humans or indirectly through increased competition from introduced species. Although protection of native biota is covered by the Protocol on Environmental Protection to the Antarctic Treaty, strict measures for preventing the introduction on non-native species or for protecting microbial habitats may be impractical. This report summarizes the research conducted to date on microbial ecosystems in continental Antarctica and discusses the need for protecting these ecosystems. The focus is on communities inhabiting soil and rock surfaces in non-coastal areas of continental Antarctica. Although current polices regarding waste management and other operations in Antarctic research stations serve to reduce the introduction on non- native microbial species, importation cannot be eliminated entirely. Increased awareness of microbial habitats by field personnel and protection of certain unique habitats from physical destruction by humans may be necessary. At present, small-scale impacts from human activities are occurring in certain areas both in terms of introduced species and destruction of habitat. On a large scale, however, it is questionable whether the introduction of non-native microbial species to terrestrial Antarctica merits concern.
Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal
International Nuclear Information System (INIS)
The scenario in a risk analysis can be defined as the propagating feature of specific initiating event which can go to a wide range of undesirable consequences. If we take various scenarios into consideration, the risk analysis becomes more complex than do without them. A lot of risk analyses have been performed to actually estimate a risk profile under both uncertain future states of hazard sources and undesirable scenarios. Unfortunately, in case of considering specific systems such as a radioactive waste disposal facility, since the behaviour of future scenarios is hardly predicted without special reasoning process, we cannot estimate their risk only with a traditional risk analysis methodology. Moreover, we believe that the sources of uncertainty at future states can be reduced pertinently by setting up dependency relationships interrelating geological, hydrological, and ecological aspects of the site with all the scenarios. It is then required current methodology of uncertainty analysis of the waste disposal facility be revisited under this belief. In order to consider the effects predicting from an evolution of environmental conditions of waste disposal facilities, this paper proposes a quantitative assessment framework integrating the inference process of Bayesian network to the traditional probabilistic risk analysis. We developed and verified an approximate probabilistic inference program for the specific Bayesian network using a bounded-variance likelihood weighting algorithm. Ultimately, specific models, including a model for uncertainty propagation of relevant parameters were developed with a comparison of variable-specific effects due to the occurrence of diverse altered evolution scenarios (AESs). After providing supporting information to get a variety of quantitative expectations about the dependency relationship between domain variables and AESs, we could connect the results of probabilistic inference from the Bayesian network with the consequence
A Bayesian Network Methodology for Infrastructure Seismic Risk Assessment and Decision Support
Bensi, Michelle Terese
2010-01-01
A Bayesian network methodology is developed for performing infrastructure seismic risk assessment and providing decision support with an emphasis on immediate post-earthquake applications. The methodology consists of four major components: (1) a seismic demand model of ground motion intensity as a spatially distributed Gaussian random field accounting for multiple seismic sources with uncertain characteristics and including finite fault rupture and directivity effects; (2) a model of the perf...
Weinstein, Lawrence; Radano, Todd A; Jack, Timothy; Kalina, Philip; Eberhardt, John S.
2009-01-01
Introduction: This paper explores the use of machine learning and Bayesian classification models to develop broadly applicable risk stratification models to guide disease management of health plan enrollees with substance use disorder (SUD). While the high costs and morbidities associated with SUD are understood by payers, who manage it through utilization review, acute interventions, coverage and cost limitations, and disease management, the literature shows mixed results for these modalitie...
Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula
Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.
2016-03-01
A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.
Dynamic Bayesian modeling for risk prediction in credit operations
DEFF Research Database (Denmark)
Borchani, Hanen; Fernandez, Ana Maria Martinez; Masegosa, Andres;
2015-01-01
Our goal is to do risk prediction in credit operations, and as data is collected continuously and reported on a monthly basis, this gives rise to a streaming data classification problem. Our analysis reveals some practical problems that have not previously been thoroughly analyzed in the context of...
Navarrete, Gorka; Correia, Rut; Sirota, Miroslav; Juanchich, Marie; Huepe, David
2015-01-01
Most of the research on Bayesian reasoning aims to answer theoretical questions about the extent to which people are able to update their beliefs according to Bayes' Theorem, about the evolutionary nature of Bayesian inference, or about the role of cognitive abilities in Bayesian inference. Few studies aim to answer practical, mainly health-related questions, such as, "What does it mean to have a positive test in a context of cancer screening?" or "What is the best way to communicate a medical test result so a patient will understand it?". This type of research aims to translate empirical findings into effective ways of providing risk information. In addition, the applied research often adopts the paradigms and methods of the theoretically-motivated research. But sometimes it works the other way around, and the theoretical research borrows the importance of the practical question in the medical context. The study of Bayesian reasoning is relevant to risk communication in that, to be as useful as possible, applied research should employ specifically tailored methods and contexts specific to the recipients of the risk information. In this paper, we concentrate on the communication of the result of medical tests and outline the epidemiological and test parameters that affect the predictive power of a test-whether it is correct or not. Building on this, we draw up recommendations for better practice to convey the results of medical tests that could inform health policy makers (What are the drawbacks of mass screenings?), be used by health practitioners and, in turn, help patients to make better and more informed decisions. PMID:26441711
Directory of Open Access Journals (Sweden)
Gorka eNavarrete
2015-09-01
Full Text Available Most of the research on Bayesian reasoning aims to answer theoretical questions about the extent to which people are able to update their beliefs according to the Bayes Theorem (Baratgin & Politzer, 2006; Barbey & Sloman, 2007; Gigerenzer & Hoffrage, 1995 about the evolutionary nature of Bayesian inference (Brase, 2002, 2007; Gigerenzer & Hoffrage, 1995, or about the role of cognitive abilities in Bayesian inference (Johnson & Tubau, 2013; Lesage, Navarrete, & De Neys, 2013; Sirota, Juanchich, & Hagmayer, 2014. Few studies aim to answer practical, mainly health-related questions, such as, questions such as ‘What does it mean to have a positive test in a context of cancer screening?’ or ‘What is the best way to communicate a medical test result so a patient will understand it?. This type of research aims to translate the empirical finding into effective ways of providing risk information. In addition, the applied research often adopts the paradigms and methods of the theoretically-motivated research. But sometimes it works the other way around, and the theoretical research borrows the importance of the practical question in the medical context. The study of Bayesian reasoning is relevant to risk communication in that,, to be as useful as possible, applied research should employ specifically tailored methods and contexts specific to the recipients of the risk information. In this paper, we concentrate on the communication of the result of medical tests and outline the epidemiological and test parameters that affect the predictive power of a test – whether it is correct or not. Building on this, we draw up recommendations for better practice to convey the results of medical tests that could inform health policy makers (e.g. what are the drawbacks of mass screenings?, be used by health practitioners and, in turn, help patients to make better and more informed decisions.
A new approach for supply chain risk management: Mapping SCOR into Bayesian network
Directory of Open Access Journals (Sweden)
Mahdi Abolghasemi
2015-01-01
Full Text Available Purpose: Increase of costs and complexities in organizations beside the increase of uncertainty and risks have led the managers to use the risk management in order to decrease risk taking and deviation from goals. SCRM has a close relationship with supply chain performance. During the years different methods have been used by researchers in order to manage supply chain risk but most of them are either qualitative or quantitative. Supply chain operation reference (SCOR is a standard model for SCP evaluation which have uncertainty in its metrics. In This paper by combining qualitative and quantitative metrics of SCOR, supply chain performance will be measured by Bayesian Networks. Design/methodology/approach: First qualitative assessment will be done by recognizing uncertain metrics of SCOR model and then by quantifying them, supply chain performance will be measured by Bayesian Networks (BNs and supply chain operations reference (SCOR in which making decision on uncertain variables will be done by predictive and diagnostic capabilities. Findings: After applying the proposed method in one of the biggest automotive companies in Iran, we identified key factors of supply chain performance based on SCOR model through predictive and diagnostic capability of Bayesian Networks. After sensitivity analysis, we find out that ‘Total cost’ and its criteria that include costs of labors, warranty, transportation and inventory have the widest range and most effect on supply chain performance. So, managers should take their importance into account for decision making. We can make decisions simply by running model in different situations. Research limitations/implications: A more precise model consisted of numerous factors but it is difficult and sometimes impossible to solve big models, if we insert all of them in a Bayesian model. We have adopted real world characteristics with our software and method abilities. On the other hand, fewer data exist for some
Impact of microbial count distributions on human health risk estimates
DEFF Research Database (Denmark)
Ribeiro Duarte, Ana Sofia; Nauta, Maarten
2015-01-01
Quantitative microbiological risk assessment (QMRA) is influenced by the choice of the probability distribution used to describe pathogen concentrations, as this may eventually have a large effect on the distribution of doses at exposure. When fitting a probability distribution to microbial...... enumeration data, several factors may have an impact on the accuracy of that fit. Analysis of the best statistical fits of different distributions alone does not provide a clear indication of the impact in terms of risk estimates. Thus, in this study we focus on the impact of fitting microbial distributions...... on risk estimates, at two different concentration scenarios and at a range of prevalence levels. By using five different parametric distributions, we investigate whether different characteristics of a good fit are crucial for an accurate risk estimate. Among the factors studied are the importance of...
Bayesian Approach for Flexible Modeling of Semicompeting Risks Data
Han, Baoguang; Yu, Menggang; Dignam, James J.; Rathouz, Paul J.
2014-01-01
Semicompeting risks data arise when two types of events, non-terminal and terminal, are observed. When the terminal event occurs first, it censors the non-terminal event, but not vice versa. To account for possible dependent censoring of the non-terminal event by the terminal event and to improve prediction of the terminal event using the non-terminal event information, it is crucial to model their association properly. Motivated by a breast cancer clinical trial data analysis, we extend the ...
A Bayesian Confidence Interval for Value-at-Risk
Contreras, Patricio; Bhattacharjee, Arnab
2003-01-01
This study assesses the accuracy of the value-at-risk estimate (VaR). On the basis of posterior distributions of the unknown population parameters, we develop a confidence interval for VaR that reflects the genuine information available about the portfolios for which the VaR is calculated. This approach is more accurate than that in Dowd (2000) as it avoids explaining the behaviour of the population parameters on the basis of distributions of sample parameters. We find that the accurac...
International Nuclear Information System (INIS)
The sound development of marine resource usage relies on a strong maritime engineering industry. The perilous marine environment poses the highest risk to all maritime work. It is therefore imperative to reduce the risk associated with maritime work by using some analytical methods other than engineering techniques. This study addresses this issue by using an integrated interpretive structure modeling (ISM) and Bayesian network (BN) approach in a risk assessment context. Mitigating or managing maritime risk relies primarily on domain expert experience and knowledge. ISM can be used to incorporate expert knowledge in a systematic manner and helps to impose order and direction on complex relationships that exist among system elements. Working with experts, this research used ISM to clearly specify an engineering risk factor relationship represented by a cause–effect diagram, which forms the structure of the BN. The expert subjective judgments were further transformed into a prior and conditional probability set to be embedded in the BN. We used the BN to evaluate the risks of two offshore pipeline projects in Taiwan. The results indicated that the BN can provide explicit risk information to support better project management. - Highlights: • We adopt an integrated method for risk assessment of offshore pipeline projects. • We conduct semi-structural interview with the experts for risk factor identification. • Interpretive structural modeling helps to form the digraph of Bayesian network (BN) • We perform the risk analysis with the experts by building a BN. • Risk evaluations of two case studies using the BN show effectiveness of the methods
Bayesian estimation of covariance matrices: Application to market risk management at EDF
International Nuclear Information System (INIS)
In this thesis, we develop new methods of regularized covariance matrix estimation, under the Bayesian setting. The regularization methodology employed is first related to shrinkage. We investigate a new Bayesian modeling of covariance matrix, based on hierarchical inverse-Wishart distribution, and then derive different estimators under standard loss functions. Comparisons between shrunk and empirical estimators are performed in terms of frequentist performance under different losses. It allows us to highlight the critical importance of the definition of cost function and show the persistent effect of the shrinkage-type prior on inference. In a second time, we consider the problem of covariance matrix estimation in Gaussian graphical models. If the issue is well treated for the decomposable case, it is not the case if you also consider non-decomposable graphs. We then describe a Bayesian and operational methodology to carry out the estimation of covariance matrix of Gaussian graphical models, decomposable or not. This procedure is based on a new and objective method of graphical-model selection, combined with a constrained and regularized estimation of the covariance matrix of the model chosen. The procedures studied effectively manage missing data. These estimation techniques were applied to calculate the covariance matrices involved in the market risk management for portfolios of EDF (Electricity of France), in particular for problems of calculating Value-at-Risk or in Asset Liability Management. (author)
Modelling microbial health risk of wastewater reuse: A systems perspective.
Beaudequin, Denise; Harden, Fiona; Roiko, Anne; Stratton, Helen; Lemckert, Charles; Mengersen, Kerrie
2015-11-01
There is a widespread need for the use of quantitative microbial risk assessment (QMRA) to determine reclaimed water quality for specific uses, however neither faecal indicator levels nor pathogen concentrations alone are adequate for assessing exposure health risk. The aim of this study was to build a conceptual model representing factors contributing to the microbiological health risks of reusing water treated in maturation ponds. This paper describes the development of an unparameterised model that provides a visual representation of theoretical constructs and variables of interest. Information was collected from the peer-reviewed literature and through consultation with experts from regulatory authorities and academic disciplines. In this paper we explore how, considering microbial risk as a modular system, following the QMRA framework enables incorporation of the many factors influencing human exposure and dose response, to better characterise likely human health impacts. By using and expanding upon the QMRA framework we deliver new insights into this important field of environmental exposures. We present a conceptual model of health risk of microbial exposure which can be used for maturation ponds and, more importantly, as a generic tool to assess health risk in diverse wastewater reuse scenarios. PMID:26277638
Capturing changes in flood risk with Bayesian approaches for flood damage assessment
Vogel, Kristin; Schröter, Kai; Kreibich, Heidi; Thieken, Annegret; Müller, Meike; Sieg, Tobias; Laudan, Jonas; Kienzler, Sarah; Weise, Laura; Merz, Bruno; Scherbaum, Frank
2016-04-01
Flood risk is a function of hazard as well as of exposure and vulnerability. All three components are under change over space and time and have to be considered for reliable damage estimations and risk analyses, since this is the basis for an efficient, adaptable risk management. Hitherto, models for estimating flood damage are comparatively simple and cannot sufficiently account for changing conditions. The Bayesian network approach allows for a multivariate modeling of complex systems without relying on expert knowledge about physical constraints. In a Bayesian network each model component is considered to be a random variable. The way of interactions between those variables can be learned from observations or be defined by expert knowledge. Even a combination of both is possible. Moreover, the probabilistic framework captures uncertainties related to the prediction and provides a probability distribution for the damage instead of a point estimate. The graphical representation of Bayesian networks helps to study the change of probabilities for changing circumstances and may thus simplify the communication between scientists and public authorities. In the framework of the DFG-Research Training Group "NatRiskChange" we aim to develop Bayesian networks for flood damage and vulnerability assessments of residential buildings and companies under changing conditions. A Bayesian network learned from data, collected over the last 15 years in flooded regions in the Elbe and Danube catchments (Germany), reveals the impact of many variables like building characteristics, precaution and warning situation on flood damage to residential buildings. While the handling of incomplete and hybrid (discrete mixed with continuous) data are the most challenging issues in the study on residential buildings, a similar study, that focuses on the vulnerability of small to medium sized companies, bears new challenges. Relying on a much smaller data set for the determination of the model
An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit
Wong, Rowena Syn Yin; Ismail, Noor Azina
2016-01-01
Background and Objectives There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU. Methods This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV) model. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method. Results The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS) was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC) values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05) for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study. Conclusion Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of
Risk analysis of emergent water pollution accidents based on a Bayesian Network.
Tang, Caihong; Yi, Yujun; Yang, Zhifeng; Sun, Jie
2016-01-01
To guarantee the security of water quality in water transfer channels, especially in open channels, analysis of potential emergent pollution sources in the water transfer process is critical. It is also indispensable for forewarnings and protection from emergent pollution accidents. Bridges above open channels with large amounts of truck traffic are the main locations where emergent accidents could occur. A Bayesian Network model, which consists of six root nodes and three middle layer nodes, was developed in this paper, and was employed to identify the possibility of potential pollution risk. Dianbei Bridge is reviewed as a typical bridge on an open channel of the Middle Route of the South to North Water Transfer Project where emergent traffic accidents could occur. Risk of water pollutions caused by leakage of pollutants into water is focused in this study. The risk for potential traffic accidents at the Dianbei Bridge implies a risk for water pollution in the canal. Based on survey data, statistical analysis, and domain specialist knowledge, a Bayesian Network model was established. The human factor of emergent accidents has been considered in this model. Additionally, this model has been employed to describe the probability of accidents and the risk level. The sensitive reasons for pollution accidents have been deduced. The case has also been simulated that sensitive factors are in a state of most likely to lead to accidents. PMID:26433361
A Bayesian Approach to Risk Informed Performance Based Regulation for Digital I and C QA Programs
International Nuclear Information System (INIS)
The purpose of applying Risk Informed Performance Based Regulation (RIPBR) is to reduce unnecessary conservatism existed in current regulations. This paper proposes a systematic way to find such unnecessary conservatism based on Bayesian Belief Network (BBN) modeling technique. First, a Bayesian based QA process model is developed, and the correspondent event tree based on the BBN is then derived. Risk insight into different QA activities can thus be investigated by comparing their contribution to final quality to determine their necessity. Independent V and V, prescribed by RG 1.168, is selected as a case study to demonstrate the effectiveness of this approach. The proposed Bayesian approach appears to be very promising in supporting the RIPBR practice for digital I and C QA programs. Related issues and future work are also discussed. It is a consensus view between licensees and regulators that there may exists unnecessary conservatism in current digital I and C QA regulatory requirements. If such conservatism can be identified and reduced then the limited resources of both licensees and regulators can be utilized more effectively. The goal of RIPBR promoted by USNRC is to provide a generic regulatory framework to eliminate such conservatism in all NRC's regulatory activities (NRC, 1995). However, in order to take the advantage of RIPBR, one needs to develop techniques to identify unnecessary conservatism, and such techniques have not been fully established for digital I and C systems yet. This paper proposed a Bayesian-based approach to identifying unnecessary conservatism in current digital I and C QA program requirements. A QA program causal influence model is developed first, and then a correspondent event tree enumerating potential scenarios is derived based on this model. Thus risk insight into different QA activities can be investigated by comparing their contribution to scenario results. The QA activities that do not have significant impact on results
International Nuclear Information System (INIS)
Formal safety assessment (FSA), as a structured and systematic risk evaluation methodology, has been increasingly and broadly used in the shipping industry around the world. Concerns have been raised as to navigational safety of the Yangtze River, China's largest and the world's busiest inland waterway. Over the last few decades, the throughput of ships in the Yangtze River has increased rapidly due to the national development of the Middle and Western parts of China. Accidents such as collisions, groundings, contacts, oil-spills and fires occur repeatedly, often causing serious consequences. In order to improve the navigational safety in the Yangtze River, this paper estimates the navigational risk of the Yangtze River using the FSA concept and a Bayesian network (BN) technique. The navigational risk model is established by considering both probability and consequences of accidents with respect to a risk matrix method, followed by a scenario analysis to demonstrate the application of the proposed model
Directory of Open Access Journals (Sweden)
Hongqiang Liu
2016-06-01
Full Text Available A Bayesian random effects modeling approach was used to examine the influence of neighborhood characteristics on burglary risks in Jianghan District, Wuhan, China. This random effects model is essentially spatial; a spatially structured random effects term and an unstructured random effects term are added to the traditional non-spatial Poisson regression model. Based on social disorganization and routine activity theories, five covariates extracted from the available data at the neighborhood level were used in the modeling. Three regression models were fitted and compared by the deviance information criterion to identify which model best fit our data. A comparison of the results from the three models indicates that the Bayesian random effects model is superior to the non-spatial models in fitting the data and estimating regression coefficients. Our results also show that neighborhoods with above average bar density and department store density have higher burglary risks. Neighborhood-specific burglary risks and posterior probabilities of neighborhoods having a burglary risk greater than 1.0 were mapped, indicating the neighborhoods that should warrant more attention and be prioritized for crime intervention and reduction. Implications and limitations of the study are discussed in our concluding section.
Bayesian risk-based decision method for model validation under uncertainty
International Nuclear Information System (INIS)
This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model. A Bayesian decision theory-based method is developed for this purpose, using a likelihood ratio as the validation metric for model assessment. An expected risk or cost function is defined as a function of the decision costs, and the likelihood and prior of each hypothesis. The risk is minimized through correctly assigning experimental data to two decision regions based on the comparison of the likelihood ratio with a decision threshold. A Bayesian validation metric is derived based on the risk minimization criterion. Two types of validation tests are considered: pass/fail tests and system response value measurement tests. The methodology is illustrated for the validation of reliability prediction models in a tension bar and an engine blade subjected to high cycle fatigue. The proposed method can effectively integrate optimal experimental design into model validation to simultaneously reduce the cost and improve the accuracy of reliability model assessment
Managing Microbial Risks from Indirect Wastewater Reuse for Irrigation in Urbanizing Watersheds.
Verbyla, Matthew E; Symonds, Erin M; Kafle, Ram C; Cairns, Maryann R; Iriarte, Mercedes; Mercado Guzmán, Alvaro; Coronado, Olver; Breitbart, Mya; Ledo, Carmen; Mihelcic, James R
2016-07-01
Limited supply of clean water in urbanizing watersheds creates challenges for safely sustaining irrigated agriculture and global food security. On-farm interventions, such as riverbank filtration (RBF), are used in developing countries to treat irrigation water from rivers with extensive fecal contamination. Using a Bayesian approach incorporating ethnographic data and pathogen measurements, quantitative microbial risk assessment (QMRA) methods were employed to assess the impact of RBF on consumer health burdens for Giardia, Cryptosporidium, rotavirus, norovirus, and adenovirus infections resulting from indirect wastewater reuse, with lettuce irrigation in Bolivia as a model system. Concentrations of the microbial source tracking markers pepper mild mottle virus and HF183 Bacteroides were respectively 2.9 and 5.5 log10 units lower in RBF-treated water than in the river water. Consumption of lettuce irrigated with river water caused an estimated median health burden that represents 37% of Bolivia's overall diarrheal disease burden, but RBF resulted in an estimated health burden that is only 1.1% of this overall diarrheal disease burden. Variability and uncertainty associated with environmental and cultural factors affecting exposure correlated more with QMRA-predicted health outcomes than factors related to disease vulnerability. Policies governing simple on-farm interventions like RBF can be intermediary solutions for communities in urbanizing watersheds that currently lack wastewater treatment. PMID:26992352
International Nuclear Information System (INIS)
The scenario in a risk analysis can be defined as the propagating feature of specific initiating event which can go to a wide range of undesirable consequences. If one takes various scenarios into consideration, the risk analysis becomes more complex than do without them. A lot of risk analyses have been performed to actually estimate a risk profile under both uncertain future states of hazard sources and undesirable scenarios. Unfortunately, in case of considering some stochastic passive systems such as a radioactive waste disposal facility, since the behaviour of future scenarios is hardly predicted without special reasoning process, we cannot estimate their risk only with a traditional risk analysis methodology. Moreover, it is believed that the sources of uncertainty at future states can be reduced pertinently by setting up dependency relationships interrelating geological, hydrological, and ecological aspects of the site with all the scenarios. It is then required current methodology of uncertainty analysis of the waste disposal facility be revisited under this belief. In order to consider the effects predicting from an evolution of environmental conditions of waste disposal facilities, this study proposes a quantitative assessment framework integrating the inference process of Bayesian network to the traditional probabilistic risk analysis. In this study an approximate probabilistic inference program for the specific Bayesian network developed and verified using a bounded-variance likelihood weighting algorithm. Ultimately, specific models, including a Monte-Carlo model for uncertainty propagation of relevant parameters, were developed with a comparison of variable-specific effects due to the occurrence of diverse altered evolution scenarios (AESs). After providing supporting information to get a variety of quantitative expectations about the dependency relationship between domain variables and AESs, this study could connect the results of probabilistic
Gut Microbial Metabolite TMAO Enhances Platelet Hyperreactivity and Thrombosis Risk.
Zhu, Weifei; Gregory, Jill C; Org, Elin; Buffa, Jennifer A; Gupta, Nilaksh; Wang, Zeneng; Li, Lin; Fu, Xiaoming; Wu, Yuping; Mehrabian, Margarete; Sartor, R Balfour; McIntyre, Thomas M; Silverstein, Roy L; Tang, W H Wilson; DiDonato, Joseph A; Brown, J Mark; Lusis, Aldons J; Hazen, Stanley L
2016-03-24
Normal platelet function is critical to blood hemostasis and maintenance of a closed circulatory system. Heightened platelet reactivity, however, is associated with cardiometabolic diseases and enhanced potential for thrombotic events. We now show gut microbes, through generation of trimethylamine N-oxide (TMAO), directly contribute to platelet hyperreactivity and enhanced thrombosis potential. Plasma TMAO levels in subjects (n > 4,000) independently predicted incident (3 years) thrombosis (heart attack, stroke) risk. Direct exposure of platelets to TMAO enhanced sub-maximal stimulus-dependent platelet activation from multiple agonists through augmented Ca(2+) release from intracellular stores. Animal model studies employing dietary choline or TMAO, germ-free mice, and microbial transplantation collectively confirm a role for gut microbiota and TMAO in modulating platelet hyperresponsiveness and thrombosis potential and identify microbial taxa associated with plasma TMAO and thrombosis potential. Collectively, the present results reveal a previously unrecognized mechanistic link between specific dietary nutrients, gut microbes, platelet function, and thrombosis risk. PMID:26972052
DEFF Research Database (Denmark)
Pires, Sara Monteiro; Hald, Tine
2010-01-01
disease. These differences presumably represent multiple factors, such as differences in survivability through the food chain and/or pathogenicity. The relative importance of the source-dependent factors varied considerably over the years, reflecting, among others, variability in the surveillance programs......Salmonella is a major cause of human gastroenteritis worldwide. To prioritize interventions and assess the effectiveness of efforts to reduce illness, it is important to attribute salmonellosis to the responsible sources. Studies have suggested that some Salmonella subtypes have a higher health...... impact than others. Likewise, some food sources appear to have a higher impact than others. Knowledge of variability in the impact of subtypes and sources may provide valuable added information for research, risk management, and public health strategies. We developed a Bayesian model that attributes...
An urban flood risk assessment method using the Bayesian Network approach
DEFF Research Database (Denmark)
Åström, Helena Lisa Alexandra
Flooding is one of the most damaging natural hazards to human societies. Recent decades have shown that flooding constitutes major threats worldwide, and due to anticipated climate change the occurrence of damaging flood events is expected to increase. Urban areas are especially vulnerable to...... flood risk scoping, flood risk assessment (FRA), and adaptation implementation and involves an ongoing process of assessment, reassessment, and response. This thesis mainly focuses on the FRA phase of FRM. FRA includes hazard analysis and impact assessment (combined called a risk analysis), adaptation...... Bayesian Network (BN) approach is developed, and the method is exemplified in an urban catchment. BNs have become an increasingly popular method for describing complex systems and aiding decision-making under uncertainty. In environmental management, BNs have mainly been utilized in ecological assessments...
Analysis and assessment of injury risk in female gymnastics:Bayesian Network approach
Directory of Open Access Journals (Sweden)
Lyudmila Dimitrova
2015-02-01
Full Text Available This paper presents a Bayesian network (BN model for estimating injury risk in female artistic gymnastics. The model illustrates the connections betweenunderlying injury risk factorsthrough a series ofcausal dependencies. The quantitativepart of the model – the conditional probability tables, are determined using ТNormal distribution with parameters, derived by experts. The injury rates calculated by the network are in an agreement with injury statistic data and correctly reports the impact of various risk factors on injury rates. The model is designed to assist coaches and supporting teams in planning the training activity so that injuries are minimized. This study provides important background for further data collection and research necessary to improve the precision of the quantitative predictions of the model.
Microbial Risk Markers for Childhood Caries in Pediatricians’ Offices
Kanasi, E.; Johansson, I; Lu, S.C.; Kressin, N.R.; Nunn, M.E.; Kent, R; Tanner, A.C.R.
2010-01-01
Dental caries in pre-school children has significant public health and health disparity implications. To determine microbial risk markers for this infection, this study aimed to compare the microbiota of children with early childhood caries with that of caries-free children. Plaque samples from incisors, molars, and the tongue from 195 children attending pediatricians’ offices were assayed by 74 DNA probes and by PCR to Streptococcus mutans. Caries-associated factors included visible plaque, ...
Hu, Yi; Ward, Michael P; Xia, Congcong; Li, Rui; Sun, Liqian; Lynn, Henry; Gao, Fenghua; Wang, Qizhi; Zhang, Shiqing; Xiong, Chenglong; Zhang, Zhijie; Jiang, Qingwu
2016-01-01
Schistosomiasis remains a major public health problem and causes substantial economic impact in east China, particularly along the Yangtze River Basin. Disease forecasting and surveillance can assist in the development and implementation of more effective intervention measures to control disease. In this study, we applied a Bayesian hierarchical spatio-temporal model to describe trends in schistosomiasis risk in Anhui Province, China, using annual parasitological and environmental data for the period 1997-2010. A computationally efficient approach-Integrated Nested Laplace Approximation-was used for model inference. A zero-inflated, negative binomial model best described the spatio-temporal dynamics of schistosomiasis risk. It predicted that the disease risk would generally be low and stable except for some specific, local areas during the period 2011-2014. High-risk counties were identified in the forecasting maps: three in which the risk remained high, and two in which risk would become high. The results indicated that schistosomiasis risk has been reduced to consistently low levels throughout much of this region of China; however, some counties were identified in which progress in schistosomiasis control was less than satisfactory. Whilst maintaining overall control, specific interventions in the future should focus on these refractive counties as part of a strategy to eliminate schistosomiasis from this region. PMID:27053447
This tutorial provides instructions for accessing, retrieving, and downloading the following software to install on a host computer in support of Quantitative Microbial Risk Assessment (QMRA) modeling:• SDMProjectBuilder (which includes the Microbial Source Module as part...
DEFF Research Database (Denmark)
Pedersen, Karin Kaereby; Pedersen, Maria; Trøseid, Marius;
2013-01-01
Microbial translocation has been suggested to be a driver of immune activation and inflammation. We hypothesized that microbial translocation may be related to dyslipidemia, insulin resistance, and the risk of coronary heart disease in HIV-infected individuals....
The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network
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Yihua Zhong
2016-01-01
Full Text Available Oilfield development aiming at crude oil production is an extremely complex process, which involves many uncertain risk factors affecting oil output. Thus, risk prediction and early warning about oilfield development may insure operating and managing oilfields efficiently to meet the oil production plan of the country and sustainable development of oilfields. However, scholars and practitioners in the all world are seldom concerned with the risk problem of oilfield block development. The early warning index system of blocks development which includes the monitoring index and planning index was refined and formulated on the basis of researching and analyzing the theory of risk forecasting and early warning as well as the oilfield development. Based on the indexes of warning situation predicted by neural network, the method dividing the interval of warning degrees was presented by “3σ” rule; and a new method about forecasting and early warning of risk was proposed by introducing neural network to Bayesian networks. Case study shows that the results obtained in this paper are right and helpful to the management of oilfield development risk.
Bayesian-network-based safety risk assessment for steel construction projects.
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. PMID:23499984
HEAT STRESS RISK PREDICTION BY USING BAYESIAN NET MODEL WITH SENSOR NETWORK
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Kanchan M. Taiwade
2014-07-01
Full Text Available With advancement in use of automation system, it is also desired to be able to know about the susceptible risk in advance for taking the preventive measures either automatically or manually. Disaster management is such an area where operatives wearing the suits and performing the activities are prone to the risk of heat stress which may cause mental impairments along with other serious effects leading to death. Such type of risk occurs in human body by not being able to compensate the heat generated into the surrounding air. The paper presents the concept of mechanism which can be used to prevent such situation by activating an alert to the operative or invoke cooling mechanism automatically before onset of the risk. The Bayesian Network Model is used to predict the onset of the risk. The model is based on the probabilities gives flexibility and simplicity in modeling the system. The system was trained with appropriate data and then compared with the real time parameters to check whether possibility of risk or not. Only those body parameters are considered which directly or indirectly participate in indicating heat stress or its onset.
Urban flooding and health risk analysis by use of quantitative microbial risk assessment
DEFF Research Database (Denmark)
Andersen, Signe Tanja
Extreme rainfall overloads combined sewers, thereby causing flooding in urban areas, and if the public is exposed to flooding, they are at risk of acquiring gastrointestinal diseases. This is a known problem and is expected to increase because the frequency and intensity of extreme rainfall are......D thesis is to identify the limitations and possibilities for optimising microbial risk assessments of urban flooding through more evidence-based solutions, including quantitative microbial data and hydrodynamic water quality models. The focus falls especially on the problem of data needs and the causes of...... variations in the data. Essential limiting factors of urban flooding QMRAs were identified as uncertainty regarding ingestion volumes, the limited use of dose-response models and low numbers of microbial parameters measurements and absent validation of the risk assessments. Because improving knowledge of...
Directory of Open Access Journals (Sweden)
Nakstad Anders R
2010-04-01
Full Text Available Abstract Introduction Endotracheal intubation (ETI has been considered an essential part of pre-hospital advanced life support. Pre-hospital ETI, however, is a complex intervention also for airway specialist like anaesthesiologists working as pre-hospital emergency physicians. We therefore wanted to investigate the quality of pre-hospital airway management by anaesthesiologists in severely traumatised patients and identify possible areas for improvement. Method We performed a risk assessment according to the predictive Bayesian approach, in a typical anaesthesiologist-manned Norwegian helicopter emergency medical service (HEMS. The main focus of the risk assessment was the event where a patient arrives in the emergency department without ETI despite a pre-hospital indication for it. Results In the risk assessment, we assigned a high probability (29% for the event assessed, that a patient arrives without ETI despite a pre-hospital indication. However, several uncertainty factors in the risk assessment were identified related to data quality, indications for use of ETI, patient outcome and need for special training of ETI providers. Conclusion Our risk assessment indicated a high probability for trauma patients with an indication for pre-hospital ETI not receiving it in the studied HEMS. The uncertainty factors identified in the assessment should be further investigated to better understand the problem assessed and consequences for the patients. Better quality of pre-hospital airway management data could contribute to a reduction of these uncertainties.
DEFF Research Database (Denmark)
Ranta, Jukka; Lindqvist, Roland; Hansson, Ingrid; Tuominen, Pirkko; Nauta, Maarten
2015-01-01
MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and concentrations of microbes in production batches. We used Bayesian modeling for statistical inference and......Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation of...... evidence synthesis of two sample data sets. Thus, parameter uncertainty was represented by a joint posterior distribution, which we then used to predict the risk and to evaluate the criteria for acceptance of production batches. We also applied the Bayesian model to compare alternative criteria, accounting...
Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models.
Scholte, Ronaldo G C; Gosoniu, Laura; Malone, John B; Chammartin, Frédérique; Utzinger, Jürg; Vounatsou, Penelope
2014-04-01
Schistosomiasis is one of the most common parasitic diseases in tropical and subtropical areas, including Brazil. A national control programme was initiated in Brazil in the mid-1970s and proved successful in terms of morbidity control, as the number of cases with hepato-splenic involvement was reduced significantly. To consolidate control and move towards elimination, there is a need for reliable maps on the spatial distribution of schistosomiasis, so that interventions can target communities at highest risk. The purpose of this study was to map the distribution of Schistosoma mansoni in Brazil. We utilized readily available prevalence data from the national schistosomiasis control programme for the years 2005-2009, derived remotely sensed climatic and environmental data and obtained socioeconomic data from various sources. Data were collated into a geographical information system and Bayesian geostatistical models were developed. Model-based maps identified important risk factors related to the transmission of S. mansoni and confirmed that environmental variables are closely associated with indices of poverty. Our smoothed predictive risk map, including uncertainty, highlights priority areas for intervention, namely the northern parts of North and Southeast regions and the eastern part of Northeast region. Our predictive risk map provides a useful tool for to strengthen existing surveillance-response mechanisms. PMID:24361640
Assessment of occupational safety risks in Floridian solid waste systems using Bayesian analysis.
Bastani, Mehrad; Celik, Nurcin
2015-10-01
Safety risks embedded within solid waste management systems continue to be a significant issue and are prevalent at every step in the solid waste management process. To recognise and address these occupational hazards, it is necessary to discover the potential safety concerns that cause them, as well as their direct and/or indirect impacts on the different types of solid waste workers. In this research, our goal is to statistically assess occupational safety risks to solid waste workers in the state of Florida. Here, we first review the related standard industrial codes to major solid waste management methods including recycling, incineration, landfilling, and composting. Then, a quantitative assessment of major risks is conducted based on the data collected using a Bayesian data analysis and predictive methods. The risks estimated in this study for the period of 2005-2012 are then compared with historical statistics (1993-1997) from previous assessment studies. The results have shown that the injury rates among refuse collectors in both musculoskeletal and dermal injuries have decreased from 88 and 15 to 16 and three injuries per 1000 workers, respectively. However, a contrasting trend is observed for the injury rates among recycling workers, for whom musculoskeletal and dermal injuries have increased from 13 and four injuries to 14 and six injuries per 1000 workers, respectively. Lastly, a linear regression model has been proposed to identify major elements of the high number of musculoskeletal and dermal injuries. PMID:26219294
Prediction of near-term breast cancer risk using a Bayesian belief network
Zheng, Bin; Ramalingam, Pandiyarajan; Hariharan, Harishwaran; Leader, Joseph K.; Gur, David
2013-03-01
Accurately predicting near-term breast cancer risk is an important prerequisite for establishing an optimal personalized breast cancer screening paradigm. In previous studies, we investigated and tested the feasibility of developing a unique near-term breast cancer risk prediction model based on a new risk factor associated with bilateral mammographic density asymmetry between the left and right breasts of a woman using a single feature. In this study we developed a multi-feature based Bayesian belief network (BBN) that combines bilateral mammographic density asymmetry with three other popular risk factors, namely (1) age, (2) family history, and (3) average breast density, to further increase the discriminatory power of our cancer risk model. A dataset involving "prior" negative mammography examinations of 348 women was used in the study. Among these women, 174 had breast cancer detected and verified in the next sequential screening examinations, and 174 remained negative (cancer-free). A BBN was applied to predict the risk of each woman having cancer detected six to 18 months later following the negative screening mammography. The prediction results were compared with those using single features. The prediction accuracy was significantly increased when using the BBN. The area under the ROC curve increased from an AUC=0.70 to 0.84 (pvalue (PPV) and negative predictive value (NPV) also increased from a PPV=0.61 to 0.78 and an NPV=0.65 to 0.75, respectively. This study demonstrates that a multi-feature based BBN can more accurately predict the near-term breast cancer risk than with a single feature.
The Impact of Consumer Phase Models in Microbial Risk Analysis
DEFF Research Database (Denmark)
Nauta, Maarten; Christensen, Bjarke Bak
2011-01-01
In quantitative microbiological risk assessment (QMRA), the consumer phase model (CPM) describes the part of the food chain between purchase of the food product at retail and exposure. Construction of a CPM is complicated by the large variation in consumer food handling practices and a limited...... in the estimated relative risk. This difference is largest for scenarios where the aim is to remove the highly contaminated portion from human exposure. Given these results, we conclude that for many purposes it is not necessary to develop a new detailed CPM for each new QMRA. However, more observational data...... on consumer food handling practices and their impact on microbial transfer and survival are needed to generalize this conclusion....
Directory of Open Access Journals (Sweden)
Enrique Gracia
2014-01-01
Full Text Available This paper uses spatial data of cases of intimate partner violence against women (IPVAW to examine neighborhood-level influences on small-area variations in IPVAW risk in a police district of the city of Valencia (Spain. To analyze area variations in IPVAW risk and its association with neighborhood-level explanatory variables we use a Bayesian spatial random-effects modeling approach, as well as disease mapping methods to represent risk probabilities in each area. Analyses show that IPVAW cases are more likely in areas of high immigrant concentration, high public disorder and crime, and high physical disorder. Results also show a spatial component indicating remaining variability attributable to spatially structured random effects. Bayesian spatial modeling offers a new perspective to identify IPVAW high and low risk areas, and provides a new avenue for the design of better-informed prevention and intervention strategies.
Research on Innovation Risk Management based on Bayesian Risk Decision-Making
2012-01-01
Innovation is an inexhaustible force for the prosperity of one nation, and also the life source of enterprises. However, the high-risk characteristics of innovation activities make enterprises to perform scientific and effective innovation risk management. Based on a general introduction of Bayestian Risk Decision-making Theory and practices of product innovation in enterprises, the author discusses how to use the theory to achieve quantitative innovation-risk management, providing references...
International Nuclear Information System (INIS)
The impact of steel corrosion on the durability of reinforced concrete structures has since a long time been a major concern in civil engineering. The main electrochemical mechanisms of the steel corrosion are know well known. The material and structure degradation is attributed to the progressive formation of an expansive corrosion product at the steel-concrete interface. To assess quantitatively the structure lifetime, a two-stage service life model has been accepted widely. So far, the research attention is mainly given to the corrosion in an un-cracked concrete. However. practically one is often confronted to the reinforcement corrosion in an already cracked concrete. How to quantify the corrosion risk is of great interest for the long term durability of these cracked structures. To this end, this paper proposes a service life modeling for the corrosion process by carbonation in a cracked or un-cracked concrete depending on the observation or monitoring data available. Some recent experimental investigations are used to calibrate the models. Then, the models are applied to a shell structure to quantify the corrosion process and determine the optimal maintenance strategy. As corrosion processes are very difficult to model and subjected to material and environmental random variations, an example of structure reassessment is presented taking into account in situ information by the mean of Bayesian approaches. The coupling of monitoring, modelling and updating leads to a new global maintenance strategy of infrastructure. In conclusion: This paper presents an unified methodology coupling predictive models, observations and Bayesian approaches in order to assess the degradation degree of an ageing structure. The particular case of corrosion is treated on an innovative way by the development of a service life model taking into account cracking effects on the kinetics of the phenomena. At a material level, the dominant factors are the crack opening and the crack nature
Englehardt, James D; An, Huren; Fleming, Lora E; Bean, Judy A
2003-10-01
Unlike other waste streams, municipal solid waste (MSW) is collected manually, and MSW collection has recently been found to be among the highest-risk occupations in the United States. However, as for other occupational groups, actual total injury rates, including the great majority of injuries not compensated and those compensated by other insurance, are not known. In this article a predictive Bayesian method of assessing total injury rates from available information without computation is presented, and used to assess the actual numbers of musculoskeletal and dermal injuries requiring clinical care of MSW workers in Florida. Closed-form predictive Bayesian distributions that narrow progressively in response to information, representing both uncertainty and variability, are presented. Available information included workers' compensation (WC) data, worker population data, and safety records for one private and one public collection agency. Subjective input comprised epidemiological and medical judgment based on a review of 165 articles. The number of injuries was assessed at 3,146 annually in Florida, or 54 +/- 18 injuries per 100 workers per year with 95% confidence. Further, WC data indicate that the injury rate is 50% higher for garbage collectors specifically, indicating a rate of approximately 80 per 100 workers. Results, though subject to uncertainty in worker numbers and classification and reporting bias, agreed closely with a survey of 251 MSW collectors, of whom 75% reported being injured (and 70% reported illness) within the past 12 months. The approach is recommended for assessment of total injury rates and, where sufficient information exists, for the more difficult assessment of occupational disease rates. PMID:12969407
Strauss, Jillian; Miranda-Moreno, Luis F; Morency, Patrick
2013-10-01
This study proposes a two-equation Bayesian modelling approach to simultaneously study cyclist injury occurrence and bicycle activity at signalized intersections as joint outcomes. This approach deals with the potential presence of endogeneity and unobserved heterogeneities and is used to identify factors associated with both cyclist injuries and volumes. Its application to identify high-risk corridors is also illustrated. Montreal, Quebec, Canada is the application environment, using an extensive inventory of a large sample of signalized intersections containing disaggregate motor-vehicle traffic volumes and bicycle flows, geometric design, traffic control and built environment characteristics in the vicinity of the intersections. Cyclist injury data for the period of 2003-2008 is used in this study. Also, manual bicycle counts were standardized using temporal and weather adjustment factors to obtain average annual daily volumes. Results confirm and quantify the effects of both bicycle and motor-vehicle flows on cyclist injury occurrence. Accordingly, more cyclists at an intersection translate into more cyclist injuries but lower injury rates due to the non-linear association between bicycle volume and injury occurrence. Furthermore, the results emphasize the importance of turning motor-vehicle movements. The presence of bus stops and total crosswalk length increase cyclist injury occurrence whereas the presence of a raised median has the opposite effect. Bicycle activity through intersections was found to increase as employment, number of metro stations, land use mix, area of commercial land use type, length of bicycle facilities and the presence of schools within 50-800 m of the intersection increase. Intersections with three approaches are expected to have fewer cyclists than those with four. Using Bayesian analysis, expected injury frequency and injury rates were estimated for each intersection and used to rank corridors. Corridors with high bicycle volumes
Directory of Open Access Journals (Sweden)
Canty Angelo
2007-09-01
Full Text Available Abstract Background This study compares the Bayesian and frequentist (non-Bayesian approaches in the modelling of the association between the risk of preterm birth and maternal proximity to hazardous waste and pollution from the Sydney Tar Pond site in Nova Scotia, Canada. Methods The data includes 1604 observed cases of preterm birth out of a total population of 17559 at risk of preterm birth from 144 enumeration districts in the Cape Breton Regional Municipality. Other covariates include the distance from the Tar Pond; the rate of unemployment to population; the proportion of persons who are separated, divorced or widowed; the proportion of persons who have no high school diploma; the proportion of persons living alone; the proportion of single parent families and average income. Bayesian hierarchical Poisson regression, quasi-likelihood Poisson regression and weighted linear regression models were fitted to the data. Results The results of the analyses were compared together with their limitations. Conclusion The results of the weighted linear regression and the quasi-likelihood Poisson regression agrees with the result from the Bayesian hierarchical modelling which incorporates the spatial effects.
Energy Technology Data Exchange (ETDEWEB)
1979-01-01
The use of probabilistic, and especially Bayesian, methods is explained. The concepts of risk and decision, and probability and frequency are elucidated. The mechanics of probability and probabilistic calculations is discussed. The use of the method for particular problems, such as the frequency of aircraft crashes at a specified nuclear reactor site, is illustrated. 64 figures, 20 tables. (RWR)
International Nuclear Information System (INIS)
The use of probabilistic, and especially Bayesian, methods is explained. The concepts of risk and decision, and probability and frequency are elucidated. The mechanics of probability and probabilistic calculations is discussed. The use of the method for particular problems, such as the frequency of aircraft crashes at a specified nuclear reactor site, is illustrated. 64 figures, 20 tables
Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk.
Fuster-Parra, P; Tauler, P; Bennasar-Veny, M; Ligęza, A; López-González, A A; Aguiló, A
2016-04-01
An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool. PMID:26777431
A Bayesian Network Approach for Offshore Risk Analysis Through Linguistic Variables
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper presents a new approach for offshore risk analysis that is capable of dealing with linguistic probabilities in Bayesian networks (BNs). In this paper, linguistic probabilities are used to describe occurrence likelihood of hazardous events that may cause possible accidents in offshore operations. In order to use fuzzy information, an f-weighted valuation function is proposed to transform linguistic judgements into crisp probability distributions which can be easily put into a BN to model causal relationships among risk factors. The use of linguistic variables makes it easier for human experts to express their knowledge, and the transformation of linguistic judgements into crisp probabilities can significantly save the cost of computation, modifying and maintaining a BN model. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinion when quantitative data are lacking, or when only qualitative or vague statements can be made. The model is a modular representation of uncertain knowledge caused due to randomness, vagueness and ignorance. This makes the risk analysis of offshore engineering systems more functional and easier in many assessment contexts. Specifically, the proposed f-weighted valuation function takes into account not only the dominating values, but also the α-level values that are ignored by conventional valuation methods. A case study of the collision risk between a Floating Production, Storage and Off-loading (FPSO) unit and the authorised vessels due to human elements during operation is used to illustrate the application of the proposed model.
Modeling logistic performance in quantitative microbial risk assessment.
Rijgersberg, Hajo; Tromp, Seth; Jacxsens, Liesbeth; Uyttendaele, Mieke
2010-01-01
In quantitative microbial risk assessment (QMRA), food safety in the food chain is modeled and simulated. In general, prevalences, concentrations, and numbers of microorganisms in media are investigated in the different steps from farm to fork. The underlying rates and conditions (such as storage times, temperatures, gas conditions, and their distributions) are determined. However, the logistic chain with its queues (storages, shelves) and mechanisms for ordering products is usually not taken into account. As a consequence, storage times-mutually dependent in successive steps in the chain-cannot be described adequately. This may have a great impact on the tails of risk distributions. Because food safety risks are generally very small, it is crucial to model the tails of (underlying) distributions as accurately as possible. Logistic performance can be modeled by describing the underlying planning and scheduling mechanisms in discrete-event modeling. This is common practice in operations research, specifically in supply chain management. In this article, we present the application of discrete-event modeling in the context of a QMRA for Listeria monocytogenes in fresh-cut iceberg lettuce. We show the potential value of discrete-event modeling in QMRA by calculating logistic interventions (modifications in the logistic chain) and determining their significance with respect to food safety. PMID:20055976
Bao, Le; Raftery, Adrian E.; Reddy, Amala
2015-01-01
In most countries in the world outside of sub-Saharan Africa, HIV is largely concentrated in sub-populations whose behavior puts them at higher risk of contracting and transmitting HIV, such as people who inject drugs, sex workers and men who have sex with men. Estimating the size of these sub-populations is important for assessing overall HIV prevalence and designing effective interventions. We present a Bayesian hierarchical model for estimating the sizes of local and national HIV key affec...
Ayele, Yonas Zewdu
2016-01-01
The papers of this thesis are not available in Munin. Paper I. Ayele YZ, Barabadi A, Barabady J.: A methodology for identification of a suitable drilling waste handling system in the Arctic region. (Manuscript). Paper II. Ayele YZ, Barabady J, Droguett EL.: Dynamic Bayesian network based risk assessment for Arctic offshore drilling waste handling practices. (Manuscript). Published version available in Journal of Offshore Mechanics and Arctic Engineering 138(5), 051302 (Jun 17, 2016) ...
Application of Bayesian and cost benefit risk analysis in water resources management
Varouchakis, E. A.; Palogos, I.; Karatzas, G. P.
2016-03-01
Decision making is a significant tool in water resources management applications. This technical note approaches a decision dilemma that has not yet been considered for the water resources management of a watershed. A common cost-benefit analysis approach, which is novel in the risk analysis of hydrologic/hydraulic applications, and a Bayesian decision analysis are applied to aid the decision making on whether or not to construct a water reservoir for irrigation purposes. The alternative option examined is a scaled parabolic fine variation in terms of over-pumping violations in contrast to common practices that usually consider short-term fines. The methodological steps are analytically presented associated with originally developed code. Such an application, and in such detail, represents new feedback. The results indicate that the probability uncertainty is the driving issue that determines the optimal decision with each methodology, and depending on the unknown probability handling, each methodology may lead to a different optimal decision. Thus, the proposed tool can help decision makers to examine and compare different scenarios using two different approaches before making a decision considering the cost of a hydrologic/hydraulic project and the varied economic charges that water table limit violations can cause inside an audit interval. In contrast to practices that assess the effect of each proposed action separately considering only current knowledge of the examined issue, this tool aids decision making by considering prior information and the sampling distribution of future successful audits.
Applications of Bayesian approach in modelling risk of malaria-related hospital mortality
Directory of Open Access Journals (Sweden)
Simbeye Jupiter S
2008-02-01
Full Text Available Abstract Background Malaria is a major public health problem in Malawi, however, quantifying its burden in a population is a challenge. Routine hospital data provide a proxy for measuring the incidence of severe malaria and for crudely estimating morbidity rates. Using such data, this paper proposes a method to describe trends, patterns and factors associated with in-hospital mortality attributed to the disease. Methods We develop semiparametric regression models which allow joint analysis of nonlinear effects of calendar time and continuous covariates, spatially structured variation, unstructured heterogeneity, and other fixed covariates. Modelling and inference use the fully Bayesian approach via Markov Chain Monte Carlo (MCMC simulation techniques. The methodology is applied to analyse data arising from paediatric wards in Zomba district, Malawi, between 2002 and 2003. Results and Conclusion We observe that the risk of dying in hospital is lower in the dry season, and for children who travel a distance of less than 5 kms to the hospital, but increases for those who are referred to the hospital. The results also indicate significant differences in both structured and unstructured spatial effects, and the health facility effects reveal considerable differences by type of facility or practice. More importantly, our approach shows non-linearities in the effect of metrical covariates on the probability of dying in hospital. The study emphasizes that the methodological framework used provides a useful tool for analysing the data at hand and of similar structure.
Bayesian objective classification of extreme UK daily rainfall for flood risk applications
Directory of Open Access Journals (Sweden)
M. A. Little
2008-11-01
Full Text Available In this study we describe an objective classification scheme for extreme UK daily precipitation to be used in flood risk analysis applications. We create a simplified representation of the spatial layout of extreme events based on a new digital archive of UK rainfall. This simplification allows a Bayesian clustering algorithm to compress these representations down to eight prototypical patterns of extreme falls. These patterns are then verified against a five-class, manual, subjective typing scheme, produced independently using known meteorological mechanisms, isohyetal maps and additional descriptive text from the archive. Compared against the manual scheme, the new objective scheme can reproduce the known meteorological conditions, both in terms of spatial layout and seasonal timing, and is shown to be of hydrological relevance when matched to several notable flooding events in the past century. Furthermore, it is computationally simple and straightforward to apply in classifying future extreme rainfall events. We discuss the practical use of this new typing scheme in flood simulations and climate change applications.
Gilkey, Kelly M.; Myers, Jerry G.; McRae, Michael P.; Griffin, Elise A.; Kallrui, Aditya S.
2012-01-01
The Exploration Medical Capability project is creating a catalog of risk assessments using the Integrated Medical Model (IMM). The IMM is a software-based system intended to assist mission planners in preparing for spaceflight missions by helping them to make informed decisions about medical preparations and supplies needed for combating and treating various medical events using Probabilistic Risk Assessment. The objective is to use statistical analyses to inform the IMM decision tool with estimated probabilities of medical events occurring during an exploration mission. Because data regarding astronaut health are limited, Bayesian statistical analysis is used. Bayesian inference combines prior knowledge, such as data from the general U.S. population, the U.S. Submarine Force, or the analog astronaut population located at the NASA Johnson Space Center, with observed data for the medical condition of interest. The posterior results reflect the best evidence for specific medical events occurring in flight. Bayes theorem provides a formal mechanism for combining available observed data with data from similar studies to support the quantification process. The IMM team performed Bayesian updates on the following medical events: angina, appendicitis, atrial fibrillation, atrial flutter, dental abscess, dental caries, dental periodontal disease, gallstone disease, herpes zoster, renal stones, seizure, and stroke.
Casarin, Roberto; Chang, Chia-Lin; Jiménez-Martín, Juan-Ángel; McAleer, Michael; Pérez-Amaral, Teodosio
2011-01-01
It is well known that the Basel II Accord requires banks and other Authorized Deposit-taking Institutions (ADIs) to communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models, whether individually or as combinations, to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous ...
Microbial pesticides [data requirements for environmental risk assessment
Mensink BJWG; Linders JBHJ; CSR
1997-01-01
The market for microbial pesticides is, though slowly, expanding. Therefore more research with these pesticides will be carried out in the near future, not only for agronomical and economical, but also for environmental reasons. As more chemical pesticides are going to be banned, microbial pesticid
R. Casarin (Roberto); C-L. Chang (Chia-Lin); J.A. Jiménez-Martín (Juan-Ángel); M.J. McAleer (Michael); T. Pérez-Amaral (Teodosio)
2011-01-01
textabstractIt is well known that the Basel II Accord requires banks and other Authorized Deposit-taking Institutions (ADIs) to communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models, whether individually or
[Microbial risk assessment of urban water bodies for aesthetical and recreational uses].
Sun, Fu; Sha, Jing; Zhang, Yi-Fan; Liu, Yan-Hua
2013-03-01
With the increasing public access to urban water bodies for aesthetical and recreational purposes, it is of critical importance for densely populated urban areas to conduct microbial risk assessment and accordingly implement effective risk management. Based on the methodology of quantitative microbial risk assessment, a case study was conducted on three typical urban water bodies for aesthetical and recreational uses in a southern city in China. Exposure assessment was carried out through water quality monitoring, field survey and literature review, and accordingly human health risk was assessed with different dose-response equations based on fecal coliforms (FC), Escherichia coli (EC) and Enterococci (ENT). Microbial risk estimated by different dose-response equations was found consistent with and comparable to each other. Stream B located in a residential area was not suitable for primary- or secondary-contact recreational uses, and its microbial risk to the public mainly came from water abstraction for household miscellaneous uses. Stream C and Lake E, located in a public open space and a scenic area respectively, could meet the current recreational requirements, and their microbial risk to the public was generally attributed to various recreational activities. It was necessary to address the public health risk associated with the unauthorized or inappropriate water uses (e.g. abstraction for household miscellaneous uses) of urban aesthetical and recreational water bodies. PMID:23745397
Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk
International Nuclear Information System (INIS)
Purpose: Prediction of radiation pneumonitis (RP) has been shown to be challenging due to the involvement of a variety of factors including dose–volume metrics and radiosensitivity biomarkers. Some of these factors are highly correlated and might affect prediction results when combined. Bayesian network (BN) provides a probabilistic framework to represent variable dependencies in a directed acyclic graph. The aim of this study is to integrate the BN framework and a systems’ biology approach to detect possible interactions among RP risk factors and exploit these relationships to enhance both the understanding and prediction of RP. Methods: The authors studied 54 nonsmall-cell lung cancer patients who received curative 3D-conformal radiotherapy. Nineteen RP events were observed (common toxicity criteria for adverse events grade 2 or higher). Serum concentration of the following four candidate biomarkers were measured at baseline and midtreatment: alpha-2-macroglobulin, angiotensin converting enzyme (ACE), transforming growth factor, interleukin-6. Dose-volumetric and clinical parameters were also included as covariates. Feature selection was performed using a Markov blanket approach based on the Koller–Sahami filter. The Markov chain Monte Carlo technique estimated the posterior distribution of BN graphs built from the observed data of the selected variables and causality constraints. RP probability was estimated using a limited number of high posterior graphs (ensemble) and was averaged for the final RP estimate using Bayes’ rule. A resampling method based on bootstrapping was applied to model training and validation in order to control under- and overfit pitfalls. Results: RP prediction power of the BN ensemble approach reached its optimum at a size of 200. The optimized performance of the BN model recorded an area under the receiver operating characteristic curve (AUC) of 0.83, which was significantly higher than multivariate logistic regression (0
Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk
Energy Technology Data Exchange (ETDEWEB)
Lee, Sangkyu, E-mail: sangkyu.lee@mail.mcgill.ca; Ybarra, Norma; Jeyaseelan, Krishinima; Seuntjens, Jan; El Naqa, Issam [Medical Physics Unit, McGill University, Montreal, Quebec H3G1A4 (Canada); Faria, Sergio; Kopek, Neil; Brisebois, Pascale [Department of Radiation Oncology, Montreal General Hospital, Montreal, H3G1A4 (Canada); Bradley, Jeffrey D.; Robinson, Clifford [Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri 63110 (United States)
2015-05-15
Purpose: Prediction of radiation pneumonitis (RP) has been shown to be challenging due to the involvement of a variety of factors including dose–volume metrics and radiosensitivity biomarkers. Some of these factors are highly correlated and might affect prediction results when combined. Bayesian network (BN) provides a probabilistic framework to represent variable dependencies in a directed acyclic graph. The aim of this study is to integrate the BN framework and a systems’ biology approach to detect possible interactions among RP risk factors and exploit these relationships to enhance both the understanding and prediction of RP. Methods: The authors studied 54 nonsmall-cell lung cancer patients who received curative 3D-conformal radiotherapy. Nineteen RP events were observed (common toxicity criteria for adverse events grade 2 or higher). Serum concentration of the following four candidate biomarkers were measured at baseline and midtreatment: alpha-2-macroglobulin, angiotensin converting enzyme (ACE), transforming growth factor, interleukin-6. Dose-volumetric and clinical parameters were also included as covariates. Feature selection was performed using a Markov blanket approach based on the Koller–Sahami filter. The Markov chain Monte Carlo technique estimated the posterior distribution of BN graphs built from the observed data of the selected variables and causality constraints. RP probability was estimated using a limited number of high posterior graphs (ensemble) and was averaged for the final RP estimate using Bayes’ rule. A resampling method based on bootstrapping was applied to model training and validation in order to control under- and overfit pitfalls. Results: RP prediction power of the BN ensemble approach reached its optimum at a size of 200. The optimized performance of the BN model recorded an area under the receiver operating characteristic curve (AUC) of 0.83, which was significantly higher than multivariate logistic regression (0
Directory of Open Access Journals (Sweden)
Giovanna Raso
2007-11-01
Full Text Available There is growing interest in the use of Bayesian geostatistical models for predicting the spatial distribution of parasitic infections, including hookworm, Schistosoma mansoni and co-infections with both parasites. The aim of this study was to predict the spatial distribution of mono-infections with either hookworm or S. mansoni in a setting where both parasites co-exist. School-based cross-sectional parasitological and questionnaire surveys were carried out in 57 rural schools in the Man region, western Côte d’Ivoire. A single stool specimen was obtained from each schoolchild attending grades 3-5. Stool specimens were processed by the Kato-Katz technique and an ether concentration method and examined for the presence of hookworm and S. mansoni eggs. The combined results from the two diagnostic approaches were considered for the infection status of each child. Demographic data (i.e. age and sex were obtained from readily available school registries. Each child’s socio-economic status was estimated, using the questionnaire data following a household-based asset approach. Environmental data were extracted from satellite imagery. The different data sources were incorporated into a geographical information system. Finally, a Bayesian spatial multinomial regression model was constructed and the spatial patterns of S. mansoni and hookworm mono-infections were investigated using Bayesian kriging. Our approach facilitated the production of smooth risk maps for hookworm and S. mansoni mono-infections that can be utilized for targeting control interventions. We argue that in settings where S. mansoni and hookworm co-exist and control efforts are under way, there is a need for both mono- and co-infection risk maps to enhance the cost-effectiveness of control programmes.
Herring, Carlie E; Stinson, Jonah; Landis, Wayne G
2015-10-01
Many coastal regions are encountering issues with the spread of nonindigenous species (NIS). In this study, we conducted a regional risk assessment using a Bayesian network relative risk model (BN-RRM) to analyze multiple vectors of NIS introductions to Padilla Bay, Washington, a National Estuarine Research Reserve. We had 3 objectives in this study. The 1st objective was to determine whether the BN-RRM could be used to calculate risk from NIS introductions for Padilla Bay. Our 2nd objective was to determine which regions and endpoints were at greatest risk from NIS introductions. Our 3rd objective was to incorporate a management option into the model and predict endpoint risk if it were to be implemented. Eradication can occur at different stages of NIS invasions, such as the elimination of these species before being introduced to the habitat or removal of the species after settlement. We incorporated the ballast water treatment management scenario into the model, observed the risk to the endpoints, and compared this risk with the initial risk estimates. The model results indicated that the southern portion of the bay was at greatest risk because of NIS. Changes in community composition, Dungeness crab, and eelgrass were the endpoints most at risk from NIS introductions. The currents node, which controls the exposure of NIS to the bay from the surrounding marine environment, was the parameter that had the greatest influence on risk. The ballast water management scenario displayed an approximate 1% reduction in risk in this Padilla Bay case study. The models we developed provide an adaptable template for decision makers interested in managing NIS in other coastal regions and large bodies of water. PMID:25845995
A quantitative microbial risk assessment for center pivot irrigation of dairy wastewaters
In the western United States where livestock wastewaters are commonly land applied, there are concerns over individuals being exposed to airborne pathogens. In response, a quantitative microbial risk assessment (QMRA) was performed to estimate infectious risks from inhaling pathogens aerosolized dur...
Standardized methods are often used to assess the likelihood of a human-health effect from exposure to a specified hazard, and inform opinions and decisions about risk management and communication. A Quantitative Microbial Risk Assessment (QMRA) is specifically adapted to detail potential human-heal...
Zacarias, Orlando P.
2015-01-01
The health sector in Mozambique is piled with data, holding records of major public health diseases, such as malaria, cholera, etc. The process of scrutinizing such a mass of health data for useful information is challenging but essential for the health authorities and professionals. Statistical learning and inferential approaches can be used to provide health decision makers with appropriate tools for disease diagnosis and assessment, where the analysis is performed using Bayesian predictive...
Disposable contact lens use as a risk factor for microbial keratitis
Radford, C.; Minassian, D; Dart, J.
1998-01-01
AIMS—A case-control study was performed to evaluate soft contact lens (SCL) wear modality as a risk factor for microbial keratitis. METHODS—Contact lens wearers presenting as new patients to Moorfields Eye Hospital accident and emergency department during a 12 month period completed a self administered questionnaire detailing demographic data and contact lens use habits. Cases were patients with a clinical diagnosis of SCL related microbial keratitis. Controls were SCL users attending with di...
A bayesian belief networks approach to risk control in construction projects
Chivatá Cárdenas, I.C.; Al-jibouri, S.H.S.; Halman, J.I.M.; Telichenko, V.; Volkov, A.; Bilchuk, I.
2012-01-01
Although risk control is a key step in risk management of construction projects, very often risk measures used are based merely on personal experience and engineering judgement rather than analysis of comprehensive information relating to a specific risk. This paper deals with an approach to provide better information to derive relevant and effective risk measures for specific risks. The approach relies on developing risk models to represent interactions between risk factors and carrying out ...
Bayesian network as a modelling tool for risk management in agriculture
DEFF Research Database (Denmark)
Rasmussen, Svend; Madsen, Anders Læsø; Lund, Mogens
The importance of risk management increases as farmers become more exposed to risk. But risk management is a difficult topic because income risk is the result of the complex interaction of multiple risk factors combined with the effect of an increasing array of possible risk management tools. In...
Risks of microbial spoilage of wine: a review
Mojsov, Kiro; Petreska, Meri; Ziberoski, Jugoslav
2011-01-01
Wines are alcoholic drinks obtained from the fermentation of grapes. The main role of microorganisms in winemaking is to convert grape sugars to alcohol, reduce wine acidity and contribute to aroma and flavor. They can also cause numerous unwelcome wine spoilage problems, which reduce wine quality and value. Winemaking processes includes multiple stages at which microbial spoilage is likely to occur and ends up with altering the quality and hygienic status of the wine. This may render the ...
Zhang, Limao; Wu, Xianguo; Qin, Yawei; Skibniewski, Miroslaw J; Liu, Wenli
2016-02-01
Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel-induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step-by-step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN-based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel-induced pipeline damage model is proposed to reveal the cause-effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment. PMID:26224125
Balbi, S.; Villa, F.; Mojtahed, V.; Hegetschweiler, K. T.; Giupponi, C.
2015-10-01
This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of: (1) likelihood of non-fatal physical injury; (2) likelihood of post-traumatic stress disorder; (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the benefits of improving an existing Early Warning System, taking into account the reliability, lead-time and scope (i.e. coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event: about 75 % of fatalities, 25 % of injuries and 18 % of post-traumatic stress disorders could be avoided.
Balbi, Stefano; Villa, Ferdinando; Mojtahed, Vahid; Hegetschweiler, Karin Tessa; Giupponi, Carlo
2016-06-01
This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; and produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of (1) likelihood of non-fatal physical injury, (2) likelihood of post-traumatic stress disorder and (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the effect of improving an existing early warning system, taking into account the reliability, lead time and scope (i.e., coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event.
Current trends in Bayesian methodology with applications
Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia
2015-01-01
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on
International Nuclear Information System (INIS)
A domino effect is a low frequency high consequence chain of accidents where a primary accident (usually fire and explosion) in a unit triggers secondary accidents in adjacent units. High complexity and growing interdependencies of chemical infrastructures make them increasingly vulnerable to domino effects. Domino effects can be considered as time dependent processes. Thus, not only the identification of involved units but also their temporal entailment in the chain of accidents matter. More importantly, in the case of domino-induced fires which can generally last much longer compared to explosions, foreseeing the temporal evolution of domino effects and, in particular, predicting the most probable sequence of accidents (or involved units) in a domino effect can be of significance in the allocation of preventive and protective safety measures. Although many attempts have been made to identify the spatial evolution of domino effects, the temporal evolution of such accidents has been overlooked. We have proposed a methodology based on dynamic Bayesian network to model both the spatial and temporal evolutions of domino effects and also to quantify the most probable sequence of accidents in a potential domino effect. The application of the developed methodology has been demonstrated via a hypothetical fuel storage plant. - Highlights: • A Dynamic Bayesian Network methodology has been developed to model domino effects. • Considering time-dependencies, both spatial and temporal evolutions of domino effects have been modeled. • The concept of most probable sequence of accidents has been proposed instead of the most probable combination of accidents. • Using backward analysis, the most vulnerable units have been identified during a potential domino effect. • The proposed methodology does not need to identify a unique primary unit (accident) for domino effect modeling
2012-07-31
... document for public comment in July, 2011(76 FR 44586). EPA received two public comments--one from a member... AGENCY Notice of Availability of Microbial Risk Assessment Guideline: Pathogenic Microorganisms With... Assessment Guideline: Pathogenic Microorganisms with Focus on Food and Water (MRA Guideline). The...
2012-07-31
... Listserv, a free electronic mail subscription service for industry, trade groups, consumer interest groups... Food Safety and Inspection Service Availability of Microbial Risk Assessment Guideline: Pathogenic Microorganisms With Focus on Food and in Water AGENCY: Food Safety and Inspection Service, USDA. ACTION:...
2011-11-01
... AGENCY External Peer Review Meeting for Draft Microbial Risk Assessment Guideline: Pathogenic... guidance for a 60 day comment period (76 FR 44586-44587). The public comment period ended on September 26... attend this peer review meeting as observers. Time will be set aside at the meeting for observers to...
Bayesian default probability models
Andrlíková, Petra
2014-01-01
This paper proposes a methodology for default probability estimation for low default portfolios, where the statistical inference may become troublesome. The author suggests using logistic regression models with the Bayesian estimation of parameters. The piecewise logistic regression model and Box-Cox transformation of credit risk score is used to derive the estimates of probability of default, which extends the work by Neagu et al. (2009). The paper shows that the Bayesian models are more acc...
Directory of Open Access Journals (Sweden)
Lawrence N Kazembe
Full Text Available Despite remarkable gains in life expectancy and declining mortality in the 21st century, in many places mostly in developing countries, adult mortality has increased in part due to HIV/AIDS or continued abject poverty levels. Moreover many factors including behavioural, socio-economic and demographic variables work simultaneously to impact on risk of mortality. Understanding risk factors of adult mortality is crucial towards designing appropriate public health interventions. In this paper we proposed a structured additive two-part random effects regression model for adult mortality data. Our proposal assumed two processes: (i whether death occurred in the household (prevalence part, and (ii number of reported deaths, if death did occur (severity part. The proposed model specification therefore consisted of two generalized linear mixed models (GLMM with correlated random effects that permitted structured and unstructured spatial components at regional level. Specifically, the first part assumed a GLMM with a logistic link and the second part explored a count model following either a Poisson or negative binomial distribution. The model was used to analyse adult mortality data of 25,793 individuals from the 2006/2007 Namibian DHS data. Inference is based on the Bayesian framework with appropriate priors discussed.
McDonald, K S; Ryder, D S; Tighe, M
2015-05-01
Bayesian Belief Networks (BBNs) are being increasingly used to develop a range of predictive models and risk assessments for ecological systems. Ecological BBNs can be applied to complex catchment and water quality issues, integrating multiple spatial and temporal variables within social, economic and environmental decision making processes. This paper reviews the essential components required for ecologists to design a best-practice predictive BBN in an ecological risk assessment (ERA) framework for aquatic ecosystems, outlining: (1) how to create a BBN for an aquatic ERA?; (2) what are the challenges for aquatic ecologists in adopting the best-practice applications of BBNs to ERAs?; and (3) how can BBNs in ERAs influence the science/management interface into the future? The aims of this paper are achieved using three approaches. The first is to demonstrate the best-practice development of BBNs in aquatic sciences using a simple nutrient model. The second is to discuss the limitations and challenges aquatic ecologists encounter when applying BBNs to ERAs. The third is to provide a framework for integrating best-practice BBNs into ERAs and the management of aquatic ecosystems. A quantitative review of the application and development of BBNs in aquatic science from 2002 to 2014 was conducted to identify areas where continued best-practice development is required. We outline a best-practice framework for the integration of BBNs into ERAs and study of complex aquatic systems. PMID:25733196
Scherb, Anke; Papakosta, Panagiota; Straub, Daniel
2014-05-01
Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the Mediterranean. To facilitate coping with wildfire risks, an understanding of the factors influencing wildfire occurrence and behavior (e.g. human activity, weather conditions, topography, fuel loads) and their interaction is of importance, as is the implementation of this knowledge in improved wildfire hazard and risk prediction systems. In this project, a probabilistic wildfire risk prediction model is developed, with integrated fire occurrence and fire propagation probability and potential impact prediction on natural and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1 km2 spatial and 1 day temporal resolution). The modeled consequences account for potential restoration costs and production losses referred to forests, agriculture, and (semi-) natural areas. BNs and a geographic information system (GIS) are coupled within this project to support a semi-automated BN model parameter learning and the spatial-temporal risk prediction. The coupling also enables the visualization of prediction results by means of daily maps. The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is used as validation data set. A special focus is put on the performance evaluation of the BN for fire occurrence, which is modeled as binary classifier and thus, could be validated by means of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of more than 70% for validation could be achieved, which indicates potential for reliable prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final prediction results. The resulting system can be easily expanded to predict additional expected damages in the mesoscale (e.g. building and infrastructure damages). The system can support planning of
Microbiological community in biogas systems and evaluation of microbial risks from gas usage
Energy Technology Data Exchange (ETDEWEB)
Vinneraas, B.; Nordin, A. [Swedish Univ. of Agricultural Sciences, Dept. of Biometry and Engineering, Uppsala (Sweden); Schoenning, C. [Swedish Inst. for Infectious Disease Control, Dept. of Parasitology, Mycology, Environmental Mirobiology and Water, Solna (Sweden)
2007-12-15
The plans for introducing biogas produced from organic waste to the pipe system for natural gas have raised concerns about the risk of transmitting disease via the gas. To assess this risk, condensate water from gas pipes and gas from different parts of biogas upgrading systems were sampled and cultured for microbial content. The number of microorganisms found in the biogas correspond to the densities in sampled natural gas. Since no pathogens were identified and since the exposure to gas from e. g. cookers and refueling of cars may only result in the inhalation of small volumes of gas, the risk of spreading disease via biogas was judged to be very low. (orig.)
DEFF Research Database (Denmark)
Schur, Nadine; Hürlimann, Eveline; Stensgaard, Anna-Sofie;
2013-01-01
Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions...
Bayesian Frailty for Competing Risks Survival Analysis in the Iranian Metastatic Colorectal Patients
Asghari Jafarabadi, Mohamad; Hajizadeh, Ebrahim; Kazemnejad, Anoshirvan; Fatemi, Seyed Reza
2010-01-01
Objective: In the competing risks problem, wherein only the event due one cause is observed, the cause-specific hazard rates are usually estimated by considering the independence assumption on the competing causes. However, this assumption is too rigorous in the practical situations. This paper aimed to extend the results of Finkelstein and Esaulova (2008), in order to relax the problem of independence assumption in the competing risks survival analysis with counting process approach by exert...
International Nuclear Information System (INIS)
The paper presents an innovative approach to integrate Human and Organisational Factors (HOF) into risk analysis. The approach has been developed and applied to a case study in the maritime industry, but it can also be utilised in other sectors. A Bayesian Belief Network (BBN) has been developed to model the Maritime Transport System (MTS), by taking into account its different actors (i.e., ship-owner, shipyard, port and regulator) and their mutual influences. The latter have been modelled by means of a set of dependent variables whose combinations express the relevant functions performed by each actor. The BBN model of the MTS has been used in a case study for the quantification of HOF in the risk analysis carried out at the preliminary design stage of High Speed Craft (HSC). The study has focused on a collision in open sea hazard carried out by means of an original method of integration of a Fault Tree Analysis (FTA) of technical elements with a BBN model of the influences of organisational functions and regulations, as suggested by the International Maritime Organisation's (IMO) Guidelines for Formal Safety Assessment (FSA). The approach has allowed the identification of probabilistic correlations between the basic events of a collision accident and the BBN model of the operational and organisational conditions. The linkage can be exploited in different ways, especially to support identification and evaluation of risk control options also at the organisational level. Conditional probabilities for the BBN have been estimated by means of experts' judgments, collected from an international panel of different European countries. Finally, a sensitivity analysis has been carried out over the model to identify configurations of the MTS leading to a significant reduction of accident probability during the operation of the HSC
基于贝叶斯风险决策的创新风险管理研究%Innovation Risk Management Based on Bayesian Risk Decision-making
Institute of Scientific and Technical Information of China (English)
郭迎春
2012-01-01
Innovation is an inexhaustible motive force for the prosperity of the country, and is also the life source of the enterprise. However, because of the high risk feature of innovation activities, the enterprise must carry on scientific and effective innovation risk management. On the overview of Bayesian Risk Decision - making theory, we discussed how to use Bayesian Risk Decision — making method for innovation risk management combined with the example of enterprise product innovation, which can provide the reference for the scientific decision - making of enterprise innovation activities.%在对贝叶斯风险决策理论进行概述的基础上,结合企业产品创新实例,探讨如何运用贝叶斯风险决策方法进行定量的创新风险管理,为企业创新活动提供科学的决策依据.
Beatriz Martínez-López; Tsviatko Alexandrov; Lina Mur; Fernando Sánchez-Vizcaíno; Sánchez-Vizcaíno, José M.
2014-01-01
The spatial pattern and epidemiology of backyard pig farming and other low bio-security pig production systems and their role in the occurrence of classical swine fever (CSF) is described and evaluated. A spatial Bayesian model was used to explore the risk factors, including human demographics, socioeconomic and environmental factors. The analyses were performed for Bulgaria, which has a large number of backyard farms (96% of all pig farms in the country are classified as backyard farms), and...
Heidinger, Joelle C; Winter, Carl K; Cullor, James S
2009-08-01
A quantitative microbial risk assessment was constructed to determine consumer risk from Staphylococcus aureus and staphylococcal enterotoxin in raw milk. A Monte Carlo simulation model was developed to assess the risk from raw milk consumption using data on levels of S. aureus in milk collected by the University of California-Davis Dairy Food Safety Laboratory from 2,336 California dairies from 2005 to 2008 and using U.S. milk consumption data from the National Health and Nutrition Examination Survey of 2003 and 2004. Four modules were constructed to simulate pathogen growth and staphylococcal enterotoxin A production scenarios to quantify consumer risk levels under various time and temperature storage conditions. The three growth modules predicted that S. aureus levels could surpass the 10(5) CFU/ml level of concern at the 99.9th or 99.99th percentile of servings and therefore may represent a potential consumer risk. Results obtained from the staphylococcal enterotoxin A production module predicted that exposure at the 99.99th percentile could represent a dose capable of eliciting staphylococcal enterotoxin intoxication in all consumer age groups. This study illustrates the utility of quantitative microbial risk assessments for identifying potential food safety issues. PMID:19722395
Utilization of Biogas and Evaluation Of Microbial Risks From Gas Usage
Czech Academy of Sciences Publication Activity Database
Čermáková, J.; Mrázek, Jakub; Fliegerová, Kateřina; Tenkrát, D.
Tatranská Kotlina : Harmony Apeiron non-profit civic association, 2011, s. 15-22. ISBN 978-80-89347-05-6. [ERIN Conference. Tatranská Kotlina (SK), 13.04.2011-16.04.2011] R&D Projects: GA ČR GPP503/10/P394 Institutional research plan: CEZ:AV0Z50450515 Keywords : biogas * microbial risk Subject RIV: EH - Ecology, Behaviour
Robertson, Danielle M.
2013-01-01
Previous studies using animal models and human clinical trials have demonstrated that the use of low oxygen transmissible contact lens materials produce corneal epithelial surface damage resulting in increased Pseudomonas aeruginosa (PA) adhesion and raft-mediated internalization into surface corneal epithelial cells. These findings led to the testable clinical predictions that: (1) microbial keratitis (MK) risk is expected to be greatest during the first 6 months of wear; (2) there is no dif...
Bayesian-Integrated Microbial Forensics▿
Jarman, Kristin H.; Kreuzer-Martin, Helen W.; WUNSCHEL, DAVID S.; Valentine, Nancy B.; Cliff, John B.; Petersen, Catherine E.; Colburn, Heather A.; Wahl, Karen L.
2008-01-01
In the aftermath of the 2001 anthrax letters, researchers have been exploring ways to predict the production environment of unknown-source microorganisms. Culture medium, presence of agar, culturing temperature, and drying method are just some of the broad spectrum of characteristics an investigator might like to infer. The effects of many of these factors on microorganisms are not well understood, but the complex way in which microbes interact with their environments suggests that numerous a...
Razen, Alexander; Brunauer, Wolgang; Klein, Nadja; Kneib, Thomas; Lang, Stefan; Umlauf, Nikolaus
2014-01-01
The Basel II framework strictly defines the conditions under which financial institutions are authorized to accept real estate as collateral in order to decrease their credit risk. A widely used concept for its valuation is the hedonic approach. It assumes, that a property can be characterized by a bundle of covariates that involves both individual attributes of the building itself and locational attributes of the region where the building is located in. Each of these attributes can be assign...
International Nuclear Information System (INIS)
Highlights: ► Addition of feathers altered bacterial and fungal communities in compost. ► Microbial communities degrading SRM and compost matrix were distinct. ► Addition of feathers may enrich for microbial communities that degrade SRM. ► Inclusion of feather in compost increased both CH4 and N2O emissions from compost. ► Density of methanogens and methanotrophs were weakly associated with CH4 emissions. - Abstract: Provided that infectious prions (PrPSc) are inactivated, composting of specified risk material (SRM) may be a viable alternative to rendering and landfilling. In this study, bacterial and fungal communities as well as greenhouse gas emissions associated with the degradation of SRM were examined in laboratory composters over two 14 day composting cycles. Chicken feathers were mixed into compost to enrich for microbial communities involved in the degradation of keratin and other recalcitrant proteins such as prions. Feathers altered the composition of bacterial and fungal communities primarily during the first cycle. The bacterial genera Saccharomonospora, Thermobifida, Thermoactinomycetaceae, Thiohalospira, Pseudomonas, Actinomadura, and Enterobacter, and the fungal genera Dothideomycetes, Cladosporium, Chaetomium, and Trichaptum were identified as candidates involved in SRM degradation. Feathers increased (P 4 primarily during the early stages of the first cycle and N2O during the second. Although inclusion of feathers in compost increases greenhouse gas emissions, it may promote the establishment of microbial communities that are more adept at degrading SRM and recalcitrant proteins such as keratin and PrPSc
Kolb Ayre, Kimberley; Caldwell, Colleen A.; Stinson, Jonah; Landis, Wayne G.
2014-01-01
Introduction and spread of the parasite Myxobolus cerebralis, the causative agent of whirling disease, has contributed to the collapse of wild trout populations throughout the intermountain west. Of concern is the risk the disease may have on conservation and recovery of native cutthroat trout. We employed a Bayesian belief network to assess probability of whirling disease in Colorado River and Rio Grande cutthroat trout (Oncorhynchus clarkii pleuriticus and Oncorhynchus clarkii virginalis, respectively) within their current ranges in the southwest United States. Available habitat (as defined by gradient and elevation) for intermediate oligochaete worm host, Tubifex tubifex, exerted the greatest influence on the likelihood of infection, yet prevalence of stream barriers also affected the risk outcome. Management areas that had the highest likelihood of infected Colorado River cutthroat trout were in the eastern portion of their range, although the probability of infection was highest for populations in the southern, San Juan subbasin. Rio Grande cutthroat trout had a relatively low likelihood of infection, with populations in the southernmost Pecos management area predicted to be at greatest risk. The Bayesian risk assessment model predicted the likelihood of whirling disease infection from its principal transmission vector, fish movement, and suggested that barriers may be effective in reducing risk of exposure to native trout populations. Data gaps, especially with regard to location of spawning, highlighted the importance in developing monitoring plans that support future risk assessments and adaptive management for subspecies of cutthroat trout.
Ayre, Kimberley Kolb; Caldwell, Colleen A; Stinson, Jonah; Landis, Wayne G
2014-09-01
Introduction and spread of the parasite Myxobolus cerebralis, the causative agent of whirling disease, has contributed to the collapse of wild trout populations throughout the intermountain west. Of concern is the risk the disease may have on conservation and recovery of native cutthroat trout. We employed a Bayesian belief network to assess probability of whirling disease in Colorado River and Rio Grande cutthroat trout (Oncorhynchus clarkii pleuriticus and Oncorhynchus clarkii virginalis, respectively) within their current ranges in the southwest United States. Available habitat (as defined by gradient and elevation) for intermediate oligochaete worm host, Tubifex tubifex, exerted the greatest influence on the likelihood of infection, yet prevalence of stream barriers also affected the risk outcome. Management areas that had the highest likelihood of infected Colorado River cutthroat trout were in the eastern portion of their range, although the probability of infection was highest for populations in the southern, San Juan subbasin. Rio Grande cutthroat trout had a relatively low likelihood of infection, with populations in the southernmost Pecos management area predicted to be at greatest risk. The Bayesian risk assessment model predicted the likelihood of whirling disease infection from its principal transmission vector, fish movement, and suggested that barriers may be effective in reducing risk of exposure to native trout populations. Data gaps, especially with regard to location of spawning, highlighted the importance in developing monitoring plans that support future risk assessments and adaptive management for subspecies of cutthroat trout. PMID:24660663
Diet and Gut Microbial Function in Metabolic and Cardiovascular Disease Risk.
Meyer, Katie A; Bennett, Brian J
2016-10-01
Over the past decade, the gut microbiome has emerged as a novel and largely unexplored source of variability for metabolic and cardiovascular disease risk, including diabetes. Animal and human studies support several possible pathways through which the gut microbiome may impact health, including the production of health-related metabolites from dietary sources. Diet is considered important to shaping the gut microbiota; in addition, gut microbiota influence the metabolism of many dietary components. In the present paper, we address the distinction between compositional and functional analysis of the gut microbiota. We focus on literature that highlights the value of moving beyond surveys of microbial composition to measuring gut microbial functioning to delineate mechanisms related to the interplay between diet and gut microbiota in cardiometabolic health. PMID:27541295
Directory of Open Access Journals (Sweden)
Rodríguez-Prieto Víctor
2012-08-01
Full Text Available Abstract Background Bovine tuberculosis (bTB is a chronic infectious disease mainly caused by Mycobacterium bovis. Although eradication is a priority for the European authorities, bTB remains active or even increasing in many countries, causing significant economic losses. The integral consideration of epidemiological factors is crucial to more cost-effectively allocate control measures. The aim of this study was to identify the nature and extent of the association between TB distribution and a list of potential risk factors regarding cattle, wild ungulates and environmental aspects in Ciudad Real, a Spanish province with one of the highest TB herd prevalences. Results We used a Bayesian mixed effects multivariable logistic regression model to predict TB occurrence in either domestic or wild mammals per municipality in 2007 by using information from the previous year. The municipal TB distribution and endemicity was clustered in the western part of the region and clearly overlapped with the explanatory variables identified in the final model: (1 incident cattle farms, (2 number of years of veterinary inspection of big game hunting events, (3 prevalence in wild boar, (4 number of sampled cattle, (5 persistent bTB-infected cattle farms, (6 prevalence in red deer, (7 proportion of beef farms, and (8 farms devoted to bullfighting cattle. Conclusions The combination of these eight variables in the final model highlights the importance of the persistence of the infection in the hosts, surveillance efforts and some cattle management choices in the circulation of M. bovis in the region. The spatial distribution of these variables, together with particular Mediterranean features that favour the wildlife-livestock interface may explain the M. bovis persistence in this region. Sanitary authorities should allocate efforts towards specific areas and epidemiological situations where the wildlife-livestock interface seems to critically hamper the definitive b
Carboy Security Risk Analysis Model of I and C System Using Bayesian Network
International Nuclear Information System (INIS)
The Korea Institute of Nuclear Safety (KINS) as a regulatory agency declares the R. G 8.22 for applying cyber security in Korea in 2011. In nuclear power industrial, ShinUljin 1, 2 unit and Shingori 3, 4 unit are demonstrating the cyber security for the first time. And in terms of research, the National Security Research Institute and the Korea Atomic Energy Research Institute are developing the nuclear power plant cyber security system in Korean. Currently, these cyber securities like regulation, demonstration and research are focused on nuclear power plant. However, cyber security is also important for the nuclear research reactor like a HANARO which is in Daejeon, primarily due to its characteristic as research reactor since since people access more than power plant. Analysis of the key elements of cyber security is possible to study through the activity-quality and architecture analysis model of cyber security. It is possible to analyze the extent reflected final risk by evaluating input score for each checklist. In this way, you can see an important checklist. Further, if the cyber-attack occurs, it is possible to provide an evidentiary material that is able to determine the key check element corresponding to each situation via a reverse calculation of BN. Finally, Utilization is possible to create a simulated penetratio test scenario according to each situation. Analysis of the key elements of cyber security is possible to study through the activity-quality and architecture analysis model of cyber security. It is possible to analyze the extent reflected in the final risk by evaluating input score for each checklist, in this way, you can see an important checklist. Furthermore, if the cyber-attack occurs, it is possible to provide an evidentiary material that enables to determine the key check element corresponding to each situation via a reverse calculation of BN. Finally, Utilization is possible to create a simulated penetration test scenario according to
Carboy Security Risk Analysis Model of I and C System Using Bayesian Network
Energy Technology Data Exchange (ETDEWEB)
Shin, Jinsoo; Heo, Gyunyoung [Kyung Hee Univ., Yongin (Korea, Republic of); Son, Hanseong [Joongbu Univ., Geumsan (Korea, Republic of); Park, Jaekwan [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2013-05-15
The Korea Institute of Nuclear Safety (KINS) as a regulatory agency declares the R. G 8.22 for applying cyber security in Korea in 2011. In nuclear power industrial, ShinUljin 1, 2 unit and Shingori 3, 4 unit are demonstrating the cyber security for the first time. And in terms of research, the National Security Research Institute and the Korea Atomic Energy Research Institute are developing the nuclear power plant cyber security system in Korean. Currently, these cyber securities like regulation, demonstration and research are focused on nuclear power plant. However, cyber security is also important for the nuclear research reactor like a HANARO which is in Daejeon, primarily due to its characteristic as research reactor since since people access more than power plant. Analysis of the key elements of cyber security is possible to study through the activity-quality and architecture analysis model of cyber security. It is possible to analyze the extent reflected final risk by evaluating input score for each checklist. In this way, you can see an important checklist. Further, if the cyber-attack occurs, it is possible to provide an evidentiary material that is able to determine the key check element corresponding to each situation via a reverse calculation of BN. Finally, Utilization is possible to create a simulated penetratio test scenario according to each situation. Analysis of the key elements of cyber security is possible to study through the activity-quality and architecture analysis model of cyber security. It is possible to analyze the extent reflected in the final risk by evaluating input score for each checklist, in this way, you can see an important checklist. Furthermore, if the cyber-attack occurs, it is possible to provide an evidentiary material that enables to determine the key check element corresponding to each situation via a reverse calculation of BN. Finally, Utilization is possible to create a simulated penetration test scenario according to
Lesaffre, Emmanuel
2012-01-01
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd
Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6
Directory of Open Access Journals (Sweden)
Dan B. Jensen
2013-09-01
Full Text Available Background: Prediction of the optimal habitat conditions for a given bacterium, based on genome sequence alone would be of value for scientific as well as industrial purposes. One example of such a habitat adaptation is the requirement for oxygen. In spite of good genome data availability, there have been only a few prediction attempts of bacterial oxygen requirements, using genome sequences. Here, we describe a method for distinguishing aerobic, anaerobic and facultative anaerobic bacteria, based on genome sequence-derived input, using naive Bayesian inference. In contrast, other studies found in literature only demonstrate the ability to distinguish two classes at a time. Results: The results shown in the present study are as good as or better than comparable methods previously described in the scientific literature, with an arguably simpler method, when results are directly compared. This method further compares the performance of a single-step naive Bayesian prediction of the three included classifications, compared to a simple Bayesian network with two steps. A two-step network, distinguishing first respiring from non-respiring organisms, followed by the distinction of aerobe and facultative anaerobe organisms within the respiring group, is found to perform best. Conclusions: A simple naive Bayesian network based on the presence or absence of specific protein domains within a genome is an effective and easy way to predict bacterial habitat preferences, such as oxygen requirement.
Draper, D.
2001-01-01
© 2012 Springer Science+Business Media, LLC. All rights reserved. Article Outline: Glossary Definition of the Subject and Introduction The Bayesian Statistical Paradigm Three Examples Comparison with the Frequentist Statistical Paradigm Future Directions Bibliography
Directory of Open Access Journals (Sweden)
Enoch C. T. So
2013-12-01
Full Text Available Background: The white coat is traditionally considered to be the appropriate attire for physicians but it may also be contaminated with microbes and act as a potential source of infection. We aimed to study patients’ acceptance of physicians’ attire, their underlying reasons, and their reactions to an educational intervention with regards to the risks of contamination. Methods: We conducted a voluntary questionnaire survey at a university teaching hospital in Hong Kong from February to July 2012. 262 patient-responses from adult inpatients and outpatients across various specialties were analysed. Results: White coats were highly favoured (90.8% when compared with scrubs (22.1%, smart casual (7.6% and formal (7.3% wears. ’Professional image’ and ‘ease of identification’ were the main attributes of the white coat. Most patients (92.2% would prefer doctors washing their white coats every few days, whilst 80.9% believed that doctors were actually doing so. After patients were informed of the potential risk of microbial contamination, white coats remained as the most favoured attire (66.4%, but with scrubs doubling in popularity (45.8%. Smart casual (9.2% and formal attire (4.6% remain the least accepted. Conclusion: Despite cross-infections being a significant concern within the healthcare environments, patients’ predominant acceptance and perceived attributes towards the white coat were maintained after an educational intervention on the risks of microbial contamination.
Carducci, Annalaura; Donzelli, Gabriele; Cioni, Lorenzo; Verani, Marco
2016-01-01
Quantitative Microbial Risk Assessment (QMRA) methodology, which has already been applied to drinking water and food safety, may also be applied to risk assessment and management at the workplace. The present study developed a preliminary QMRA model to assess microbial risk that is associated with inhaling bioaerosols that are contaminated with human adenovirus (HAdV). This model has been applied to air contamination data from different occupational settings, including wastewater systems, solid waste landfills, and toilets in healthcare settings and offices, with different exposure times. Virological monitoring showed the presence of HAdVs in all the evaluated settings, thus confirming that HAdV is widespread, but with different average concentrations of the virus. The QMRA results, based on these concentrations, showed that toilets had the highest probability of viral infection, followed by wastewater treatment plants and municipal solid waste landfills. Our QMRA approach in occupational settings is novel, and certain caveats should be considered. Nonetheless, we believe it is worthy of further discussions and investigations. PMID:27447658
The influence of processing on the microbial risk associated with Rooibos (Aspalathus linearis) tea.
Gouws, Pieter; Hartel, Toni; van Wyk, Rudean
2014-12-01
This review discusses the influence of processing on the microbial risk associated with Salmonella in Rooibos tea, the identification of Salmonella and preventative and control measures to control microbial contamination. Rooibos tea, like other plant products, naturally contains a high microbial load. Downstream processing steps of these products usually help in reducing any contaminants present. Due to the delicate flavour properties and nature of Rooibos, gentle processing techniques are necessary for the production of good quality tea. However, this has a major influence on the microbiological status of the product. The presence of Salmonella in Rooibos is poorly understood. The ubiquitous distribution of Salmonella in the natural environment and its prevalence in the global food chain, the physiological adaptability, virulence of the bacterial pathogen and its serious economic impact on the food industry, emphasises the need for continued awareness and stringent controls at all levels of food production. With the advances of technology and information at hand, the processing of Rooibos needs to be re-evaluated. Since the delicate nature of Rooibos prohibits the use of harsh methods to control Salmonella, alternative methods for the steam pasteurisation of Rooibos show great potential to control Salmonella in a fast, efficient and cost-effective manner. These alternative methods will significantly improve the microbiological quality of Rooibos and provide a product that is safe to consumers. PMID:24789759
Eregno, Fasil Ejigu; Tryland, Ingun; Tjomsland, Torulv; Myrmel, Mette; Robertson, Lucy; Heistad, Arve
2016-04-01
This study investigated the public health risk from exposure to infectious microorganisms at Sandvika recreational beaches, Norway and dose-response relationships by combining hydrodynamic modelling with Quantitative Microbial Risk Assessment (QMRA). Meteorological and hydrological data were collected to produce a calibrated hydrodynamic model using Escherichia coli as an indicator of faecal contamination. Based on average concentrations of reference pathogens (norovirus, Campylobacter, Salmonella, Giardia and Cryptosporidium) relative to E. coli in Norwegian sewage from previous studies, the hydrodynamic model was used for simulating the concentrations of pathogens at the local beaches during and after a heavy rainfall event, using three different decay rates. The simulated concentrations were used as input for QMRA and the public health risk was estimated as probability of infection from a single exposure of bathers during the three consecutive days after the rainfall event. The level of risk on the first day after the rainfall event was acceptable for the bacterial and parasitic reference pathogens, but high for the viral reference pathogen at all beaches, and severe at Kalvøya-small and Kalvøya-big beaches, supporting the advice of avoiding swimming in the day(s) after heavy rainfall. The study demonstrates the potential of combining discharge-based hydrodynamic modelling with QMRA in the context of bathing water as a tool to evaluate public health risk and support beach management decisions. PMID:26802355
Bioaerosol Deposition to Food Crops near Manure Application: Quantitative Microbial Risk Assessment.
Jahne, Michael A; Rogers, Shane W; Holsen, Thomas M; Grimberg, Stefan J; Ramler, Ivan P; Kim, Seungo
2016-03-01
Production of both livestock and food crops are central priorities of agriculture; however, food safety concerns arise where these practices intersect. In this study, we investigated the public health risks associated with potential bioaerosol deposition to crops grown in the vicinity of manure application sites. A field sampling campaign at dairy manure application sites supported the emission, transport, and deposition modeling of bioaerosols emitted from these lands following application activities. Results were coupled with a quantitative microbial risk assessment model to estimate the infection risk due to consumption of leafy green vegetable crops grown at various distances downwind from the application area. Inactivation of pathogens ( spp., spp., and O157:H7) on both the manure-amended field and on crops was considered to determine the maximum loading of pathogens to plants with time following application. Overall median one-time infection risks at the time of maximum loading decreased from 1:1300 at 0 m directly downwind from the field to 1:6700 at 100 m and 1:92,000 at 1000 m; peak risks (95th percentiles) were considerably greater (1:18, 1:89, and 1:1200, respectively). Median risk was below 1:10,000 at >160 m downwind. As such, it is recommended that a 160-m setback distance is provided between manure application and nearby leafy green crop production. Additional distance or delay before harvest will provide further protection of public health. PMID:27065414
Energy Technology Data Exchange (ETDEWEB)
Xu, Shanwei [Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2P5 (Canada); Agriculture and Agri-Food Canada, Lethbridge Research Centre, P.O. Box 3000, Lethbridge, Alberta, Canada T1J 4B1 (Canada); Reuter, Tim [Alberta Agriculture and Rural Development, Lethbridge, Alberta, Canada T1J 4V6 (Canada); Gilroyed, Brandon H. [Agriculture and Agri-Food Canada, Lethbridge Research Centre, P.O. Box 3000, Lethbridge, Alberta, Canada T1J 4B1 (Canada); Tymensen, Lisa [Alberta Agriculture and Rural Development, Lethbridge, Alberta, Canada T1J 4V6 (Canada); Hao, Yongxin; Hao, Xiying [Agriculture and Agri-Food Canada, Lethbridge Research Centre, P.O. Box 3000, Lethbridge, Alberta, Canada T1J 4B1 (Canada); Belosevic, Miodrag [Department of Biological Science, University of Alberta, Edmonton, Alberta, Canada T6G 2E9 (Canada); Leonard, Jerry J. [Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2P5 (Canada); McAllister, Tim A., E-mail: tim.mcallister@agr.gc.ca [Agriculture and Agri-Food Canada, Lethbridge Research Centre, P.O. Box 3000, Lethbridge, Alberta, Canada T1J 4B1 (Canada)
2013-06-15
Highlights: ► Addition of feathers altered bacterial and fungal communities in compost. ► Microbial communities degrading SRM and compost matrix were distinct. ► Addition of feathers may enrich for microbial communities that degrade SRM. ► Inclusion of feather in compost increased both CH{sub 4} and N{sub 2}O emissions from compost. ► Density of methanogens and methanotrophs were weakly associated with CH{sub 4} emissions. - Abstract: Provided that infectious prions (PrP{sup Sc}) are inactivated, composting of specified risk material (SRM) may be a viable alternative to rendering and landfilling. In this study, bacterial and fungal communities as well as greenhouse gas emissions associated with the degradation of SRM were examined in laboratory composters over two 14 day composting cycles. Chicken feathers were mixed into compost to enrich for microbial communities involved in the degradation of keratin and other recalcitrant proteins such as prions. Feathers altered the composition of bacterial and fungal communities primarily during the first cycle. The bacterial genera Saccharomonospora, Thermobifida, Thermoactinomycetaceae, Thiohalospira, Pseudomonas, Actinomadura, and Enterobacter, and the fungal genera Dothideomycetes, Cladosporium, Chaetomium, and Trichaptum were identified as candidates involved in SRM degradation. Feathers increased (P < 0.05) headspace concentrations of CH{sub 4} primarily during the early stages of the first cycle and N{sub 2}O during the second. Although inclusion of feathers in compost increases greenhouse gas emissions, it may promote the establishment of microbial communities that are more adept at degrading SRM and recalcitrant proteins such as keratin and PrP{sup Sc}.
Microbial Health Risks Associated with Exposure to Stormwater in a Water Plaza
Sales-Ortells, Helena; Medema, Gertjan
2015-04-01
Climate change scenarios predict an increase of intense rainfall events in summer in Western Europe. Current urban drainage systems cannot cope with such intense precipitation events. Cities are constructing local stormwater storage facilities to prevent pluvial flooding. Combining storage with other functions, such as recreation, may lead to exposure to contaminants. This study assessed the microbial quality of rainwater collected in a water plaza in Rotterdam (The Netherlands) and the health risks associated with recreational exposure. The water plaza collects street run-off, diverges first flush to the sewer system and stores the rest of the run-off in the plaza as open water. A rain simulation experiment was conducted using drinking water from fire hydrants. The water flowed over the street pavement into the street gutters and into the square. Samples were collected from the first flush diverted water and from two different levels of the water plaza at different points in time. Campylobacter spp., Cryptosporidium, and Legionella pneumophila were the pathogens investigated, using quantitative PCR. Escherichia coli was quantified with culture methods to obtain information on faecal contamination. Microbial source tracking tools (human Bacteroides, avian Helicobacter and canine mitochondrial DNA, all analysed with quantitative PCR) were used to determine the origin (human, animal) of the intestinal pathogens. To estimate the health risks for children playing in the water plaza after a rain event, a quantitative microbial risk assessment model was built. The volume of water ingested was obtained from literature on similar locations (flooded streets). Published dose-response models were used to calculate the risk per event. Exposure frequency was estimated using weather data (precipitation events). E. coli concentrations were below the level for excellent bathing water in the EU Bathing Water Directive. Cryptosporidium was not found in any sample. Campylobacter spp
Directory of Open Access Journals (Sweden)
Ana M. Vicedo-Cabrera
2013-11-01
Full Text Available A core challenge in epidemiological analysis of the impact of exposure to air pollution on health is assessment of the individual exposure for subjects at risk. Geographical information systems (GIS-based pollution mapping, such as kriging, has become one of the main tools for evaluating individual exposure to ambient pollutants. We applied universal Bayesian kriging to estimate the residential exposure to gaseous air pollutants for children living in a high-risk area (Milazzo- Valle del Mela in Sicily, Italy. Ad hoc air quality monitoring campaigns were carried out: 12 weekly measurements for sulphur dioxide (SO2 and nitrogen dioxide (NO2 were obtained from 21 passive dosimeters located at each school yard of the study area from November 2007 to April 2008. Universal Bayesian kriging was performed to predict individual exposure levels at each residential address for all 6- to 12-years-old children attending primary school at various locations in the study area. Land use, altitude, distance to main roads and population density were included as covariates in the models. A large geographical heterogeneity in air quality was recorded suggesting complex exposure patterns. We obtained a predicted mean level of 25.78 (±10.61 μg/m3 of NO2 and 4.10 (±2.71 μg/m3 of SO2 at 1,682 children’s residential addresses, with a normalised root mean squared error of 28% and 25%, respectively. We conclude that universal Bayesian kriging approach is a useful tool for the assessment of realistic exposure estimates with regard to ambient pollutants at home addresses. Its prediction uncertainty is highly informative and can be used for both designing subsequent campaigns and for improved modelling of epidemiological associations.
Risk-benefit assessment of cold-smoked salmon: microbial risk versus nutritional benefit
DEFF Research Database (Denmark)
Berjia, Firew Lemma; Hoekstra, Jeljer; Andersen, Rikke;
2012-01-01
endpoints due to exposure to the pathogen L. monocytogenes. Two consumption scenarios were considered: a reference scenario (23g/day and 20g/day for man and woman respectively) and an alternative scenario (40g/day for both sexes). In order to evaluate and compare the risks and benefits, the Disability...
Corrosion risk associated with microbial souring control using nitrate or nitrite.
Hubert, Casey; Nemati, Mehdi; Jenneman, Gary; Voordouw, Gerrit
2005-08-01
Souring, the production of hydrogen sulfide by sulfate-reducing bacteria (SRB) in oil reservoirs, can be controlled through nitrate or nitrite addition. To assess the effects of this containment approach on corrosion, metal coupons were installed in up-flow packed-bed bioreactors fed with medium containing 8 mM sulfate and 25 mM lactate. Following inoculation with produced water to establish biogenic H(2)S production, some bioreactors were treated with 17.5 mM nitrate or up to 20 mM nitrite, eliminating souring. Corrosion rates were highest near the outlet of untreated bioreactors (up to 0.4 mm year(-1)). Nitrate (17.5 mM) eliminated sulfide but gave pitting corrosion near the inlet of the bioreactor, whereas a high nitrite dose (20 mM) completely eliminated microbial activity and associated corrosion. More gradual, step-wise addition of nitrite up to 20 mM resulted in the retention of microbial activity and localized pitting corrosion, especially near the bioreactor inlet. We conclude that: (1) SRB control by nitrate or nitrite reduction shifts the corrosion risk from the bioreactor outlet to the inlet (i.e. from production to injection wells) and (2) souring treatment by continuous addition of a high inhibitory nitrite dose is preferable from a corrosion-prevention point of view. PMID:15711941
Sieroslawska, Anna
2014-07-01
The aim of the study was to elucidate the usefulness of the Microbial Assay for Risk Assessment (MARA) to evaluate toxicity in samples containing cyanobacterial products. Cyanobacterial extracts with different cyanotoxin contents and pure cyanotoxins-microcystin-LR, cylindrospermopsin and anatoxin-a-were tested. On the basis of the microbial reaction, MARA indicated only slight or no toxicity in the studied extracts. Similarly, no or low toxicity of pure toxins was detected at the concentrations used (up to 10 μg/ml). Weak relationships between the reactions of individual organisms exposed to cyanotoxin-containing extracts and to the same pure toxins were observed. On the other hand, inhibition of some organisms, such as Pichia anomalia, whose growth was not impacted by pure cyanotoxins, indicated the presence of other biologically active compounds in the studied extracts. In conclusion, MARA assay is not enough sensitive to be used as a good tool for cyanotoxin screening. It may, however, be applied in searching for antimicrobial/antifungal cyanobacteria-derived compounds. PMID:24682641
GIS-based analysis of drinking-water supply structures: a module for microbial risk assessment.
Kistemann, T; Herbst, S; Dangendorf, F; Exner, M
2001-05-01
Water-related infections constitute an important health impact world-wide. A set of tools serving for Microbial Risk Assessment (MRA) of waterborne diseases should comprise the entire drinking-water management system and take into account the Hazard Analysis and Critical Control Point (HACCP) concept which provides specific Critical Control Points (CCPs) reflecting each step of drinking-water provision. A Geographical Information System (GIS) study concerning water-supply structure (WSS) was conducted in the Rhein-Berg District (North Rhine-Westphalia, Germany). As a result, suitability of the existing water databases HYGRIS (hydrological basis geo-information system) and TEIS (drinking-water recording and information system) for the development of a WSS-GIS module could be demonstrated. Spatial patterns within the integrated raw and drinking-water data can easily be uncovered by GIS-specific options. The application of WSS-GIS allows a rapid visualization and analysis of drinking-water supply structure and offers huge advantages concerning microbial monitoring of raw and drinking water as well as recognition and investigation of incidents and outbreaks. Increasing requests regarding health protection and health reporting, demands for a better outbreak management and water-related health impacts of global climate change are major challenges of future water management to be tackled with methods including spatial analysis. GIS is assumed to be a very useful tool to meet these requirements. PMID:11434210
Institute of Scientific and Technical Information of China (English)
刘丽梅; 高永超; 王玎
2014-01-01
Varieties of risk factors such as operating environment, personnel, equipment may bring microbiological hazards into foods. In order to effectively implement risk management,we used the modular process risk modeling framework to improve the quantitative microbiological risk assessment method. In this method, risk factors were abstracted as a hazard transfer modular. Food safety risk factors included the sources of raw materials, storage risk, operational personnel hygiene, and environmental contamination risks. The hazard transfer process described the contamination frequency and the probability distribution of microbial quantity that introduced into a product by environment, operating personnel, equipment and other risk factors. If risk did not exist, the amount of microbial number was zero. If risk existed, the probability distribution of microbial number was defined with discrete or continuous distribution function. The proportion transferred to a product was described by function g(x) with operating time, temperature, contact area and etc. The control process described varieties of control measures such as the use of different disinfection, the implementation of different test frequencies may be taken in the production. The prevention and control impacts of a measure were described by the probability and quantity change of microbial number introduced by risk factors in the model. The utility modular was used to characterize the consumption cost and gain of the control measure. A modular process risk model can be established by Bayesian network by the following three steps:1) Define processes, materials mixing and partitioning, and processing parameters, select risk factors that may introduce microbes into a product and affect the microbial dynamics; 2) Select the appropriate basic processes, and define the Bayesian network structure of risk model;3) Collect risk data, and define the conditional probability of each node in the model by analyzing risk data
Bayesian Benchmark Dose Analysis
Fang, Qijun; Piegorsch, Walter W.; Barnes, Katherine Y.
2014-01-01
An important objective in environmental risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs) that induce a pre-specified Benchmark Response (BMR) in a target population. Established inferential approaches for BMD analysis typically involve one-sided, frequentist confidence limits, leading in practice to what are called Benchmark Dose Lower Limits (BMDLs). Appeal to Bayesian modeling and credible limits for building BMDLs is far less developed, however. Indee...
Energy Technology Data Exchange (ETDEWEB)
Vinneraas, Bjoern [Swedish University of Agricultural Sciences, Department of Biometry and Engineering, Box 7032, SE-750 07 Uppsala (Sweden); Schoenning, Caroline [Swedish Institute for Infectious Disease Control, Department of Parasitology, Mycology, Environmental Mirobiology and Water, SE-171 82 Solna (Sweden); Nordin, Annika [National Veterinary Institute, Department of Wild Life, Fish and Environment, SE-751 89 Uppsala (Sweden)
2006-08-31
The plans for introducing biogas produced from organic waste to the pipe system for natural gas has raised concerns about the risk of transmitting disease via the gas. To assess this risk, condensate water from gas pipes and gas from different parts of a biogas upgrading system were sampled and cultured for microbial content. On average, 10{sup 5} cfu ml{sup -1} were found in the condensate water throughout the system, while in the gas between 10 and 100 cfu m{sup -3} were found. The microorganisms were subjected to further identification and found to represent a wide variety, e.g. fungi and spore-forming and non-spore-forming bacteria, including species such as Enterobacteriaceae. The number of microorganisms found in the biogas corresponded to the densities in sampled natural gas, which also held 10-100 cfu m{sup -3}. Since no pathogens were identified and since the exposure to gas from e.g. cookers and refuelling of cars may only result in the inhalation of small volumes of gas, the risk of spreading disease via biogas was judged to be very low. (author)
Middle East Desert Dust Exposure: Health Risks from Metals and Microbial Pathogens
Lyles, M. B.
2014-12-01
In the Middle East, dust and sand storms are a persistent problem and can deliver significant amounts of micro-particulates via inhalation into the mouth, nasal pharynx, & lungs due to the fine size and abundance of these micro-particulates. The chronic and acute health risks of this dust inhalation have not been well studied nor has the dust been effectively characterized as to its chemical composition, mineral content, or microbial flora. Scientific experiments were designed to study the Kuwaiti and Iraqi dust as to its physical, chemical, and biological characteristics and for its potential to cause adverse health effects. First, dust samples from different locations were collected and processed and exposure data collected. Initial chemical and physical characterization of each sample including particle size distribution and inorganic analysis was conducted, followed by characterization of biologic flora of the dust, including bacteria, fungi and viruses. Data indicates that the mineralized dust is composed of calcium carbonate over a matrix of metallic silicate nanocrystals containing a variety of trace and heavy metals constituting ~3 % of the PM10 particles by weight, of which ~1% is bioaccessible aluminum and reactive iron, each. The particles also consist of ~1% bioavailable aluminum and reactive iron each. Microbial analysis reveals a significant biodiversity of bacterial, fungi, and viruses of which ~30% are known pathogens. Of the microbes identified, several have hemolytic properties and most have significant antibiotic resistance. Viral analysis indicates a tremendous amount of virons with a large percent of RNA viruses. The level of total suspended particle mass at PM 10 along with environmental & physiological conditions present constitute an excessive exposure to micro-particulates including PM 2.5 and the potential for adverse health effects. Reported data on cell culture and animal studies have indicated a high level of toxicity to these dust
Decker, Jody F.; Slawson, Robin M.
2012-01-01
Objective: The aim of this Canadian study was to assess student behavioral response to disease transmission risk, while identifying high microbial deposition/transmission sites. Participants: A student survey was conducted during October 2009. Methods: The methods included a survey of students to assess use of health services, vaccination…
Directory of Open Access Journals (Sweden)
Beatriz Martínez-López
2014-05-01
Full Text Available The spatial pattern and epidemiology of backyard pig farming and other low bio-security pig production systems and their role in the occurrence of classical swine fever (CSF is described and evaluated. A spatial Bayesian model was used to explore the risk factors, including human demographics, socioeconomic and environmental factors. The analyses were performed for Bulgaria, which has a large number of backyard farms (96% of all pig farms in the country are classified as backyard farms, and it is one of the countries for which both backyard pig and farm counts were available. Results reveal that the high-risk areas are typically concentrated in areas with small family farms, high numbers of outgoing pig shipments and low levels of personal consumption (i.e. economically deprived areas. Identification of risk factors and high-risk areas for CSF will allow to targeting risk-based surveillance strategies leading to prevention, control and, ultimately, elimination of the disease in Bulgaria and other countries with similar socio-epidemiological conditions.
Kirstein, Roland
2005-01-01
This paper presents a modification of the inspection game: The ?Bayesian Monitoring? model rests on the assumption that judges are interested in enforcing compliant behavior and making correct decisions. They may base their judgements on an informative but imperfect signal which can be generated costlessly. In the original inspection game, monitoring is costly and generates a perfectly informative signal. While the inspection game has only one mixed strategy equilibrium, three Perfect Bayesia...
Directory of Open Access Journals (Sweden)
Rosalba Portuesi
Full Text Available BACKGROUND: Determining genetic risk is a fundamental prerequisite for the implementation of primary prevention trials for type 1 diabetes (T1D. The aim of this study was to assess the risk conferred by HLA-DRB1, INS-VNTR and PTPN22 single genes on the onset of T1D and the joint risk conferred by all these three susceptibility loci using the Bayesian Network (BN approach in both population-based case-control and family clustering data sets. METHODOLOGY/PRINCIPAL FINDINGS: A case-control French cohort, consisting of 868 T1D patients and 73 French control subjects, a French family data set consisting of 1694 T1D patients and 2340 controls were analysed. We studied both samples separately applying the BN probabilistic approach, that is a graphical model that encodes probabilistic relationships among variables of interest. As expected HLA-DRB1 is the most relevant susceptibility gene. We proved that INS and PTPN22 genes marginally influence T1D risk in all risk HLA-DRB1 genotype categories. The absolute risk conferred by carrying simultaneously high, moderate or low risk HLA-DRB1 genotypes together with at risk INS and PTPN22 genotypes, was 11.5%, 1.7% and 0.1% in the case-control sample and 19.8%, 6.6% and 2.2% in the family cohort, respectively. CONCLUSIONS/SIGNIFICANCE: This work represents, to the best of our knowledge, the first study based on both case-control and family data sets, showing the joint effect of HLA, INS and PTPN22 in a T1D Caucasian population with a wide range of age at T1D onset, adding new insights to previous findings regarding data sets consisting of patients and controls <15 years at onset.
Microbial quality and associated health risks of raw milk marketed in the Tanga region of Tanzania
Institute of Scientific and Technical Information of China (English)
Swai ES; Schoonman L
2011-01-01
Objective: To evaluate microbial quality and associated health risks of raw milk marketed in the Tanga region of Tanzania. Methods: A microbial quality assessment of marketed raw milk was undertaken by evaluating 59 samples of milk from selling points (collecting centres =15), bicycle boys (12) and kiosks/restaurants (32) in Tanga city during April-May 2005. Quality and milk-borne hazards were assessed using a combination of tests in order to quantify the occurrence ofBrucellosis well as standard plate count (SPC). Specific gravity (SG) determination was used as an indicator of adulteration. Results: The mean coliform plate count (c.f.u/mL) of milk handled by bicycle boys (4.2×106) was significantly higher than that handled by collecting centres (3.0×106) and kiosk/ restaurants (1.4×106), respectively (P < 0.05). Of the 59 milk samples collected, 33 (56%) were Brucella milk ring test (MRT)-positive and 78% and 17% of the samples graded satisfactorily based on SG and coliform plate counts as prescribed by East African Community standards for raw milk. There was no verocytotoxigenic E. coli (VTEC) O157: H7 in any of the milk samples collected and analysed during the present study. Conclusions: It can be concluded that raw market milk in the study area is of poor bacteriological quality and hazardous for human consumption. This highlights the need to implement good hygiene practices and effective monitoring from production through the delivery chain to the consumer. Further studies are needed for detection of toxins that are produced by E. coli, other pathogenic spore forming bacteria (Bacillus spp. and Clostridium spp.) and other harmful microorganisms. (milk ring test), Escherichia coli (E. coli) O157:H7 (culture), the coliform bacteria as
Bessiere, Pierre; Ahuactzin, Juan Manuel; Mekhnacha, Kamel
2013-01-01
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean
Ranta, Jukka; Lindqvist, Roland; Hansson, Ingrid; Tuominen, Pirkko; Nauta, Maarten
2015-01-01
Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation of MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and con...
The introduction of bacteria into aquifers for bioremediation purposes requires monitoring of the persistence and activity of microbial populations for efficacy and risk assessment purposes. Burkholderia cepacia G4 PR1 constitutively expresses a toluene ortho-monooxygenase (tom) ...
Larson, Nicholas B; McDonnell, Shannon; Albright, Lisa Cannon; Teerlink, Craig; Stanford, Janet; Ostrander, Elaine A; Isaacs, William B; Xu, Jianfeng; Cooney, Kathleen A; Lange, Ethan; Schleutker, Johanna; Carpten, John D; Powell, Isaac; Bailey-Wilson, Joan; Cussenot, Olivier; Cancel-Tassin, Geraldine; Giles, Graham; MacInnis, Robert; Maier, Christiane; Whittemore, Alice S; Hsieh, Chih-Lin; Wiklund, Fredrik; Catolona, William J; Foulkes, William; Mandal, Diptasri; Eeles, Rosalind; Kote-Jarai, Zsofia; Ackerman, Michael J; Olson, Timothy M; Klein, Christopher J; Thibodeau, Stephen N; Schaid, Daniel J
2016-09-01
Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work. PMID:27312771
QMRAcatch: Microbial Quality Simulation of Water Resources including Infection Risk Assessment.
Schijven, Jack; Derx, Julia; de Roda Husman, Ana Maria; Blaschke, Alfred Paul; Farnleitner, Andreas H
2015-09-01
Given the complex hydrologic dynamics of water catchments and conflicts between nature protection and public water supply, models may help to understand catchment dynamics and evaluate contamination scenarios and may support best environmental practices and water safety management. A catchment model can be an educative tool for investigating water quality and for communication between parties with different interests in the catchment. This article introduces an interactive computational tool, QMRAcatch, that was developed to simulate concentrations in water resources of , a human-associated microbial source tracking (MST) marker, enterovirus, norovirus, , and as target microorganisms and viruses (TMVs). The model domain encompasses a main river with wastewater discharges and a floodplain with a floodplain river. Diffuse agricultural sources of TMVs that discharge into the main river are not included in this stage of development. The floodplain river is fed by the main river and may flood the plain. Discharged TMVs in the river are subject to dilution and temperature-dependent degradation. River travel times are calculated using the Manning-Gauckler-Strickler formula. Fecal deposits from wildlife, birds, and visitors in the floodplain are resuspended in flood water, runoff to the floodplain river, or infiltrate groundwater. Fecal indicator and MST marker data facilitate calibration. Infection risks from exposure to the pathogenic TMVs by swimming or drinking water consumption are calculated, and the required pathogen removal by treatment to meet a health-based quality target can be determined. Applicability of QMRAcatch is demonstrated by calibrating the tool for a study site at the River Danube near Vienna, Austria, using field TMV data, including a sensitivity analysis and evaluation of the model outcomes. PMID:26436266
Linking Predation Risk, Herbivore Physiological Stress and Microbial Decomposition of Plant Litter
Schmitz, Oswald J; Bradford, Mark A.; Strickland, Michael S.; Hawlena, Dror
2013-01-01
The quantity and quality of detritus entering the soil determines the rate of decomposition by microbial communities as well as recycle rates of nitrogen (N) and carbon (C) sequestration1,2. Plant litter comprises the majority of detritus3, and so it is assumed that decomposition is only marginally influenced by biomass inputs from animals such as herbivores and carnivores4,5. However, carnivores may influence microbial decomposition of plant litter via a chain of interactions in which predat...
DEFF Research Database (Denmark)
Koop, Gerrit; Collar, Carol A.; Toft, Nils;
2013-01-01
Identification of risk factors for subclinical intramammary infections (IMI) in dairy goats should contribute to improved udder health. Intramammary infection may be diagnosed by bacteriological culture or by somatic cell count (SCC) of a milk sample. Both bacteriological culture and SCC are impe...... test is a common problem in veterinary epidemiology and may lead to biased estimates of odds ratios or other measures of association in risk factor studies. The approach described herein can be used to address these problems. (C) 2012 Elsevier B.V. All rights reserved....
Energy Technology Data Exchange (ETDEWEB)
Denman, Matthew R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Groth, Katrina M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Cardoni, Jeffrey N. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wheeler, Timothy A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-04-01
Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self-correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system's design to manage the accident. Inherently and passively safe designs are laudable, but nonetheless extreme boundary conditions can interfere with the design attributes which facilitate inherent safety, thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intend of exploring two issues : (1) can human intervention either improve or worsen the potential end states and (2) can a Bayesian Network be constructed to infer the state of the reactor to inform (1). ACKNOWLEDGEMENTS The authors would like to acknowledge the U.S. Department of Energy's Office of Nuclear Energy for funding this research through Work Package SR-14SN100303 under the Advanced Reactor Concepts program. The authors also acknowledge the PRA teams at Argonne National Laboratory, Oak Ridge National Laboratory, and Idaho National Laboratory for their continue d contributions to the advanced reactor PRA mission area.
Bayesian artificial intelligence
Korb, Kevin B
2010-01-01
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente
DEFF Research Database (Denmark)
Jensen, Finn Verner; Nielsen, Thomas Dyhre
2016-01-01
Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes and...... largely due to the availability of efficient inference algorithms for answering probabilistic queries about the states of the variables in the network. Furthermore, to support the construction of Bayesian network models, learning algorithms are also available. We give an overview of the Bayesian network...
Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models
Directory of Open Access Journals (Sweden)
Raso Giovanna
2012-05-01
Full Text Available Abstract Background In Côte d’Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged Methods Using different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte d’Ivoire, focusing on children aged Plasmodium spp. infection risk for entire Côte d’Ivoire, including uncertainty. Results Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged Conclusion The malaria risk map at high spatial resolution gives an important overview of the geographical distribution of the disease in Côte d’Ivoire. It is a useful tool for the national malaria control programme and can be utilized for spatial targeting of control interventions and rational resource allocation.
Marino, Dale J.; Clewell, Harvey J.; Gentry, P. Robinan; Covington, Tammie R.; Hack, C. Eric; David, Raymond M.; Morgott, David A.
2006-01-01
KEYWORDS - CLASSIFICATION: analysis;Animals;Bayes Theorem;chemically induced;Carcinogens;Dose-Response Relationship,Drug;Environment;Inhalation Exposure;metabolism;methods;Markov Chains;mechanisms of carcinogenesis;Methylene Chloride;Mice;Models,Biological;Monte Carlo Method;Neoplasms;pharmacokinetics;Risk Assessment;Safety.
Opsteegh, Marieke; Prickaerts, Saskia; Frankena, Klaas; Evers, Eric G
2011-11-01
Toxoplasma gondii is an important foodborne pathogen, and the cause of a high disease burden due to congenital toxoplasmosis in The Netherlands. The aim of this study was to quantify the relative contribution of sheep, beef and pork products to human T. gondii infections by Quantitative Microbial Risk Assessment (QMRA). Bradyzoite concentration and portion size data were used to estimate the bradyzoite number in infected unprocessed portions for human consumption. The reduction factors for salting, freezing and heating as estimated based on published experiments in mice, were subsequently used to estimate the bradyzoite number in processed portions. A dose-response relation for T. gondii infection in mice was used to estimate the human probability of infection due to consumption of these originally infected processed portions. By multiplying these probabilities with the prevalence of T. gondii per livestock species and the number of portions consumed per year, the number of infections per year was calculated for the susceptible Dutch population and the subpopulation of susceptible pregnant women. QMRA results predict high numbers of infections per year with beef as the most important source. Although many uncertainties were present in the data and the number of congenital infections predicted by the model was almost twenty times higher than the number estimated based on the incidence in newborns, the usefulness of the advice to thoroughly heat meat is confirmed by our results. Forty percent of all predicted infections is due to the consumption of unheated meat products, and sensitivity analysis indicates that heating temperature has the strongest influence on the predicted number of infections. The results also demonstrate that, even with a low prevalence of infection in cattle, consumption of beef remains an important source of infection. Developing this QMRA model has helped identify important gaps of knowledge and resulted in the following recommendations for
Directory of Open Access Journals (Sweden)
Natasha A Loghmanpour
Full Text Available This study investigated the use of Bayesian Networks (BNs for left ventricular assist device (LVAD therapy; a treatment for end-stage heart failure that has been steadily growing in popularity over the past decade. Despite this growth, the number of LVAD implants performed annually remains a small fraction of the estimated population of patients who might benefit from this treatment. We believe that this demonstrates a need for an accurate stratification tool that can help identify LVAD candidates at the most appropriate point in the course of their disease. We derived BNs to predict mortality at five endpoints utilizing the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS database: containing over 12,000 total enrolled patients from 153 hospital sites, collected since 2006 to the present day, and consisting of approximately 230 pre-implant clinical variables. Synthetic minority oversampling technique (SMOTE was employed to address the uneven proportion of patients with negative outcomes and to improve the performance of the models. The resulting accuracy and area under the ROC curve (% for predicted mortality were 30 day: 94.9 and 92.5; 90 day: 84.2 and 73.9; 6 month: 78.2 and 70.6; 1 year: 73.1 and 70.6; and 2 years: 71.4 and 70.8. To foster the translation of these models to clinical practice, they have been incorporated into a web-based application, the Cardiac Health Risk Stratification System (CHRiSS. As clinical experience with LVAD therapy continues to grow, and additional data is collected, we aim to continually update these BN models to improve their accuracy and maintain their relevance. Ongoing work also aims to extend the BN models to predict the risk of adverse events post-LVAD implant as additional factors for consideration in decision making.
Chen, Ming-Hui; Ibrahim, Joseph G; Amy Xia, H; Liu, Thomas; Hennessey, Violeta
2014-04-30
Recently, the Center for Drug Evaluation and Research at the Food and Drug Administration released a guidance that makes recommendations about how to demonstrate that a new antidiabetic therapy to treat type 2 diabetes is not associated with an unacceptable increase in cardiovascular risk. One of the recommendations from the guidance is that phases II and III trials should be appropriately designed and conducted so that a meta-analysis can be performed. In addition, the guidance implies that a sequential meta-analysis strategy could be adopted. That is, the initial meta-analysis could aim at demonstrating the upper bound of a 95% confidence interval (CI) for the estimated hazard ratio to be drug application or a biologics license application. Subsequently after the marketing authorization, a final meta-analysis would need to show the upper bound to be drug development program for evaluating cardiovascular risk. PMID:24343859
Makkaew, P; Miller, M; Fallowfield, H J; Cromar, N J
2016-01-01
This study assessed the contamination of Escherichia coli, in lettuce grown with treated domestic wastewater in four different irrigation configurations: open spray, spray under plastic sheet cover, open drip and drip under plastic sheet cover. Samples of lettuce from each irrigation configuration and irrigating wastewater were collected during the growing season. No E. coli was detected in lettuce from drip irrigated beds. All lettuce samples from spray beds were positive for E. coli, however, no statistical difference (p > 0.05) was detected between lettuces grown in open spray or covered spray beds. The results from the field experiment were also compared to a laboratory experiment which used submersion of lettuce in wastewater of known E. coli concentration as a surrogate method to assess contamination following irrigation. The microbial quality of spray bed lettuces was not significantly different from submersed lettuce when irrigated with wastewater containing 1,299.7 E. coli MPN/100 mL (p > 0.05). This study is significant since it is the first to validate that the microbial contamination of lettuce irrigated with wastewater in the field is comparable with a laboratory technique frequently applied in the quantitative microbial risk assessment of the consumption of wastewater irrigated salad crops. PMID:27508380
Directory of Open Access Journals (Sweden)
Lucia Bonadonna
2011-06-01
Full Text Available In areas at risk (hospitals, nursing homes but in also in community such as schools, swimming pools, gyms, drinking water plumbing system can harbor also pathogenic microrganisms, potential vehicles of diseases because of structural factors and phenomena of microbial regrowth. In a setting of prevention, for limiting the exposure to pathogens in the environment and for minimizing the risk, at the point of use, specific disposable filter units could be installed. Aims. The purpose of this study was to examine the effectiveness in retaining microrganisms of a medical devices installed at drinking water faucets. Materials and methods. One hundred and eighty samples of water were microbiologically analyzed. Natural heterotrophic microbial flora present in drinking water were analytically determined before and after the passage through the system. Contemporaneously, twenty four suspensions of Pseudomonas aeruginosa and Escherichia coli at known concentration were determined for the calculation of the limit of retention calculated taking into examination the worst cases associated with the highest microbial concentrations. Results. The investigation showed that the devices were both efficient at reducing the natural microbial load and at guaranteeing the removal of high concentration of microrganisms (till 5 log. Fifty-seven out of 90 samples analyzed for the heterotrophs did not show any growth after the drainage through the system. In the positive samples, 86% showed a microbial concentration ranging between 1 e 3 CFU/L. Progressive growing concentrations of P. aeruginosa and E. coli proved a high limit of retention: none growth was obtained till the concentrations of 1.3x1012 CFU/mL and 5.0x1013 CFU/mL, respectively. Conclusions. The data show that the filter units installed directly at the point of use can be helpful at guaranteeing reliable retention of microbes. Still, in order to obtain an appropriate operation of the device, in spite of both
Gelman, Andrew; Stern, Hal S; Dunson, David B; Vehtari, Aki; Rubin, Donald B
2013-01-01
FUNDAMENTALS OF BAYESIAN INFERENCEProbability and InferenceSingle-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian ApproachesHierarchical ModelsFUNDAMENTALS OF BAYESIAN DATA ANALYSISModel Checking Evaluating, Comparing, and Expanding ModelsModeling Accounting for Data Collection Decision AnalysisADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional ApproximationsREGRESSION MODELS Introduction to Regression Models Hierarchical Linear
Yuan, Ying; MacKinnon, David P.
2009-01-01
This article proposes Bayesian analysis of mediation effects. Compared to conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian mediation analysis, inference is straightforward and exact, which makes it appealing for studies with small samples. Third, the Bayesian approach is conceptua...
Bayesian Games with Intentions
Bjorndahl, Adam; Halpern, Joseph Y.; Pass, Rafael
2016-01-01
We show that standard Bayesian games cannot represent the full spectrum of belief-dependent preferences. However, by introducing a fundamental distinction between intended and actual strategies, we remove this limitation. We define Bayesian games with intentions, generalizing both Bayesian games and psychological games, and prove that Nash equilibria in psychological games correspond to a special class of equilibria as defined in our setting.
Opsteegh, M.; Prickaerts, S.; Frankena, K.; Evers, E.G.
2011-01-01
Toxoplasma gondii is an important foodborne pathogen, and the cause of a high disease burden due to congenital toxoplasmosis in The Netherlands. The aim of this study was to quantify the relative contribution of sheep, beef and pork products to human T. gondii infections by Quantitative Microbial Ri
Directory of Open Access Journals (Sweden)
Ana Paola Balderrama-Carmona
2014-09-01
Conclusions: Soil and air inhalation and/or ingestion are important vehicles for these parasites. To our knowledge, the results obtained in the present study represent the first QMRAs for cryptosporidiosis and giardiasis due to soil and air inhalation/ingestion in Mexico. In addition, this is the first evidence of the microbial air quality around these parasites in rural zones.
Corsetti, James P; Salzman, Peter; Ryan, Dan; Moss, Arthur J; Zareba, Wojciech; Sparks, Charles E
2016-09-01
Data is presented that was utilized as the basis for Bayesian network modeling of influence pathways focusing on the central role of a polymorphism of plasminogen activator inhibitor-2 (PAI-2) on recurrent cardiovascular disease risk in patients with high levels of HDL cholesterol and C-reactive protein (CRP) as a marker of inflammation, "Influences on Plasminogen Activator Inhibitor-2 Polymorphism-Associated Recurrent Cardiovascular Disease Risk in Patients with High HDL Cholesterol and Inflammation" (Corsetti et al., 2016; [1]). The data consist of occurrence of recurrent coronary events in 166 post myocardial infarction patients along with 1. clinical data on gender, race, age, and body mass index; 2. blood level data on 17 biomarkers; and 3. genotype data on 53 presumptive CVD-related single nucleotide polymorphisms. Additionally, a flow diagram of the Bayesian modeling procedure is presented along with Bayesian network subgraphs (root nodes to outcome events) utilized as the data from which PAI-2 associated influence pathways were derived (Corsetti et al., 2016; [1]). PMID:27284570
Zhang, Juan; Fan, Shu-kai
2016-06-01
Various petroleum components possess distinctive migration and toxicity characteristics. Evaluation of contamination levels on the basis of total concentrations of petroleum hydrocarbons in soil and groundwater is limited. Hunpu, a typical wastewater-irrigated area, is located at the southwest of Shenyang City, Liaoning Province, China. In this study, various fractions, exposure pathways, and soil microbial communities were taken into account to make petroleum contamination evaluation more effective and precise in the region. The concentrations and hazard quotients of aliphatic fractions, as the bulk of an oil, verified that the groundwater must not be drunk directly. The total concentrations of aliphatic hydrocarbons (TAHs) for C10-34 were 68.90-199.87 μg g(-1) in soil in Hunpu, which required cleanup according to Oklahoma criteria. However, both health and ecological risks indicated that petroleum contamination in surface soil was not serious. Microbes may use aliphatic fractions as carbon and energy source for their growth, which was indicated by positive correlation between them. TAHsC12-16 posed highest human health risks and had the most significant effect on the soil microbial composition, although its concentration was low in both the groundwater and the soil. Straight-, branched-chain saturated, and cyclopropyl phospholipid fatty acids had more closely positive correlation with TAHsC12-16, which indicated that regulation of bacterial membrane fluidity to toxic petroleum pollutants. This study can also provide the guidelines for assessment and management of petroleum contamination. PMID:27011340
Bayesian approach to rough set
Marwala, Tshilidzi
2007-01-01
This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance criteria. The proposed method is tested to estimate the risk of HIV given demographic data. The results obtained shows that the proposed approach is able to achieve an average accuracy of 58% with the accuracy varying up to 66%. In addition the Bayesian rough set give the probabilities of the estimated HIV status as well as the linguistic rules describing how the demographic parameters drive the risk of HIV.
Energy Technology Data Exchange (ETDEWEB)
Pritchard, A.M. [Corrosion and Fouling Consultancy, Oxford (United Kingdom)
2002-07-01
Risk management is a vital function for managers in industry today and is concerned with avoiding or minimising the harm from potential accidents or incidents. The harm, which may be physical or financial, or both, can be represented as a sum of the product of hazards and the associated risks. Hazards can include substances, machines, methods of work and other aspects of work organisation. Risk expresses the likelihood that the harm from a particular hazard is realised. In overall risk assessment both the risk and the severity or impact of the harm must be assessed. Once the assessment has been made, the process of risk management is undertaken to eradicate or minimise the harm from each and all of the identified risks. The mechanism that links hazards and the harm that they cause is often clear and well-established. However, this is not the case for Microbially Influenced Corrosion (MIC), which has recently been defined by members of a network of European specialists (BRITE-EURAM Network on Microbiologically Influenced Corrosion of Industrial Materials BRRT-CT98-5084) as 'the influence of microorganisms on the kinetics of corrosion processes of metals, mineral and synthetic materials, caused by microorganisms adhering to the corroding interface (usually called 'bio-films'). A prerequisite for MIC is the presence of microorganisms. If MIC is based on their activity, (i) water, (ii) energy source, (iii) carbon source, (iv) electron donator, and (v) electron acceptor are required'. It is clearly a complex process, which is further complicated by the range of microorganisms, mainly bacteria, that are involved. The mechanisms of MIC are relatively poorly understood. The simple presence of microorganisms in a wide range of systems does not generally give rise to corrosion. Other approaches have to be investigated as a means of assessing the risk. (author)
Abia, Akebe Luther King; Ubomba-Jaswa, Eunice; Genthe, Bettina; Momba, Maggy Ndombo Benteke
2016-10-01
Although higher microbial concentrations have been reported in sediments than in the overlying water column, most quantitative microbial risk assessment (QMRA) studies have not clearly indicated the contribution of sediment-borne pathogens to estimated risks. Thus, the present study aimed at determining the public health risk associated with exposure to pathogenic bacteria in polluted river water under undisturbed conditions and conditions of sediment resuspension in the Apies River, Gauteng, South Africa. Microbial pathogens were isolated and identified using culture and molecular methods. The beta-Poisson dose-response model was used to estimate the probability of infection (Pi) with the various pathogens, following accidental/intentional ingestion of 1mL or 100mL (or 50mL) of untreated river water. Mean wet season Escherichia coli counts ranged between 5.8E+01 and 8.8E+04MPN/100mL (water column) and between 2.40E+03 and 1.28E+05MPN/100mL (sediments). Mean dry season E. coli counts ranged between 5.11E+00 and 3.40E+03MPN/100mL (water column) and between 5.09E+00 and 6.30E+03MPN/100mL (sediments). Overall (water and sediments) Vibrio cholerae was the most detected pathogen (58.8%) followed by Salmonella spp. (23.9%) and Shigella (10.1%). Ingestion of 1mL of river water could lead to 0%-4% and 1%-74% Pi with E. coli during the dry and wet season, respectively. During the dry season, the Pi with V. cholerae, Salmonella spp. and Shigella spp. were 0%-1.39%, 0%-4.11% and 0%-0.16% respectively, depending on volume of water ingested. The risks of infections with all microorganisms increased during the wet season. A 2-log increase in water E. coli count following sediments disturbance led to approximately 10 times higher Pi with E. coli than when sediments were undisturbed. Therefore, the use of the untreated water from the Apies River for drinking, household purposes or recreational activities poses a potential health risk to the users of the river. PMID:27297265
Oliver, David Michael; Porter, Kenneth D. H.; Pachepsky, Yakov A.; Muirhead, Richard W.; Reaney, Sim M.; Coffey, Rory; Kay, David; Milledge, David Graham; Hong, Eunmi; Steven G Anthony; Page, Trevor John Charles; Bloodworth, Jack W.; Mellander, Per-Erik; Carbonneau, Patrice E.; McGrane, Scott J.
2016-01-01
The application of models to predict concentrations of faecal indicator organisms (FIOs) in environmental systems plays an important role for guiding decision-making associated with the management of microbial water quality. In recent years there has been an increasing demand by policy-makers for models to help inform FIO dynamics in order to prioritise efforts for environmental and human-health protection. However, given the limited evidence-base on which FIO models are built relative to oth...
On the Relation between Robust and Bayesian Decision Making
Adam, Klaus
2003-01-01
This paper compares Bayesian decision theory with robust decision theory where the decision maker optimizes with respect to the worst state realization. For a class of robust decision problems there exists a sequence of Bayesian decision problems whose solution converges towards the robust solution. It is shown that the limiting Bayesian problem displays infinite risk aversion and that its solution is insensitive (robust) to the precise assignment of prior probabilities. Moreover, the limitin...
Bayesian analysis of rare events
Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang
2016-06-01
In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.
Oliver, David M; Porter, Kenneth D H; Pachepsky, Yakov A; Muirhead, Richard W; Reaney, Sim M; Coffey, Rory; Kay, David; Milledge, David G; Hong, Eunmi; Anthony, Steven G; Page, Trevor; Bloodworth, Jack W; Mellander, Per-Erik; Carbonneau, Patrice E; McGrane, Scott J; Quilliam, Richard S
2016-02-15
The application of models to predict concentrations of faecal indicator organisms (FIOs) in environmental systems plays an important role for guiding decision-making associated with the management of microbial water quality. In recent years there has been an increasing demand by policy-makers for models to help inform FIO dynamics in order to prioritise efforts for environmental and human-health protection. However, given the limited evidence-base on which FIO models are built relative to other agricultural pollutants (e.g. nutrients) it is imperative that the end-user expectations of FIO models are appropriately managed. In response, this commentary highlights four over-arching questions associated with: (i) model purpose; (ii) modelling approach; (iii) data availability; and (iv) model application, that must be considered as part of good practice prior to the deployment of any modelling approach to predict FIO behaviour in catchment systems. A series of short and longer-term research priorities are proposed in response to these questions in order to promote better model deployment in the field of catchment microbial dynamics. PMID:26657248
Rubin, Donald B.
1981-01-01
The Bayesian bootstrap is the Bayesian analogue of the bootstrap. Instead of simulating the sampling distribution of a statistic estimating a parameter, the Bayesian bootstrap simulates the posterior distribution of the parameter; operationally and inferentially the methods are quite similar. Because both methods of drawing inferences are based on somewhat peculiar model assumptions and the resulting inferences are generally sensitive to these assumptions, neither method should be applied wit...
Bayesian statistics an introduction
Lee, Peter M
2012-01-01
Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel
Understanding Computational Bayesian Statistics
Bolstad, William M
2011-01-01
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistic
Institute of Scientific and Technical Information of China (English)
谢洪涛
2012-01-01
This paper is aimed at converting fault trees to fault Bayesian networks, and solving the main causes and probability of slope collapse accidents. For slope excavation and support system in which the causal relationship is uncertain, description with probability is more reasonable. The fault tree logic gates do not have this capability because il is described in the logic of certainty. Bayesian network is regarded as uncertain knowledge representation and reasoning of the most effective theoretical model. In this paper, referring to the qualitative analysis of slope collapse accident with Fault Tree Analysis (FTA) done by previous researchers, a Bayesian network conversion from FTA is carried out. Based on the fault tree of slope collapse, a corresponding fault Bayesian network model of the slope collapse is established. To establish Bayesian network, the causal relationship between all variables are analyzed based on their prior probability. When new evidence becomes available, the posterior probabilities of a set of variables of structure condition can be updated, which has practical value for evaluation of the structure. Applying the fault Bayesian network model, the probability of risk event of slope collapse are calculated. The basic events are rearranged based on the importance according to the importance analysis to find out the most influential potential factor for the occurrence of slope collapse accident. The result shows that the fault Bayesian network based method could obtain more additional information. Furthermore, the network can help to make account of the changing conditions of nodes induced by the variation of any other nodes of networks, which the fault tree approach failed to do so. Therefore, it can be said that the Bayesian network approach can be taken as a good substitute for fault tree approach for hazard assessment with promising perspective of application.%针对故障树分析方法在风险评估中的局限性,研究了故障贝叶
DEFF Research Database (Denmark)
Andersen, Signe Tanja; Erichsen, A. C.; Mark, O.;
2013-01-01
Quantitative microbial risk assessments (QMRAs) often lack data on water quality leading to great uncertainty in the QMRA because of the many assumptions. The quantity of waste water contamination was estimated and included in a QMRA on an extreme rain event leading to combined sewer overflow (CS...
Keswani, Anisha; Oliver, David M; Gutierrez, Tony; Quilliam, Richard S
2016-07-01
Marine plastic debris is well characterized in terms of its ability to negatively impact terrestrial and marine environments, endanger coastal wildlife, and interfere with navigation, tourism and commercial fisheries. However, the impacts of potentially harmful microorganisms and pathogens colonising plastic litter are not well understood. The hard surface of plastics provides an ideal environment for opportunistic microbial colonisers to form biofilms and might offer a protective niche capable of supporting a diversity of different microorganisms, known as the "Plastisphere". This biotope could act as an important vector for the persistence and spread of pathogens, faecal indicator organisms (FIOs) and harmful algal bloom species (HABs) across beach and bathing environments. This review will focus on the existent knowledge and research gaps, and identify the possible consequences of plastic-associated microbes on human health, the spread of infectious diseases and bathing water quality. PMID:27128352
Microbial colonisation of orthopaedic tourniquets: A potential risk for surgical site infection
Directory of Open Access Journals (Sweden)
S K Sahu
2015-01-01
Full Text Available Pneumatic tourniquets have been used in orthopaedic surgery to get avascular fields. Sixteen such tourniquets were analysed for microbial colonisation. Samples were taken from two inner and two outer areas of each tourniquet and cultured on sheep blood agar. Eight of these were wiped with Savlon and the rest with Sterillium solution. Post-treatment samples from the same sites were again cultured. After incubation, colonies from each site were identified and counted. It was observed that the tourniquets were colonised with coagulase-negative staphylococci, Staphylococcus aureus, Bacillus, diphtheroids, Pseudomonas, Acinetobacter, enterococci, enterobacteria, and Candida. On treating with Savlon and Sterillium, there was 92.18% and 95.70% reduction in the colony count, respectively.
Kundu, Arti; McBride, Graham; Wuertz, Stefan
2013-10-15
We used site-specific quantitative microbial risk assessment (QMRA) to assess the probability of adenovirus illness for three groups of swimmers: adults with primary contact, children with primary contact, and secondary contact regardless of age. Human enteroviruses and adenoviruses were monitored by qPCR in a multi-use watershed and Adenovirus type 40/41 was detected in 11% of 73 samples, ranging from 147 to 4117 genomes per liter. Enterovirus was detected only once (32 genomes per liter). Seven of eight virus detections occurred when E. coli concentrations were below the single sample maximum water quality criterion for contact recreation, and five of eight virus detections occurred when fecal coliforms were below the corresponding criterion. We employed dose-harmonization to convert viral genome measurements to TCID50 values needed for dose-response curves. The three scenarios considered different amounts of water ingestion and Monte Carlo simulation was used to account for the variability associated with the doses. The mean illness risk in children based on adenovirus measurements obtained over 11 months was estimated to be 3.5%, which is below the 3.6% risk considered tolerable by the current United States EPA recreational criteria for gastrointestinal illnesses (GI). The mean risks of GI illness for adults and secondary contact were 1.9% and 1.0%, respectively. These risks changed appreciably when different distributions were fitted to the data as determined by Monte Carlo simulations. In general, risk was at a maximum for the log-logistic distribution and lowest for the hockey stick distribution in all three selected scenarios. Also, under default assumptions, the risk was lowered considerably when assuming that only a small proportion of Adenovirus 40/41 (3%) was as infectious as Adenovirus type 4, compared to the assumption that all genomes were Adenovirus 4. In conclusion, site-specific QMRA on water-borne adenoviruses in this watershed provided a similar
LENUS (Irish Health Repository)
Saeed, Ayman
2012-02-01
AIM: To identify the risk factors for, and to report the microbiological findings and clinical outcomes of, severe microbial keratitis (MK). METHODS: This was a retrospective study of all cases of presumed MK admitted to a tertiary referral center over a 2-year period (September 2001 to August 2003). Data recorded included demographic data, details relating to possible risk factors, results of microbiological studies, clinical findings at presentation, and clinical and visual outcomes. RESULTS: Ninety patients were admitted with a diagnosis of presumed MK during the study period. The mean age of patients was 45 +\\/- 32 years, and the male to female ratio was 47:43 (52.2%:47.7%). Predisposing risk factors for MK included contact lens wear (37; 41.1%), anterior segment disease (19; 21.1%), ocular trauma (13; 14.4%), systemic disease (5; 5.6%), and previous ocular surgery (1; 1.1%). Cultured organisms included gram-negative bacteria (17; 51.5%), gram-positive bacteria (11, 33.3%), acanthamoeba (2; 6.1%), and fungi (1; 3%). Visual acuity improved significantly after treatment [mean best-corrected visual acuity (+\\/-standard deviation) at presentation: 0.76 (+\\/-0.11); mean best-corrected visual acuity at last follow-up: 0.24 (+\\/-0.07); P < 0.001]. Secondary surgical procedures were required in 18 (20%) cases, and these included punctal cautery (1; 1.1%), tissue glue repair of corneal perforation (2; 2.2%), tarsorrhaphy (9; 9.9%), Botulinum toxin-induced ptosis (1; 1.1%), penetrating keratoplasty (3; 3.3%), and evisceration (2; 2.2%). CONCLUSIONS: Contact lens wear remains a significant risk factor for severe MK. MK remains a threat to vision and to the eye, but the majority of cases respond to prompt and appropriate antimicrobial therapy.
Frühwirth-Schnatter, Sylvia
1990-01-01
In the paper at hand we apply it to Bayesian statistics to obtain "Fuzzy Bayesian Inference". In the subsequent sections we will discuss a fuzzy valued likelihood function, Bayes' theorem for both fuzzy data and fuzzy priors, a fuzzy Bayes' estimator, fuzzy predictive densities and distributions, and fuzzy H.P.D .-Regions. (author's abstract)
Yuan, Ying; MacKinnon, David P.
2009-01-01
In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…
Delhalle, L; Ellouze, M; Yde, M; Clinquart, A; Daube, G; Korsak, N
2012-12-01
In 2005, the Belgian authorities reported a Listeria monocytogenes contamination episode in cheese made from raw goat's milk. The presence of an asymptomatic shedder goat in the herd caused this contamination. On the basis of data collected at the time of the episode, a retrospective study was performed using an exposure assessment model covering the production chain from the milking of goats up to delivery of cheese to the market. Predictive microbiology models were used to simulate the growth of L. monocytogenes during the cheese process in relation with temperature, pH, and water activity. The model showed significant growth of L. monocytogenes during chilling and storage of the milk collected the day before the cheese production (median increase of 2.2 log CFU/ml) and during the addition of starter and rennet to milk (median increase of 1.2 log CFU/ml). The L. monocytogenes concentration in the fresh unripened cheese was estimated to be 3.8 log CFU/g (median). This result is consistent with the number of L. monocytogenes in the fresh cheese (3.6 log CFU/g) reported during the cheese contamination episode. A variance-based method sensitivity analysis identified the most important factors impacting the cheese contamination, and a scenario analysis then evaluated several options for risk mitigation. Thus, by using quantitative microbial risk assessment tools, this study provides reliable information to identify and control critical steps in a local production chain of cheese made from raw goat's milk. PMID:23212008
[Air Microbial Pollution and Health Risk of Urban Black Odorous Water].
Liu, Jian-fu; Chen, Jing-xiong; Gu, Shi-you
2016-04-15
Aiming at the possihle air microhial pollution of urhan hlack odorous water the contamination characteristics of hacteria, fungi and total microhe as well as health risks of different types of population within certain distance from the urhan hlack odorous water were studied. The results showed that hacteria and fungi pollution was primary within offshore 200 m; under near calm condition, there was an aggregation phenomenon of microorganisms within offshore 20 m; the concentrations of hacteria, fungi and total microhe were the highest in the morning, the middle at noon, and the lowest in the afternoon; within offshore 200 m, the width of hlack odorous water was significantly correlated with the concentrations of hacteria, fungi and total microorganisms; the microhial health risk of residents mainly existed in the offshore 100 m range; at the same offshore distance, the short-term exposure health risk to children was the greatest, followed hy women, men to a minimum. PMID:27548945
Mark H. Huff; Turley, Marianne C.; Randy Molina; Russ Holmes; Steve Morey; Hohenlohe, Paul A.; Bruce G. Marcot; John A. Laurence
2006-01-01
We developed a set of decision-aiding models as Bayesian belief networks (BBNs) that represented a complex set of evaluation guidelines used to determine the appropriate conservation of hundreds of potentially rare species on federally-administered lands in the Pacific Northwest United States. The models were used in a structured assessment and paneling procedure as part of an adaptive management process that evaluated new scientific information under the Northwest Forest Plan. The models wer...
Using an Integrated, Multi-disciplinary Framework to Support Quantitative Microbial Risk Assessments
The Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES) provides the infrastructure to link disparate models and databases seamlessly, giving an assessor the ability to construct an appropriate conceptual site model from a host of modeling choices, so a numbe...
DEVELOPMENT OF MICROBIAL METAGENOMIC MARKERS FOR ENVIRONMENTAL MONITORING AND RISK ASSESSMENT
The microbiological water quality standards established by EPA depend on culturing fecal indicator bacteria to predict the risks associated with water usage. For decades this has been the favored approach to microbiological monitoring in spite of the fact that culture-based meth...
Bayesian Approach to Neuro-Rough Models for Modelling HIV
Marwala, Tshilidzi
2007-01-01
This paper proposes a new neuro-rough model for modelling the risk of HIV from demographic data. The model is formulated using Bayesian framework and trained using Markov Chain Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62% as opposed to 58% obtained from a Bayesian formulated rough set model trained using Markov chain Monte Carlo method and 62% obtained from a Bayesian formulated multi-layered perceptron (MLP) model trained using hybrid Monte. The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model.
Before new, rapid quantitative PCR (qPCR) methods for recreational water quality assessment and microbial source tracking (MST) can be useful in a regulatory context, an understanding of the ability of the method to detect a DNA target (marker) when the contaminant soure has been...
Canfora, Loredana; Sbrana, Cristiana; Avio, Luciano; Felici, Barbara; Scatà, Maria Carmela; Neri, Ulderico; Benedetti, Anna
2014-09-15
The cultivation of GMPs in Europe raises many questions about the environmental risks, in particular about their ecological impact on non-target organisms and on soil properties. The aim of a multidisciplinary group engaged in a LIFE+project (MAN-GMP-ITA) was to validate and improve an existing environmental risk assessment (ERA) methodology on GMPs within the European legislative framework on GMOs. Given the impossibility of evaluating GMO impact directly, as GMPs are banned in Italy, GMPs have not been used at any stage of the project. The project thus specifically focused on the conditions for the implementation of ERA in different areas of Italy, with an emphasis on some sensitive and protected areas located in the North, Centre, and South of the country, in order to lay the necessary baseline for evaluating the possible effects of a GMP on soil communities. Our sub-group carried out soil analyses in order to obtain soil health and fertility indicators to be used as baselines in the ERA model. Using various methods of chemical, biochemical, functional and genetic analysis, our study assessed the changes in diversity and functionality of bacterial populations, and arbuscular mycorrhizal fungi. The results show that plant identity and growth, soil characteristics, and field site climatic parameters are key factors in contributing to variation in microbial community structure and diversity, thus validating our methodological approach. Our project has come to the conclusion that the uneven composition and biological-agronomical quality of soils need to be taken into consideration in a risk analysis within the framework of ERA for the release of genetically modified plants. PMID:25014185
一种基于贝叶斯网络的软件项目风险管理方法%A Method of Software Project Risk Management Based on Bayesian Networks
Institute of Scientific and Technical Information of China (English)
李天纵; 王强
2011-01-01
提出一种新的软件项目风险管理方法,采用贝叶斯网络同时对风险发生概率和风险影响进行推理.该风险管理方法能够随着软件项目的进行持续地评估潜在风险,并采取适当的措施应对风险.实践证明,在软件开发过程中引入该风险管理方法能够有效地对风险进行管理,提高软件开发的成功率.%A new method of software project risk mangement is presented, which uses the Bayesian networks to infer the probability of risk occurrence and the impact of risk simultaneously. The method can assess risks continuously and take proper measures to respond to risks during software project. Practice proves that by introducing this method into software development process, project risks will be well-managed and project success rate will be improved.
Enoch C. T. So; Faraday H. F. Fung; Joshua K. H. Yeung; Lilian H. Y. Chow; Julio S. H. Kwok; Ruby L. Y. Lam; Tommy C. Y. So; Faye S. M. Yu; Dana Vackova; Gilberto K. K. Leung
2013-01-01
Background: The white coat is traditionally considered to be the appropriate attire for physicians but it may also be contaminated with microbes and act as a potential source of infection. We aimed to study patients’ acceptance of physicians’ attire, their underlying reasons, and their reactions to an educational intervention with regards to the risks of contamination. Methods: We conducted a voluntary questionnaire survey at a university teaching hospital in Hong Kong from February to July 2...
Crotta, Matteo; Paterlini, Franco; Rizzi, Rita; Guitian, Javier
2016-02-01
Foodborne disease as a result of raw milk consumption is an increasing concern in Western countries. Quantitative microbial risk assessment models have been used to estimate the risk of illness due to different pathogens in raw milk. In these models, the duration and temperature of storage before consumption have a critical influence in the final outcome of the simulations and are usually described and modeled as independent distributions in the consumer phase module. We hypothesize that this assumption can result in the computation, during simulations, of extreme scenarios that ultimately lead to an overestimation of the risk. In this study, a sensorial analysis was conducted to replicate consumers' behavior. The results of the analysis were used to establish, by means of a logistic model, the relationship between time-temperature combinations and the probability that a serving of raw milk is actually consumed. To assess our hypothesis, 2 recently published quantitative microbial risk assessment models quantifying the risks of listeriosis and salmonellosis related to the consumption of raw milk were implemented. First, the default settings described in the publications were kept; second, the likelihood of consumption as a function of the length and temperature of storage was included. When results were compared, the density of computed extreme scenarios decreased significantly in the modified model; consequently, the probability of illness and the expected number of cases per year also decreased. Reductions of 11.6 and 12.7% in the proportion of computed scenarios in which a contaminated milk serving was consumed were observed for the first and the second study, respectively. Our results confirm that overlooking the time-temperature dependency may yield to an important overestimation of the risk. Furthermore, we provide estimates of this dependency that could easily be implemented in future quantitative microbial risk assessment models of raw milk pathogens. PMID
Fai, Patricia Bi Asanga; Mbida, Mpoame; Demefack, Jean Marc; Yamssi, Cedric
2015-11-01
Many microbiotests that have been proposed for use in the risk assessment of environmental pollutants have the drawback of lacking relevant published data on various aspects of their test application possibilities and therefore do not receive the regulatory recognition which they may deserve. The MARA bioassay lacks published data for many relevant environmental pollutants, particularly pesticides and this may limit its use in regulatory framework. The present study has assessed the sensitivity of the MARA bioassay relative to other established bioassays (Daphnia magna and Oreochromis niloticus) to two widely used herbicide formulations: Roundup (having glyphosate as active ingredient) and Herbextra (with the active ingredient being 2,4-dichlorophenoxyacetic acid-2,4-D). Roundup was found to be more toxic than Herbextra in all three bioassays. The D. magna EC50 s obtained for Roundup and Herbextra were respectively 5.55 and 356.61 mg/l while the LC50 s for O. niloticus were 11.30 and 222,28 mg/l respectively. In the case of the MARA bioassay microbial toxic concentrations (MTCs) for individual species ranged from 6.85 to 468 mg/l with an overall mean MTC of 101.82 mg/l for glyphosate and from 74.67 to 13,333 mg/l for 2,4-D giving an overall mean MTC of 2855.88 mg/l. Although the overall MTCs from the MARA bioassay were much higher than the LC50 s and EC50 s from the fish and daphnia bioassays respectively, the most sensitive MARA organism for each of the herbicides had MTCs that were comparable to or lower than the corresponding endpoints from the other bioassays implying that the MARA assay is a potentially useful bioassay for risk assessment of pesticides. PMID:26362569
Granade, Christopher; Cory, D G
2015-01-01
In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of- the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a lack of informative prior distributions, and an inability to track time-dependent processes. Here, we solve all three problems. First, we use modern statistical methods, as pioneered by Husz\\'ar and Houlsby and by Ferrie, to make Bayesian tomography numerically tractable. Our approach allows for practical computation of Bayesian point and region estimators for quantum states and channels. Second, we propose the first informative priors on quantum states and channels. Finally, we develop a method that allows online tracking of time-dependent states and estimates the drift and diffusion processes affecting a state. We provide source code and animated visual examples for our methods.
基于贝叶斯网络的供应链风险模糊综合评判%Fuzzy Comprehensive Evaluation of Supply Chain Risks Based on Bayesian Networks
Institute of Scientific and Technical Information of China (English)
吴天魁; 王波; 顾基发; 周晓辉
2014-01-01
The existing common methods of analyzing risks cannot evaluate the supply chain risks for sure,because the risks leading to the failure of supply chain are fuzzy and complex.This article puts forward a fuzzy comprehensive evaluation of supply chain risks based on Bayesian networks.Taking the supply chain risks of an enterprise as an example,we calculated the logarithmic probability P of risky affairs through linear deduction of Bayesian networks and then worked out the main risky af-fairs and their ranks by the way of fuzzy comprehensive evaluation.This research provides a reference for evaluating supply chain risks and other parallel ones,which makes it convenient for enterprises to take provisions to lower risks and reduce los-ses.%由于导致供应链失效的风险因素具有模糊性、复杂性，增加了供应链风险分析难度，一般的风险分析方法不能很好地评判供应链风险。文章提出了基于贝叶斯网络的供应链风险模糊综合评判方法，以某企业供应链风险为例，通过构建供应链失效风险的贝叶斯网络对风险事件发生概率进行线性推理，再采用模糊综合评判方法求出供应链风险中主要风险事件及总风险事件的风险等级，为该企业及早调控供应链提供依据。本评判方法可为企业供应链风险及其他类似风险的预防和控制提供参考依据。
Bayesian exploratory factor analysis
Gabriella Conti; Sylvia Frühwirth-Schnatter; James Heckman; Rémi Piatek
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identifi cation criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study c...
Bayesian Exploratory Factor Analysis
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study co...
Bayesian Exploratory Factor Analysis
Gabriella Conti; Sylvia Fruehwirth-Schnatter; Heckman, James J.; Remi Piatek
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on \\emph{ad hoc} classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo s...
Bayesian exploratory factor analysis
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo st...
Bayesian exploratory factor analysis
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James; Piatek, Rémi
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study co...
Carbonetto, Peter; Kisynski, Jacek; De Freitas, Nando; Poole, David L
2012-01-01
The Bayesian Logic (BLOG) language was recently developed for defining first-order probability models over worlds with unknown numbers of objects. It handles important problems in AI, including data association and population estimation. This paper extends BLOG by adopting generative processes over function spaces - known as nonparametrics in the Bayesian literature. We introduce syntax for reasoning about arbitrary collections of objects, and their properties, in an intuitive manner. By expl...
Learning Bayesian Networks from Correlated Data
Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola
2016-05-01
Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.
Landuyt, Dries; Lemmens, Pieter; D'hondt, Rob; Broekx, Steven; Liekens, Inge; De Bie, Tom; Declerck, Steven A J; De Meester, Luc; Goethals, Peter L M
2014-12-01
Freshwater ponds deliver a broad range of ecosystem services (ESS). Taking into account this broad range of services to attain cost-effective ESS delivery is an important challenge facing integrated pond management. To assess the strengths and weaknesses of an ESS approach to support decisions in integrated pond management, we applied it on a small case study in Flanders, Belgium. A Bayesian belief network model was developed to assess ESS delivery under three alternative pond management scenarios: intensive fish farming (IFF), extensive fish farming (EFF) and nature conservation management (NCM). A probabilistic cost-benefit analysis was performed that includes both costs associated with pond management practices and benefits associated with ESS delivery. Whether or not a particular ESS is included in the analysis affects the identification of the most preferable management scenario by the model. Assessing the delivery of a more complete set of ecosystem services tends to shift the results away from intensive management to more biodiversity-oriented management scenarios. The proposed methodology illustrates the potential of Bayesian belief networks. BBNs facilitate knowledge integration and their modular nature encourages future model expansion to more encompassing sets of services. Yet, we also illustrate the key weaknesses of such exercises, being that the choice whether or not to include a particular ecosystem service may determine the suggested optimal management practice. PMID:25005053
Directory of Open Access Journals (Sweden)
Mark H. Huff
2006-12-01
Full Text Available We developed a set of decision-aiding models as Bayesian belief networks (BBNs that represented a complex set of evaluation guidelines used to determine the appropriate conservation of hundreds of potentially rare species on federally-administered lands in the Pacific Northwest United States. The models were used in a structured assessment and paneling procedure as part of an adaptive management process that evaluated new scientific information under the Northwest Forest Plan. The models were not prescriptive but helped resource managers and specialists to evaluate complicated and at times conflicting conservation guidelines and to reduce bias and uncertainty in evaluating the scientific data. We concluded that applying the BBN modeling framework to complex and equivocal evaluation guidelines provided a set of clear, intuitive decision-aiding tools that greatly aided the species evaluation and conservation process.
International Nuclear Information System (INIS)
The optimization of preventive maintenance of aging materials can be well modelled through the application of the two-parameters Weibull law. Unfortunately there is no theoretical model describing the aging of components implied in emergency interventions which may fail in very moment of demand. These components are relevant to the safety of Nuclear Power Plants and the main problem with them is the optimization of the maintenance and the test schedule. Indeed the effect of any test is twofold, namely: the test reveals failures which otherwise would occur when the Power Plant would need urgent intervention but at the same time it causes the aging process to step up. The aging process is suspected to affect certain safeguard equipment, but it does not exist a theoretical model of component aging in function of demands. Sections 2.1 and 2.2 of the paper are devoted to the description of a Bayesian model of discrete aging, deriving the likelihood function of the model (data modelling) and to selecting 'the best prior' (Analyst knowledge) that can be associated to the likelihood, respectively. The aspects of numerical integration relative to assessing the predictive reliability of the aging components are discussed in Section 2.3. Section 3 reports a Reliability Centered Maintenance application of the model to cylinders of emergency electrical generators of the NPP nuclear sectors. These components are subjected to an annual endoscopic checking and to a systematical replacement at every 5 years. The statistical Bayesian decision theory was applied to determine an optimal period of cylinder replacement based on the aging model presented in this paper. It is shown that the optimal value is between 10 to 12 years. (author)
Bayesian least squares deconvolution
Asensio Ramos, A.; Petit, P.
2015-11-01
Aims: We develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods: We consider LSD under the Bayesian framework and we introduce a flexible Gaussian process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results: We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
Bayesian least squares deconvolution
Ramos, A Asensio
2015-01-01
Aims. To develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods. We consider LSD under the Bayesian framework and we introduce a flexible Gaussian Process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results. We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
Loredo, T J
2004-01-01
I describe a framework for adaptive scientific exploration based on iterating an Observation--Inference--Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian methodology for the inference and design stages: Bayesian inference to quantify what we have learned from the available data and predict future data, and Bayesian decision theory to identify which new observations would teach us the most. When the goal of the experiment is simply to make inferences, the framework identifies a computationally efficient iterative ``maximum entropy sampling'' strategy as the optimal strategy in settings where the noise statistics are independent of signal properties. Results of applying the method to two ``toy'' problems with simulated data--measuring the orbit of an extrasolar planet, and locating a hidden one-dimensional object--show the approach can significantly improve observational eff...
Bayesian and frequentist inequality tests
David M. Kaplan; Zhuo, Longhao
2016-01-01
Bayesian and frequentist criteria are fundamentally different, but often posterior and sampling distributions are asymptotically equivalent (and normal). We compare Bayesian and frequentist hypothesis tests of inequality restrictions in such cases. For finite-dimensional parameters, if the null hypothesis is that the parameter vector lies in a certain half-space, then the Bayesian test has (frequentist) size $\\alpha$; if the null hypothesis is any other convex subspace, then the Bayesian test...
Bayesian multiple target tracking
Streit, Roy L
2013-01-01
This second edition has undergone substantial revision from the 1999 first edition, recognizing that a lot has changed in the multiple target tracking field. One of the most dramatic changes is in the widespread use of particle filters to implement nonlinear, non-Gaussian Bayesian trackers. This book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that implements
Bayesian Exploratory Factor Analysis
DEFF Research Database (Denmark)
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.;
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the...... corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates...
Bayesian Geostatistical Design
DEFF Research Database (Denmark)
Diggle, Peter; Lophaven, Søren Nymand
2006-01-01
locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model...
Czech Academy of Sciences Publication Activity Database
Krejsa, Jiří; Věchet, S.
Bratislava: Slovak University of Technology in Bratislava, 2010, s. 217-222. ISBN 978-80-227-3353-3. [Robotics in Education . Bratislava (SK), 16.09.2010-17.09.2010] Institutional research plan: CEZ:AV0Z20760514 Keywords : mobile robot localization * bearing only beacons * Bayesian filters Subject RIV: JD - Computer Applications, Robotics
DEFF Research Database (Denmark)
Antoniou, Constantinos; Harrison, Glenn W.; Lau, Morten I.;
2015-01-01
A large literature suggests that many individuals do not apply Bayes’ Rule when making decisions that depend on them correctly pooling prior information and sample data. We replicate and extend a classic experimental study of Bayesian updating from psychology, employing the methods of experimenta...
Bayesian Independent Component Analysis
DEFF Research Database (Denmark)
Winther, Ole; Petersen, Kaare Brandt
2007-01-01
In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...
Noncausal Bayesian Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution as a...
Loredo, Thomas J.
2004-04-01
I describe a framework for adaptive scientific exploration based on iterating an Observation-Inference-Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian methodology for the inference and design stages: Bayesian inference to quantify what we have learned from the available data and predict future data, and Bayesian decision theory to identify which new observations would teach us the most. When the goal of the experiment is simply to make inferences, the framework identifies a computationally efficient iterative ``maximum entropy sampling'' strategy as the optimal strategy in settings where the noise statistics are independent of signal properties. Results of applying the method to two ``toy'' problems with simulated data-measuring the orbit of an extrasolar planet, and locating a hidden one-dimensional object-show the approach can significantly improve observational efficiency in settings that have well-defined nonlinear models. I conclude with a list of open issues that must be addressed to make Bayesian adaptive exploration a practical and reliable tool for optimizing scientific exploration.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
A Bayesian Modelling of Wildfires in Portugal
Silva, Giovani L.; Soares, Paulo; Marques, Susete; Dias, Inês M.; Oliveira, Manuela M.; Borges, Guilherme J.
2015-01-01
In the last decade wildfires became a serious problem in Portugal due to different issues such as climatic characteristics and nature of Portuguese forest. In order to analyse wildfire data, we employ beta regression for modelling the proportion of burned forest area, under a Bayesian perspective. Our main goal is to find out fire risk factors that influence the proportion of area burned and what may make a forest type susceptible or resistant to fire. Then, we analyse wildfire...
Directory of Open Access Journals (Sweden)
Chatterjee B
2009-01-01
Full Text Available Background: The failure of empirical therapy is frequently observed, even in community-acquired urinary tract infections. We, therefore, conducted a prospective, clinic-based study in 2004-2005 to document anti-microbial resistance rates and correlate them with possible risk factors to assist empirical decision-making. Materials and Methods: Symptomatic patients with pyuria underwent urine culture. Isolates were identified using standard methods and anti-microbial resistance was determined by disk-diffusion. Ultrasonography was used to detect complicating factors. Patients were stratified by the presence of complicating factors and history of invasive procedures for comparison of resistance rates. Statistical Method Used: Chi-square or Fisher exact tests, as appropriate. Results: There were 156 E. coli isolates, of which 105 were community-acquired. Twenty-three community-acquired isolates were from patients with complicating factors while 82 were from patients without any. Fifty-one isolates were from patients who had recently undergone invasive procedures on the urinary tract. Thirty-two community-acquired isolates from reproductive-age women without apparent complicating factors had resistance rates of 50% or above against tetracyclines, Co-trimoxazole, aminopenicillins, Nalidixic acid, Ciprofloxacin and 1 st generation cephalosporins. Resistance rates were significantly higher among isolates from patients subjected to invasive procedures, except against Co-trimoxazole, tetracyclines and Amikacin. Conclusion: High rates of anti-microbial resistance in community-acquired uropathogens have made antimicrobial sensitivity testing necessary even in a rural, primary-care setting.
Bayesian network learning for natural hazard assessments
Vogel, Kristin
2016-04-01
Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables
Bayesian classification and regression trees for predicting incidence of cryptosporidiosis.
Directory of Open Access Journals (Sweden)
Wenbiao Hu
Full Text Available BACKGROUND: Classification and regression tree (CART models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and applied to the problem of modelling the cryptosporidiosis infection in Queensland, Australia. METHODOLOGY/PRINCIPAL FINDINGS: We compared the results of a Bayesian CART model with those obtained using a Bayesian spatial conditional autoregressive (CAR model. Overall, the analyses indicated that the nature and magnitude of the effect estimates were similar for the two methods in this study, but the CART model more easily accommodated higher order interaction effects. CONCLUSIONS/SIGNIFICANCE: A Bayesian CART model for identification and estimation of the spatial distribution of disease risk is useful in monitoring and assessment of infectious diseases prevention and control.
Probability and Bayesian statistics
1987-01-01
This book contains selected and refereed contributions to the "Inter national Symposium on Probability and Bayesian Statistics" which was orga nized to celebrate the 80th birthday of Professor Bruno de Finetti at his birthplace Innsbruck in Austria. Since Professor de Finetti died in 1985 the symposium was dedicated to the memory of Bruno de Finetti and took place at Igls near Innsbruck from 23 to 26 September 1986. Some of the pa pers are published especially by the relationship to Bruno de Finetti's scientific work. The evolution of stochastics shows growing importance of probability as coherent assessment of numerical values as degrees of believe in certain events. This is the basis for Bayesian inference in the sense of modern statistics. The contributions in this volume cover a broad spectrum ranging from foundations of probability across psychological aspects of formulating sub jective probability statements, abstract measure theoretical considerations, contributions to theoretical statistics an...
Bayesian Magic in Asteroseismology
Kallinger, T.
2015-09-01
Only a few years ago asteroseismic observations were so rare that scientists had plenty of time to work on individual data sets. They could tune their algorithms in any possible way to squeeze out the last bit of information. Nowadays this is impossible. With missions like MOST, CoRoT, and Kepler we basically drown in new data every day. To handle this in a sufficient way statistical methods become more and more important. This is why Bayesian techniques started their triumph march across asteroseismology. I will go with you on a journey through Bayesian Magic Land, that brings us to the sea of granulation background, the forest of peakbagging, and the stony alley of model comparison.
Bayesian Nonparametric Graph Clustering
Banerjee, Sayantan; Akbani, Rehan; Baladandayuthapani, Veerabhadran
2015-01-01
We present clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables. As opposed to standard approaches that assume known graph structures, we first estimate the edge structure of the unknown graph using Bayesian neighborhood selection approaches, wherein we account for the uncertainty of graphical structure learning through model-averaged estimates of the suitable parameters. Subsequently, we develop a nonparametric graph cluster...
Approximate Bayesian recursive estimation
Czech Academy of Sciences Publication Activity Database
Kárný, Miroslav
2014-01-01
Roč. 285, č. 1 (2014), s. 100-111. ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Approximate parameter estimation * Bayesian recursive estimation * Kullback–Leibler divergence * Forgetting Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.038, year: 2014 http://library.utia.cas.cz/separaty/2014/AS/karny-0425539.pdf
Bayesian Generalized Rating Curves
Helgi Sigurðarson 1985
2014-01-01
A rating curve is a curve or a model that describes the relationship between water elevation, or stage, and discharge in an observation site in a river. The rating curve is fit from paired observations of stage and discharge. The rating curve then predicts discharge given observations of stage and this methodology is applied as stage is substantially easier to directly observe than discharge. In this thesis a statistical rating curve model is proposed working within the framework of Bayesian...
Heteroscedastic Treed Bayesian Optimisation
Assael, John-Alexander M.; Wang, Ziyu; Shahriari, Bobak; De Freitas, Nando
2014-01-01
Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global optimisation of black-box functions which are expensive to evaluate. At the core of this approach is a Gaussian process prior that captures our belief about the distribution over functions. However, in many cases a single Gaussian process is not flexible enough to capture non-stat...
Efficient Bayesian Phase Estimation
Wiebe, Nathan; Granade, Chris
2016-07-01
We introduce a new method called rejection filtering that we use to perform adaptive Bayesian phase estimation. Our approach has several advantages: it is classically efficient, easy to implement, achieves Heisenberg limited scaling, resists depolarizing noise, tracks time-dependent eigenstates, recovers from failures, and can be run on a field programmable gate array. It also outperforms existing iterative phase estimation algorithms such as Kitaev's method.
Brody, Samuel; Lapata, Mirella
2009-01-01
Sense induction seeks to automatically identify word senses directly from a corpus. A key assumption underlying previous work is that the context surrounding an ambiguous word is indicative of its meaning. Sense induction is thus typically viewed as an unsupervised clustering problem where the aim is to partition a word’s contexts into different classes, each representing a word sense. Our work places sense induction in a Bayesian context by modeling the contexts of the ambiguous word as samp...
Bayesian Neural Word Embedding
Barkan, Oren
2016-01-01
Recently, several works in the domain of natural language processing presented successful methods for word embedding. Among them, the Skip-gram (SG) with negative sampling, known also as Word2Vec, advanced the state-of-the-art of various linguistics tasks. In this paper, we propose a scalable Bayesian neural word embedding algorithm that can be beneficial to general item similarity tasks as well. The algorithm relies on a Variational Bayes solution for the SG objective and a detailed step by ...
International Nuclear Information System (INIS)
This study is a contribution towards a risk assessment of the São Domingos Mine area (Portugal), integrating information from: soil physicochemical characteristics, pseudo-total and bioavailable trace elements (As, Cd, Cr, Cu, Ni, Pb and Zn), ecotoxicological evaluation, and microbial indicators. The bioassays using soil eluates (seed germination, luminescent inhibition of Vibrio fischeri and Daphnia magna immobilization) confirmed the soil toxicity categorization obtained with the bioassays using soil (plant growth tests, Eisenia fetida mortality and avoidance behaviour). However, the soil identified as the most toxic using bioassays, was different from the expected when considering the results from pseudo-total and effective bioavailable trace elements. Taking in consideration the observations, it is highly recommended to complement the results from environmental chemistry with results from bioassays, in order to provide a more complete and relevant information on the bioavailability of contaminants and to characterize the risk of contaminated soils. - Highlights: ► Impaired soil retention and habitat functions for all tested soils. ► Aquatic and terrestrial bioassays agreed in the soil toxicity categorization. ► Do results obtained by chemical methods really translate into “biological availability”? ► In multi-contaminated sites, risk estimation based only on chemical methods is inadequate. ► Bioassays provide a more realistic risk assessment of contaminated sites. - Bioassays provide a more complete and relevant information to characterize the risk of contaminated soils, and should be used to complement chemical results.
Westrell, T; Schönning, C; Stenström, T A; Ashbolt, N J
2004-01-01
Hazard Analysis and Critical Control Points (HACCP) was applied for identifying and controlling exposure to pathogenic microorganisms encountered during normal sludge and wastewater handling at a 12,500 m3/d treatment plant utilising tertiary wastewater treatment and mesophilic sludge digestion. The hazardous scenarios considered were human exposure during treatment, handling, soil application and crop consumption, and exposure via water at the wetland-area and recreational swimming. A quantitative microbial risk assessment (QMRA), including rotavirus, adenovirus, haemorrhagic E. coli, Salmonella, Giardia and Cryptosporidium, was performed in order to prioritise pathogen hazards for control purposes. Human exposures were treated as individual risks but also related to the endemic situation in the general population. The highest individual health risk from a single exposure was via aerosols for workers at the belt press for sludge dewatering (virus infection risk = 1). The largest impact on the community would arise if children ingested sludge at the unprotected storage site, although in the worst-case situation the largest number of infections would arise through vegetables fertilised with sludge and eaten raw (not allowed in Sweden). Acceptable risk for various hazardous scenarios, treatment and/or reuse strategies could be tested in the model. PMID:15344769
Wiegerinck, Wim; Schoenaker, Christiaan; Duane, Gregory
2016-04-01
Recently, methods for model fusion by dynamically combining model components in an interactive ensemble have been proposed. In these proposals, fusion parameters have to be learned from data. One can view these systems as parametrized dynamical systems. We address the question of learnability of dynamical systems with respect to both short term (vector field) and long term (attractor) behavior. In particular we are interested in learning in the imperfect model class setting, in which the ground truth has a higher complexity than the models, e.g. due to unresolved scales. We take a Bayesian point of view and we define a joint log-likelihood that consists of two terms, one is the vector field error and the other is the attractor error, for which we take the L1 distance between the stationary distributions of the model and the assumed ground truth. In the context of linear models (like so-called weighted supermodels), and assuming a Gaussian error model in the vector fields, vector field learning leads to a tractable Gaussian solution. This solution can then be used as a prior for the next step, Bayesian attractor learning, in which the attractor error is used as a log-likelihood term. Bayesian attractor learning is implemented by elliptical slice sampling, a sampling method for systems with a Gaussian prior and a non Gaussian likelihood. Simulations with a partially observed driven Lorenz 63 system illustrate the approach.
Bayesian theory and applications
Dellaportas, Petros; Polson, Nicholas G; Stephens, David A
2013-01-01
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...
Unbounded Bayesian Optimization via Regularization
Shahriari, Bobak; Bouchard-Côté, Alexandre; De Freitas, Nando
2015-01-01
Bayesian optimization has recently emerged as a popular and efficient tool for global optimization and hyperparameter tuning. Currently, the established Bayesian optimization practice requires a user-defined bounding box which is assumed to contain the optimizer. However, when little is known about the probed objective function, it can be difficult to prescribe such bounds. In this work we modify the standard Bayesian optimization framework in a principled way to allow automatic resizing of t...
Bayesian optimization for materials design
Frazier, Peter I.; Wang, Jialei
2015-01-01
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian pro...
Institute of Scientific and Technical Information of China (English)
李红; 樊燕燕; 庞伟英
2015-01-01
Scaffolding collapse accidents often occur in the engineering project,and cause serious consequences. Through using the fault tree analysis of Bayesian network not only avoids the traditional fault three quantitative shortcoming,but also finds out the main factors of scaffolding collapse accident. First,the logical relationship between the various risk factors is established through the fault tree analysis,and according to the transformation rules the fault tree mode can be transformed into Bayesian Network model,the prior probability of scaffolding collapse accident and the posterior probability of basic events can be calculated by reasoning the ability of the Bayesian network. In addition,the description of an event can not only use happen and not happen,so the part of the basic events is corrected by the two states into three states,and calculate the prior probability and the posterior probability. The above results were compared and analyzed,which shows that considering the polymorphism events can get more accurate probability distribution of events. Finally,given that the posterior probability of the scaffold occurring event is relative large,some scaffolding collapse prevention measures are put forward.%针对脚手架坍塌事故在工程项目中发生比较频繁，造成的后果比较严重。通过运用贝叶斯网络的故障树方法，不仅避免了传统故障树法的缺点，也定量找出脚手架坍塌事故的主要因素。首先应用故障树法建立风险因素之间的逻辑关系，并按照转化规则将故障树模型转化成贝叶斯网络模型，应用贝叶斯网络的推理能力计算出脚手架坍塌的先验概率以及各底事件的后验概率。另外，一个事件不能仅仅用发生和不发生描述，所以将部分基本事件由二态修正成三态，计算出先验概率和后验概率。通过以上得到的结果进行对比分析，从而表明，考虑事件的多态性可以更加准确的得到事件
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Decentralized Distributed Bayesian Estimation
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
Praha: ÚTIA AVČR, v.v.i, 2011 - (Janžura, M.; Ivánek, J.). s. 16-16 [7th International Workshop on Data–Algorithms–Decision Making. 27.11.2011-29.11.2011, Mariánská] R&D Projects: GA ČR 102/08/0567; GA ČR GA102/08/0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : estimation * distributed estimation * model Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2011/AS/dedecius-decentralized distributed bayesian estimation.pdf
Congdon, Peter
2014-01-01
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBU
Computationally efficient Bayesian tracking
Aughenbaugh, Jason; La Cour, Brian
2012-06-01
In this paper, we describe the progress we have achieved in developing a computationally efficient, grid-based Bayesian fusion tracking system. In our approach, the probability surface is represented by a collection of multidimensional polynomials, each computed adaptively on a grid of cells representing state space. Time evolution is performed using a hybrid particle/grid approach and knowledge of the grid structure, while sensor updates use a measurement-based sampling method with a Delaunay triangulation. We present an application of this system to the problem of tracking a submarine target using a field of active and passive sonar buoys.
Improved iterative Bayesian unfolding
D'Agostini, G
2010-01-01
This paper reviews the basic ideas behind a Bayesian unfolding published some years ago and improves their implementation. In particular, uncertainties are now treated at all levels by probability density functions and their propagation is performed by Monte Carlo integration. Thus, small numbers are better handled and the final uncertainty does not rely on the assumption of normality. Theoretical and practical issues concerning the iterative use of the algorithm are also discussed. The new program, implemented in the R language, is freely available, together with sample scripts to play with toy models.
Prediction of road accidents: A Bayesian hierarchical approach
DEFF Research Database (Denmark)
Deublein, Markus; Schubert, Matthias; Adey, Bryan T.;
2013-01-01
-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks...... in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models.Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis...... of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions...
Bayesian Inference on Gravitational Waves
Directory of Open Access Journals (Sweden)
Asad Ali
2015-12-01
Full Text Available The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.
Adaptive Dynamic Bayesian Networks
Energy Technology Data Exchange (ETDEWEB)
Ng, B M
2007-10-26
A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.
Bayesian analysis toolkit - BAT
International Nuclear Information System (INIS)
Statistical treatment of data is an essential part of any data analysis and interpretation. Different statistical methods and approaches can be used, however the implementation of these approaches is complicated and at times inefficient. The Bayesian analysis toolkit (BAT) is a software package developed in C++ framework that facilitates the statistical analysis of the data using Bayesian theorem. The tool evaluates the posterior probability distributions for models and their parameters using Markov Chain Monte Carlo which in turn provide straightforward parameter estimation, limit setting and uncertainty propagation. Additional algorithms, such as simulated annealing, allow extraction of the global mode of the posterior. BAT sets a well-tested environment for flexible model definition and also includes a set of predefined models for standard statistical problems. The package is interfaced to other software packages commonly used in high energy physics, such as ROOT, Minuit, RooStats and CUBA. We present a general overview of BAT and its algorithms. A few physics examples are shown to introduce the spectrum of its applications. In addition, new developments and features are summarized.
Book review: Bayesian analysis for population ecology
Link, William A.
2011-01-01
Brian Dennis described the field of ecology as “fertile, uncolonized ground for Bayesian ideas.” He continued: “The Bayesian propagule has arrived at the shore. Ecologists need to think long and hard about the consequences of a Bayesian ecology. The Bayesian outlook is a successful competitor, but is it a weed? I think so.” (Dennis 2004)
DEFF Research Database (Denmark)
Hartelius, Karsten; Carstensen, Jens Michael
2003-01-01
A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov random field (MRF) model which...... represents the spatial coordinates of the grid nodes. Knowledge of how grid nodes are depicted in the observed image is described through the observation model. The prior consists of a node prior and an arc (edge) prior, both modeled as Gaussian MRFs. The node prior models variations in the positions of grid...... nodes and the arc prior models variations in row and column spacing across the grid. Grid matching is done by placing an initial rough grid over the image and applying an ensemble annealing scheme to maximize the posterior distribution of the grid. The method can be applied to noisy images with missing...
Crotta, Matteo; Rizzi, Rita; Varisco, Giorgio; Daminelli, Paolo; Cunico, Elena Cosciani; Luini, Mario; Graber, Hans Ulrich; Paterlini, Franco; Guitian, Javier
2016-03-01
Quantitative microbial risk assessment (QMRA) models are extensively applied to inform management of a broad range of food safety risks. Inevitably, QMRA modeling involves an element of simplification of the biological process of interest. Two features that are frequently simplified or disregarded are the pathogenicity of multiple strains of a single pathogen and consumer behavior at the household level. In this study, we developed a QMRA model with a multiple-strain approach and a consumer phase module (CPM) based on uncertainty distributions fitted from field data. We modeled exposure to staphylococcal enterotoxin A in raw milk in Lombardy; a specific enterotoxin production module was thus included. The model is adaptable and could be used to assess the risk related to other pathogens in raw milk as well as other staphylococcal enterotoxins. The multiplestrain approach, implemented as a multinomial process, allowed the inclusion of variability and uncertainty with regard to pathogenicity at the bacterial level. Data from 301 questionnaires submitted to raw milk consumers were used to obtain uncertainty distributions for the CPM. The distributions were modeled to be easily updatable with further data or evidence. The sources of uncertainty due to the multiple-strain approach and the CPM were identified, and their impact on the output was assessed by comparing specific scenarios to the baseline. When the distributions reflecting the uncertainty in consumer behavior were fixed to the 95th percentile, the risk of exposure increased up to 160 times. This reflects the importance of taking into consideration the diversity of consumers' habits at the household level and the impact that the lack of knowledge about variables in the CPM can have on the final QMRA estimates. The multiple-strain approach lends itself to use in other food matrices besides raw milk and allows the model to better capture the complexity of the real world and to be capable of geographical
Asymptotically minimax Bayesian predictive densities for multinomial models
Komaki, Fumiyasu
2011-01-01
One-step ahead prediction for the multinomial model is considered. The performance of a predictive density is evaluated by the average Kullback-Leibler divergence from the true density to the predictive density. Asymptotic approximations of risk functions of Bayesian predictive densities based on Dirichlet priors are obtained. It is shown that a Bayesian predictive density based on a specific Dirichlet prior is asymptotically minimax. The asymptotically minimax prior is different from known objective priors such as the Jeffreys prior or the uniform prior.
Hamadeh, Ahmed F.
2014-03-01
Constructed wetlands (CWs) and managed aquifer recharge (MAR) represent commonly used natural treatment systems for reclamation and reuse of wastewater. However, each of these technologies have some limitations with respect to removal of different contaminants. Combining these two technologies into a hybrid CW-MAR system will lead to synergy in terms of both water quality and costs. This promising technology will help in the reduction of bacteria and viruses, trace and heavy metals, organic micropollutants, and nutrients. Use of subsurface flow CWs as pre-treatment for MAR has multiple benefits: (i) it creates a barrier for different microbial and chemical pollutants, (ii) it reduces the residence time for water recovery, and (iii) it avoids clogging during MAR as CWs can remove suspended solids and enhance the reclaimed water quality. This paper analyzes the removal of different contaminants by CW and MAR systems based on a literature review. It is expected that a combination of these natural treatment systems (CWs and MAR) could become an attractive, efficient and cost-effective technology for water reclamation and reuse. © IWA Publishing 2014.
Bayesian Methods and Universal Darwinism
Campbell, John
2010-01-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a 'copy with selective retention' algorithm abstracted from Darwin's theory of...
Portfolio Allocation for Bayesian Optimization
Brochu, Eric; Hoffman, Matthew W.; De Freitas, Nando
2010-01-01
Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several differen...
Neuronanatomy, neurology and Bayesian networks
Bielza Lozoya, Maria Concepcion
2014-01-01
Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In this keynote talk we will pinpoint a number of neuroscience problems that can be addressed using Bayesian networks. In neuroanatomy, we will show computer simulation models of dendritic trees and classification of neuron types, both based on morphological features. In neurology, we will present the search for genetic biomarkers in Alzheimer's disease and the prediction of health-related qualit...
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification......, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended...
Directory of Open Access Journals (Sweden)
YounJung Jung
2015-07-01
Full Text Available Four different manufactured surface-coated silver nanoparticles (AgNPs with coating of citrate, tannic acid, polyethylene glycol, and branched polyethylenimine were used in this study. The toxicity of surface-coated AgNPs was evaluated by a luminous microbial array for toxicity risk assessment (LumiMARA using multi-species of luminescent bacteria. The salt stability of four different AgNPs was measured by UV absorbance at 400 nm wavelength, and different surface-charged AgNPs in combination with bacteria were observed using scanning electron microscopy (SEM. Both branched polyethylenimine (BPEI-AgNPs and polyethylene glycol (PEG-AgNPs were shown to be stable with 2% NaCl (non-aggregation, whereas both citrate (Cit-AgNPs and tannic acid (Tan-AgNPs rapidly aggregated in 2% NaCl solution. The values of the 50% effective concentration (EC50 for BPEI-AgNPs in marine bacteria strains (1.57 to 5.19 mg/L were lower than those for the other surface-coated AgNPs (i.e., Cit-AgNPs, Tan-AgNPs, and PEG-AgNPs. It appears that the toxicity of AgNPs could be activated by the interaction of positively charged AgNPs with the negatively charged bacterial cell wall from the results of LumiMARA. LumiMARA for toxicity screening has advantageous compared to a single-species bioassay and is applicable for environmental samples as displaying ranges of assessment results.
Dale Poirier
2008-01-01
This paper provides Bayesian rationalizations for White’s heteroskedastic consistent (HC) covariance estimator and various modifications of it. An informed Bayesian bootstrap provides the statistical framework.
Nonparametric Bayesian Classification
Coram, M A
2002-01-01
A Bayesian approach to the classification problem is proposed in which random partitions play a central role. It is argued that the partitioning approach has the capacity to take advantage of a variety of large-scale spatial structures, if they are present in the unknown regression function $f_0$. An idealized one-dimensional problem is considered in detail. The proposed nonparametric prior uses random split points to partition the unit interval into a random number of pieces. This prior is found to provide a consistent estimate of the regression function in the $\\L^p$ topology, for any $1 \\leq p < \\infty$, and for arbitrary measurable $f_0:[0,1] \\rightarrow [0,1]$. A Markov chain Monte Carlo (MCMC) implementation is outlined and analyzed. Simulation experiments are conducted to show that the proposed estimate compares favorably with a variety of conventional estimators. A striking resemblance between the posterior mean estimate and the bagged CART estimate is noted and discussed. For higher dimensions, a ...
BAT - Bayesian Analysis Toolkit
International Nuclear Information System (INIS)
One of the most vital steps in any data analysis is the statistical analysis and comparison with the prediction of a theoretical model. The many uncertainties associated with the theoretical model and the observed data require a robust statistical analysis tool. The Bayesian Analysis Toolkit (BAT) is a powerful statistical analysis software package based on Bayes' Theorem, developed to evaluate the posterior probability distribution for models and their parameters. It implements Markov Chain Monte Carlo to get the full posterior probability distribution that in turn provides a straightforward parameter estimation, limit setting and uncertainty propagation. Additional algorithms, such as Simulated Annealing, allow to evaluate the global mode of the posterior. BAT is developed in C++ and allows for a flexible definition of models. A set of predefined models covering standard statistical cases are also included in BAT. It has been interfaced to other commonly used software packages such as ROOT, Minuit, RooStats and CUBA. An overview of the software and its algorithms is provided along with several physics examples to cover a range of applications of this statistical tool. Future plans, new features and recent developments are briefly discussed.
Bayesian seismic AVO inversion
Energy Technology Data Exchange (ETDEWEB)
Buland, Arild
2002-07-01
A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P-wave velocity, S-wave velocity and density. Distributions for other elastic parameters can also be assessed, for example acoustic impedance, shear impedance and P-wave to S-wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance, hence exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3-D dataset from the Sleipner Field. The results show good agreement with well logs but the uncertainty is high. The stochastic model includes uncertainties of both the elastic parameters, the wavelet and the seismic and well log data. The posterior distribution is explored by Markov chain Monte Carlo simulation using the Gibbs sampler algorithm. The inversion algorithm has been tested on a seismic line from the Heidrun Field with two wells located on the line. The uncertainty of the estimated wavelet is low. In the Heidrun examples the effect of including uncertainty of the wavelet and the noise level was marginal with respect to the AVO inversion results. We have developed a 3-D linearized AVO inversion method with spatially coupled model parameters where the objective is to obtain posterior distributions for P-wave velocity, S
Bayesian detection of causal rare variants under posterior consistency.
Liang, Faming
2013-07-26
Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.
Bayesian detection of causal rare variants under posterior consistency.
Directory of Open Access Journals (Sweden)
Faming Liang
Full Text Available Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD, to tackle this problem. The new method simultaneously addresses two issues: (i (Global association test Are there any of the variants associated with the disease, and (ii (Causal variant detection Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.
Bayesian modeling using WinBUGS
Ntzoufras, Ioannis
2009-01-01
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...
Probability biases as Bayesian inference
Directory of Open Access Journals (Sweden)
Andre; C. R. Martins
2006-11-01
Full Text Available In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions. %XX In that sense, it should be no surprise that humans reason with % probability as it has been observed.
Bayesian Methods and Universal Darwinism
Campbell, John
2010-01-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a 'copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that system...
Bayesian methods for proteomic biomarker development
Directory of Open Access Journals (Sweden)
Belinda Hernández
2015-12-01
In this review we provide an introduction to Bayesian inference and demonstrate some of the advantages of using a Bayesian framework. We summarize how Bayesian methods have been used previously in proteomics and other areas of bioinformatics. Finally, we describe some popular and emerging Bayesian models from the statistical literature and provide a worked tutorial including code snippets to show how these methods may be applied for the evaluation of proteomic biomarkers.
Bayesian test and Kuhn's paradigm
Institute of Scientific and Technical Information of China (English)
Chen Xiaoping
2006-01-01
Kuhn's theory of paradigm reveals a pattern of scientific progress,in which normal science alternates with scientific revolution.But Kuhn underrated too much the function of scientific test in his pattern,because he focuses all his attention on the hypothetico-deductive schema instead of Bayesian schema.This paper employs Bayesian schema to re-examine Kuhn's theory of paradigm,to uncover its logical and rational components,and to illustrate the tensional structure of logic and belief,rationality and irrationality,in the process of scientific revolution.
3D Bayesian contextual classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
2000-01-01
We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....
Bayesian Model Averaging for Propensity Score Analysis
Kaplan, David; Chen, Jianshen
2013-01-01
The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…
Bayesian networks and food security - An introduction
Stein, A.
2004-01-01
This paper gives an introduction to Bayesian networks. Networks are defined and put into a Bayesian context. Directed acyclical graphs play a crucial role here. Two simple examples from food security are addressed. Possible uses of Bayesian networks for implementation and further use in decision sup
Bayesian variable order Markov models: Towards Bayesian predictive state representations
C. Dimitrakakis
2009-01-01
We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more st
O'Toole, J; Keywood, M; Sinclair, M; Leder, K
2009-01-01
The aim of this study was to address existing data gaps and to determine the size distribution of aerosols associated with water-efficient devices during typical domestic activities. This information is important to assist in understanding infection spread during water-using activities and in designing water regulations. Three water-using scenarios were evaluated: i) showering using a water-efficient showerhead; ii) use of a high pressure spray unit for cleaning cars and iii) toilet flushing using a dual flush low volume flush device. For each scenario a control condition (conventional lower efficiency device) was selected for benchmarking purposes. Shower module results highlighted the complexity of particle generation and removal processes and showed that more than 90% of total particle mass in the breathing zone was attributed to particle diameters greater than 6 mum. Conversely, results for car washing experiments showed that particle diameters up to 6 mum constituted the major part of the total mass generated by both water-efficient and conventional devices. Even under worse case scenario conditions for toilet flushing, particle measurements were at or below the level of detection of the measuring instrumentation. The data provide information that assists in health risk assessment and in determining future research directions, including methodological aspects. PMID:19934513
Bayesian point event modeling in spatial and environmental epidemiology.
Lawson, Andrew B
2012-10-01
This paper reviews the current state of point event modeling in spatial epidemiology from a Bayesian perspective. Point event (or case event) data arise when geo-coded addresses of disease events are available. Often, this level of spatial resolution would not be accessible due to medical confidentiality constraints. However, for the examination of small spatial scales, it is important to be capable of examining point process data directly. Models for such data are usually formulated based on point process theory. In addition, special conditioning arguments can lead to simpler Bernoulli likelihoods and logistic spatial models. Goodness-of-fit diagnostics and Bayesian residuals are also considered. Applications within putative health hazard risk assessment, cluster detection, and linkage to environmental risk fields (misalignment) are considered. PMID:23035034
Bayesian Analysis of Experimental Data
Directory of Open Access Journals (Sweden)
Lalmohan Bhar
2013-10-01
Full Text Available Analysis of experimental data from Bayesian point of view has been considered. Appropriate methodology has been developed for application into designed experiments. Normal-Gamma distribution has been considered for prior distribution. Developed methodology has been applied to real experimental data taken from long term fertilizer experiments.
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis Linda
2006-01-01
configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for the salt and pepper noise. The inference in the model is discussed...
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis
configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in...
ANALYSIS OF BAYESIAN CLASSIFIER ACCURACY
Directory of Open Access Journals (Sweden)
Felipe Schneider Costa
2013-01-01
Full Text Available The naÃ¯ve Bayes classifier is considered one of the most effective classification algorithms today, competing with more modern and sophisticated classifiers. Despite being based on unrealistic (naÃ¯ve assumption that all variables are independent, given the output class, the classifier provides proper results. However, depending on the scenario utilized (network structure, number of samples or training cases, number of variables, the network may not provide appropriate results. This study uses a process variable selection, using the chi-squared test to verify the existence of dependence between variables in the data model in order to identify the reasons which prevent a Bayesian network to provide good performance. A detailed analysis of the data is also proposed, unlike other existing work, as well as adjustments in case of limit values between two adjacent classes. Furthermore, variable weights are used in the calculation of a posteriori probabilities, calculated with mutual information function. Tests were applied in both a naÃ¯ve Bayesian network and a hierarchical Bayesian network. After testing, a significant reduction in error rate has been observed. The naÃ¯ve Bayesian network presented a drop in error rates from twenty five percent to five percent, considering the initial results of the classification process. In the hierarchical network, there was not only a drop in fifteen percent error rate, but also the final result came to zero.
Bayesian Agglomerative Clustering with Coalescents
Teh, Yee Whye; Daumé III, Hal; Roy, Daniel
2009-01-01
We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman's coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show experimentally the superiority of our algorithms over others, and demonstrate our approach in document clustering and phylolinguistics.
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new...
Topics in Bayesian statistics and maximum entropy
International Nuclear Information System (INIS)
Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling. The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information, and Bayesian court decision-making. (author)
Bayesian Spatial Modelling with R-INLA
Directory of Open Access Journals (Sweden)
Finn Lindgren
2015-02-01
Full Text Available The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA approach proposed by Rue, Martino, and Chopin (2009 is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized linear mixed to spatial and spatio-temporal models. Combined with the stochastic partial differential equation approach (SPDE, Lindgren, Rue, and Lindstrm 2011, one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial mod- els, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.
A genetic and spatial Bayesian analysis of mastitis resistance
Frigessi Arnoldo; Sæbø Solve
2004-01-01
Abstract A nationwide health card recording system for dairy cattle was introduced in Norway in 1975 (the Norwegian Cattle Health Services). The data base holds information on mastitis occurrences on an individual cow basis. A reduction in mastitis frequency across the population is desired, and for this purpose risk factors are investigated. In this paper a Bayesian proportional hazards model is used for modelling the time to first veterinary treatment of clinical mastitis, including both ge...
A genetic and spatial Bayesian analysis of mastitis resistance
Sæbø, Solve; Frigessi, Arnoldo
2004-01-01
A nationwide health card recording system for dairy cattle was introduced in Norway in 1975 (the Norwegian Cattle Health Services). The data base holds information on mastitis occurrences on an individual cow basis. A reduction in mastitis frequency across the population is desired, and for this purpose risk factors are investigated. In this paper a Bayesian proportional hazards model is used for modelling the time to first veterinary treatment of clinical mastitis, including both genetic and...
Bayesian methods for measures of agreement
Broemeling, Lyle D
2009-01-01
Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process.The author employs a Bayesian approach to provide statistical inferences based on various models of intra- and interrater agreement. He presents many examples that illustrate the Bayesian mode of reasoning and explains elements of a Bayesian application, including prior information, experimental information, the likelihood function, posterior distribution, and predictive distribution. The appendices provide the necessary theoretical foundation to understand Bayesian methods as well as introduce the fundamentals of programming and executing the WinBUGS software.Taking a Bayesian approach to inference, this hands-on book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation...
Plug & Play object oriented Bayesian networks
DEFF Research Database (Denmark)
Bangsø, Olav; Flores, J.; Jensen, Finn Verner
2003-01-01
Object oriented Bayesian networks have proven themselves useful in recent years. The idea of applying an object oriented approach to Bayesian networks has extended their scope to larger domains that can be divided into autonomous but interrelated entities. Object oriented Bayesian networks have...... been shown to be quite suitable for dynamic domains as well. However, processing object oriented Bayesian networks in practice does not take advantage of their modular structure. Normally the object oriented Bayesian network is transformed into a Bayesian network and, inference is performed...... by constructing a junction tree from this network. In this paper we propose a method for translating directly from object oriented Bayesian networks to junction trees, avoiding the intermediate translation. We pursue two main purposes: firstly, to maintain the original structure organized in an instance tree...
Flexible Bayesian Nonparametric Priors and Bayesian Computational Methods
Zhu, Weixuan
2016-01-01
The definition of vectors of dependent random probability measures is a topic of interest in Bayesian nonparametrics. They represent dependent nonparametric prior distributions that are useful for modelling observables for which specific covariate values are known. Our first contribution is the introduction of novel multivariate vectors of two-parameter Poisson-Dirichlet process. The dependence is induced by applying a L´evy copula to the marginal L´evy intensities. Our attenti...
Energy Technology Data Exchange (ETDEWEB)
Konopka, Allan
2009-03-19
Microbial ecology is a relatively young discipline within the field of microbiology. Its modern history spans just the past 60 years, and the field is defined by its emphasis on understanding the interactions of microbes with their environment, rather than their behavior under artificial laboratory conditions. Because microbes are ubiquitous, microbial ecologists study a broad diversity of habitats that range from aquatic to terrestrial to plant- or animal-associated. This has made it a challenge to identify unifying principles within the field. One approach is to recognize that although the activity of microbes in nature have effects at the macroscale, they interact with their physical, chemical and biological milieu at a scale of micrometers. At this scale, several different microbial ecosystems can be defined, based upon association with particles, the presence of environmental gradients and the continuous availability of water. Principles applicable to microbial ecology reflect not only their population ecology and physiological ecology, but also their broad versatility and quantitative importance in the biosphere as biogeochemical catalysts and capacity for rapid physiological and evolutionary responses.
Attention in a bayesian framework
DEFF Research Database (Denmark)
Whiteley, Louise Emma; Sahani, Maneesh
2012-01-01
include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental...... settings, where cues shape expectations about a small number of upcoming stimuli and thus convey "prior" information about clearly defined objects. While operationally consistent with the experiments it seeks to describe, this view of attention as prior seems to miss many essential elements of both its......The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models of...
Bayesian Sampling using Condition Indicators
DEFF Research Database (Denmark)
Faber, Michael H.; Sørensen, John Dalsgaard
2002-01-01
allows for a Bayesian formulation of the indicators whereby the experience and expertise of the inspection personnel may be fully utilized and consistently updated as frequentistic information is collected. The approach is illustrated on an example considering a concrete structure subject to corrosion......The problem of control quality of components is considered for the special case where the acceptable failure rate is low, the test costs are high and where it may be difficult or impossible to test the condition of interest directly. Based on the classical control theory and the concept of...... condition indicators introduced by Benjamin and Cornell (1970) a Bayesian approach to quality control is formulated. The formulation is then extended to the case where the quality control is based on sampling of indirect information about the condition of the components, i.e. condition indicators. This...
BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS
Directory of Open Access Journals (Sweden)
Thordis Linda Thorarinsdottir
2011-05-01
Full Text Available In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.
Bayesian Seismology of the Sun
Gruberbauer, Michael
2013-01-01
We perform a Bayesian grid-based analysis of the solar l=0,1,2 and 3 p modes obtained via BiSON in order to deliver the first Bayesian asteroseismic analysis of the solar composition problem. We do not find decisive evidence to prefer either of the contending chemical compositions, although the revised solar abundances (AGSS09) are more probable in general. We do find indications for systematic problems in standard stellar evolution models, unrelated to the consequences of inadequate modelling of the outer layers on the higher-order modes. The seismic observables are best fit by solar models that are several hundred million years older than the meteoritic age of the Sun. Similarly, meteoritic age calibrated models do not adequately reproduce the observed seismic observables. Our results suggest that these problems will affect any asteroseismic inference that relies on a calibration to the Sun.
Bayesian priors for transiting planets
Kipping, David M
2016-01-01
As astronomers push towards discovering ever-smaller transiting planets, it is increasingly common to deal with low signal-to-noise ratio (SNR) events, where the choice of priors plays an influential role in Bayesian inference. In the analysis of exoplanet data, the selection of priors is often treated as a nuisance, with observers typically defaulting to uninformative distributions. Such treatments miss a key strength of the Bayesian framework, especially in the low SNR regime, where even weak a priori information is valuable. When estimating the parameters of a low-SNR transit, two key pieces of information are known: (i) the planet has the correct geometric alignment to transit and (ii) the transit event exhibits sufficient signal-to-noise to have been detected. These represent two forms of observational bias. Accordingly, when fitting transits, the model parameter priors should not follow the intrinsic distributions of said terms, but rather those of both the intrinsic distributions and the observational ...
Bayesian Inference for Radio Observations
Lochner, Michelle; Zwart, Jonathan T L; Smirnov, Oleg; Bassett, Bruce A; Oozeer, Nadeem; Kunz, Martin
2015-01-01
(Abridged) New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inaccurate uncertainty estimates and biased results because such methods ignore any correlations between parameters. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realisation of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. Thi...
Bayesian inference on proportional elections.
Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio
2015-01-01
Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259
A Bayesian Nonparametric IRT Model
Karabatsos, George
2015-01-01
This paper introduces a flexible Bayesian nonparametric Item Response Theory (IRT) model, which applies to dichotomous or polytomous item responses, and which can apply to either unidimensional or multidimensional scaling. This is an infinite-mixture IRT model, with person ability and item difficulty parameters, and with a random intercept parameter that is assigned a mixing distribution, with mixing weights a probit function of other person and item parameters. As a result of its flexibility...
Bayesian segmentation of hyperspectral images
Mohammadpour, Adel; Mohammad-Djafari, Ali
2007-01-01
In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.
Bayesian segmentation of hyperspectral images
Mohammadpour, Adel; Féron, Olivier; Mohammad-Djafari, Ali
2004-11-01
In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.
Bayesian Stable Isotope Mixing Models
Parnell, Andrew C.; Phillips, Donald L.; Bearhop, Stuart; Semmens, Brice X.; Ward, Eric J.; Moore, Jonathan W.; Andrew L Jackson; Inger, Richard
2012-01-01
In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching Bayesian statistical framework which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixture. The most widely used application is quantifying the diet of organisms based on the food sources they have been observed to consume. At the centre of the multivariate statistical model we propose is a compositional m...
Bayesian Network--Response Regression
WANG, LU; Durante, Daniele; Dunson, David B.
2016-01-01
There is an increasing interest in learning how human brain networks vary with continuous traits (e.g., personality, cognitive abilities, neurological disorders), but flexible procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and Gaussian process priors to allow flexible shifts of the conditional expectation for a network-valued random variable across the feature space, while including subject-specific random eff...
Bayesian estimation of turbulent motion
Héas, P.; Herzet, C.; Mémin, E.; Heitz, D.; P. D. Mininni
2013-01-01
International audience Based on physical laws describing the multi-scale structure of turbulent flows, this article proposes a regularizer for fluid motion estimation from an image sequence. Regularization is achieved by imposing some scale invariance property between histograms of motion increments computed at different scales. By reformulating this problem from a Bayesian perspective, an algorithm is proposed to jointly estimate motion, regularization hyper-parameters, and to select the ...
Elements of Bayesian experimental design
Energy Technology Data Exchange (ETDEWEB)
Sivia, D.S. [Rutherford Appleton Lab., Oxon (United Kingdom)
1997-09-01
We consider some elements of the Bayesian approach that are important for optimal experimental design. While the underlying principles used are very general, and are explained in detail in a recent tutorial text, they are applied here to the specific case of characterising the inferential value of different resolution peakshapes. This particular issue was considered earlier by Silver, Sivia and Pynn (1989, 1990a, 1990b), and the following presentation confirms and extends the conclusions of their analysis.
Skill Rating by Bayesian Inference
Di Fatta, Giuseppe; Haworth, Guy McCrossan; Regan, Kenneth W.
2009-01-01
Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players’ skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of ...
Topics in Nonparametric Bayesian Statistics
2003-01-01
The intersection set of Bayesian and nonparametric statistics was almost empty until about 1973, but now seems to be growing at a healthy rate. This chapter gives an overview of various theoretical and applied research themes inside this field, partly complementing and extending recent reviews of Dey, Müller and Sinha (1998) and Walker, Damien, Laud and Smith (1999). The intention is not to be complete or exhaustive, but rather to touch on research areas of interest, partly by example.
Cover Tree Bayesian Reinforcement Learning
Tziortziotis, Nikolaos; Dimitrakakis, Christos; Blekas, Konstantinos
2013-01-01
This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian piecewise-linear models, which can be updated in closed form. The tree structure itself is constructed using the cover tree method, which remains efficient in high dimensional spaces. We combine the model with Thompson sampling and approximate dynamic programming to obtain effective exploration po...
Bayesian kinematic earthquake source models
Minson, S. E.; Simons, M.; Beck, J. L.; Genrich, J. F.; Galetzka, J. E.; Chowdhury, F.; Owen, S. E.; Webb, F.; Comte, D.; Glass, B.; Leiva, C.; Ortega, F. H.
2009-12-01
Most coseismic, postseismic, and interseismic slip models are based on highly regularized optimizations which yield one solution which satisfies the data given a particular set of regularizing constraints. This regularization hampers our ability to answer basic questions such as whether seismic and aseismic slip overlap or instead rupture separate portions of the fault zone. We present a Bayesian methodology for generating kinematic earthquake source models with a focus on large subduction zone earthquakes. Unlike classical optimization approaches, Bayesian techniques sample the ensemble of all acceptable models presented as an a posteriori probability density function (PDF), and thus we can explore the entire solution space to determine, for example, which model parameters are well determined and which are not, or what is the likelihood that two slip distributions overlap in space. Bayesian sampling also has the advantage that all a priori knowledge of the source process can be used to mold the a posteriori ensemble of models. Although very powerful, Bayesian methods have up to now been of limited use in geophysical modeling because they are only computationally feasible for problems with a small number of free parameters due to what is called the "curse of dimensionality." However, our methodology can successfully sample solution spaces of many hundreds of parameters, which is sufficient to produce finite fault kinematic earthquake models. Our algorithm is a modification of the tempered Markov chain Monte Carlo (tempered MCMC or TMCMC) method. In our algorithm, we sample a "tempered" a posteriori PDF using many MCMC simulations running in parallel and evolutionary computation in which models which fit the data poorly are preferentially eliminated in favor of models which better predict the data. We present results for both synthetic test problems as well as for the 2007 Mw 7.8 Tocopilla, Chile earthquake, the latter of which is constrained by InSAR, local high
Bayesian Kernel Mixtures for Counts
Canale, Antonio; David B Dunson
2011-01-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviatio...
Bayesian Optimization for Adaptive MCMC
Mahendran, Nimalan; Wang, Ziyu; Hamze, Firas; De Freitas, Nando
2011-01-01
This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to non-differentiable objective functions and trades off exploration and exploitation to reduce the number of potentially costly objective function evaluations. We demonstrate the strategy in the complex setting of sampling from constrained, discrete and densely connected probabilistic graphical models where, for each variation of the problem, one needs to adjust the parameters o...
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael;
2009-01-01
and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....
Quantile pyramids for Bayesian nonparametrics
2009-01-01
P\\'{o}lya trees fix partitions and use random probabilities in order to construct random probability measures. With quantile pyramids we instead fix probabilities and use random partitions. For nonparametric Bayesian inference we use a prior which supports piecewise linear quantile functions, based on the need to work with a finite set of partitions, yet we show that the limiting version of the prior exists. We also discuss and investigate an alternative model based on the so-called substitut...
Space Shuttle RTOS Bayesian Network
Morris, A. Terry; Beling, Peter A.
2001-01-01
With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores
Bayesian analysis of contingency tables
Gómez Villegas, Miguel A.; González Pérez, Beatriz
2005-01-01
The display of the data by means of contingency tables is used in different approaches to statistical inference, for example, to broach the test of homogeneity of independent multinomial distributions. We develop a Bayesian procedure to test simple null hypotheses versus bilateral alternatives in contingency tables. Given independent samples of two binomial distributions and taking a mixed prior distribution, we calculate the posterior probability that the proportion of successes in the first...
Bayesian Credit Ratings (new version)
Paola Cerchiello; Paolo Giudici
2013-01-01
In this contribution we aim at improving ordinal variable selection in the context of causal models. In this regard, we propose an approach that provides a formal inferential tool to compare the explanatory power of each covariate, and, therefore, to select an effective model for classification purposes. Our proposed model is Bayesian nonparametric, and, thus, keeps the amount of model specification to a minimum. We consider the case in which information from the covariates is at the ordinal ...
Bayesian second law of thermodynamics
Bartolotta, Anthony; Carroll, Sean M.; Leichenauer, Stefan; Pollack, Jason
2016-08-01
We derive a generalization of the second law of thermodynamics that uses Bayesian updates to explicitly incorporate the effects of a measurement of a system at some point in its evolution. By allowing an experimenter's knowledge to be updated by the measurement process, this formulation resolves a tension between the fact that the entropy of a statistical system can sometimes fluctuate downward and the information-theoretic idea that knowledge of a stochastically evolving system degrades over time. The Bayesian second law can be written as Δ H (ρm,ρ ) + F |m≥0 , where Δ H (ρm,ρ ) is the change in the cross entropy between the original phase-space probability distribution ρ and the measurement-updated distribution ρm and F |m is the expectation value of a generalized heat flow out of the system. We also derive refined versions of the second law that bound the entropy increase from below by a non-negative number, as well as Bayesian versions of integral fluctuation theorems. We demonstrate the formalism using simple analytical and numerical examples.
Quantum Inference on Bayesian Networks
Yoder, Theodore; Low, Guang Hao; Chuang, Isaac
2014-03-01
Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.
Institute of Scientific and Technical Information of China (English)
陈帆; 谢洪涛
2014-01-01
传统的计算方法难以对建筑技术创新风险进行定量分析。通过专家先验知识与问卷调查数据相结合，可以建立建筑技术创新风险评估的贝叶斯信念网络模型。采用NETICA软件拟合样本数据，得到网络模型各个节点间的条件概率分布。在某绿色建筑技术创新项目中应用结果表明贝叶斯网络模型能够比较准确地实现对建筑技术创新风险的定量预测，与工程实际情况对比具有较好的符合性。%Risk assessment of construction technology innovation is an unresolved problem for the traditional calculation method. This paper describes how Bayesian network is applied to quantifying the probability of construction technology innovation risks. Based on the review of relevant literature and survey, a Bayesian network model is constructed to risk assessment of construction technology innovation. Through using the NETICA software to fit the data samples of ques-tionnaire investigation, it gets the probability density of most nodes. The validity of the proposed model is tested by using a realistic case study with high compliance at last.
Pooled Bayesian meta-analysis of two Polish studies on radiation-induced cancers
International Nuclear Information System (INIS)
The robust Bayesian regression method was applied to perform meta-analysis of two independent studies on influence of low ionising radiation doses on the occurrence of fatal cancers. The re-analysed data come from occupational exposure analysis of nuclear workers in Swierk (Poland) and from ecological study of cancer risk from natural background radiation in Poland. Such two different types of data were analysed, and three popular models were tested: constant, linear and quadratic dose-response dependencies. The Bayesian model selection algorithm was used for all models. The Bayesian statistics clearly indicates that the popular linear no-threshold (LNT) assumption is not valid for presented cancer risks in the range of low doses of ionising radiation. The subject of LNT hypothesis use in radiation risk prediction and assessment is also discussed. (authors)
Development of bayesian update database for PRA data analysis (BUDDA)
International Nuclear Information System (INIS)
It is necessary what independent plant PRA (Probabilistic Risk Assessment) for risk informed applications of nuclear power plant. Therefore, it must build the environment that the utilities can efficiently collect PRA data, and can estimate PRA parameters without statistical expertise. This report explains development of failure events analysis DB for PRA failure rate computation using bayesian update technique. BUDDA has the function to compute failure rate with a combination of multiple DB (include the pre-installed data based on NUCIA), and to manage independent plant DB (failure events, number of components, operation time, number of demand , prior distributions). (author)
12th Brazilian Meeting on Bayesian Statistics
Louzada, Francisco; Rifo, Laura; Stern, Julio; Lauretto, Marcelo
2015-01-01
Through refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesia...
Bayesian Posterior Distributions Without Markov Chains
Cole, Stephen R.; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B.
2012-01-01
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential ex...
Predicting Software Suitability Using a Bayesian Belief Network
Beaver, Justin M.; Schiavone, Guy A.; Berrios, Joseph S.
2005-01-01
The ability to reliably predict the end quality of software under development presents a significant advantage for a development team. It provides an opportunity to address high risk components earlier in the development life cycle, when their impact is minimized. This research proposes a model that captures the evolution of the quality of a software product, and provides reliable forecasts of the end quality of the software being developed in terms of product suitability. Development team skill, software process maturity, and software problem complexity are hypothesized as driving factors of software product quality. The cause-effect relationships between these factors and the elements of software suitability are modeled using Bayesian Belief Networks, a machine learning method. This research presents a Bayesian Network for software quality, and the techniques used to quantify the factors that influence and represent software quality. The developed model is found to be effective in predicting the end product quality of small-scale software development efforts.
Bayesian networks with applications in reliability analysis
Langseth, Helge
2002-01-01
A common goal of the papers in this thesis is to propose, formalize and exemplify the use of Bayesian networks as a modelling tool in reliability analysis. The papers span work in which Bayesian networks are merely used as a modelling tool (Paper I), work where models are specially designed to utilize the inference algorithms of Bayesian networks (Paper II and Paper III), and work where the focus has been on extending the applicability of Bayesian networks to very large domains (Paper IV and ...
Directory of Open Access Journals (Sweden)
Ahmed Elshahhat
2016-06-01
Full Text Available Bayesian and non-Bayesian estimators are obtained for the unknown parameters of Weibull distribution based on the generalized Type-II progressive hybrid censoring scheme and different special cases are obtained. The asymptotic variance covariance matrix and approximate confidence intervals based on the asymptotic normality of the maximum likelihood estimators are obtained. Bayes estimates and Bayes risks have been developed under a squared error loss function using informative and non-informative priors for the unknown Weibull parameters. It is observed that the estimators obtained are not available in closed forms, although they can be easily evaluated for a given sample by using suitable numerical methods. Therefore, a numerical example is considered to illustrate the proposed estimators.
Bayesian phylogeography finds its roots.
Directory of Open Access Journals (Sweden)
Philippe Lemey
2009-09-01
Full Text Available As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.
Bayesian Methods and Universal Darwinism
Campbell, John
2009-12-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent Champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a `copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the Operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that Systems will evolve to states of highest entropy subject to the constraints of scientific law. This principle may be inverted to provide illumination as to the nature of scientific law. Our best cosmological theories suggest the universe contained much less complexity during the period shortly after the Big Bang than it does at present. The scientific subject matter of atomic physics, chemistry, biology and the social sciences has been created since that time. An explanation is proposed for the existence of this subject matter as due to the evolution of constraints in the form of adaptations imposed on Maximum Entropy. It is argued these adaptations were discovered and instantiated through the Operations of a succession of Darwinian processes.
Bayesian Query-Focused Summarization
Daumé, Hal
2009-01-01
We present BayeSum (for ``Bayesian summarization''), a model for sentence extraction in query-focused summarization. BayeSum leverages the common case in which multiple documents are relevant to a single query. Using these documents as reinforcement for query terms, BayeSum is not afflicted by the paucity of information in short queries. We show that approximate inference in BayeSum is possible on large data sets and results in a state-of-the-art summarization system. Furthermore, we show how BayeSum can be understood as a justified query expansion technique in the language modeling for IR framework.
Numeracy, frequency, and Bayesian reasoning
Directory of Open Access Journals (Sweden)
Gretchen B. Chapman
2009-02-01
Full Text Available Previous research has demonstrated that Bayesian reasoning performance is improved if uncertainty information is presented as natural frequencies rather than single-event probabilities. A questionnaire study of 342 college students replicated this effect but also found that the performance-boosting benefits of the natural frequency presentation occurred primarily for participants who scored high in numeracy. This finding suggests that even comprehension and manipulation of natural frequencies requires a certain threshold of numeracy abilities, and that the beneficial effects of natural frequency presentation may not be as general as previously believed.
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
2013-01-01
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Collaborative Kalman Filtration: Bayesian Perspective
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil
Lisabon, Portugalsko: Institute for Systems and Technologies of Information, Control and Communication (INSTICC), 2014, s. 468-474. ISBN 978-989-758-039-0. [11th International Conference on Informatics in Control, Automation and Robotics - ICINCO 2014. Vien (AT), 01.09.2014-03.09.2014] R&D Projects: GA ČR(CZ) GP14-06678P Institutional support: RVO:67985556 Keywords : Bayesian analysis * Kalman filter * distributed estimation Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2014/AS/dedecius-0431324.pdf
Directory of Open Access Journals (Sweden)
Peter-Leon Hagedoorn
2015-12-01
Full Text Available Metalloproteomics is a rapidly developing field of science that involves the comprehensive analysis of all metal-containing or metal-binding proteins in a biological sample. The purpose of this review is to offer a comprehensive overview of the research involving approaches that can be categorized as inductively coupled plasma (ICP-MS based methods, X-ray absorption/fluorescence, radionuclide based methods and bioinformatics. Important discoveries in microbial proteomics will be reviewed, as well as the outlook to new emerging approaches and research areas.
International Nuclear Information System (INIS)
The long term safety and integrity of radioactive waste disposal sites proposed for use by Ontario Hydro may be affected by the release of radioactive gases. Microbes mediate the primary pathways of waste degradation and hence an assessment of their potential to produce gaseous end products from the breakdown of low level waste was performed. Due to a number of unknown variables, assumptions were made regarding environmental and waste conditions that controlled microbial activity; however, it was concluded that 14C and 3H would be produced, albeit over a long time scale of about 1500 years for 14C in the worst case situation
Demain, A L
2000-01-01
For thousands of years, microorganisms have been used to supply products such as bread, beer and wine. A second phase of traditional microbial biotechnology began during World War I and resulted in the development of the acetone-butanol and glycerol fermentations, followed by processes yielding, for example, citric acid, vitamins and antibiotics. In the early 1970s, traditional industrial microbiology was merged with molecular biology to yield more than 40 biopharmaceutical products, such as erythropoietin, human growth hormone and interferons. Today, microbiology is a major participant in global industry, especially in the pharmaceutical, food and chemical industries. PMID:10631778
Microbial Ecosystems, Protection of
Bodelier, P.L.E.; Nelson, K.E.
2014-01-01
Synonyms Conservation of microbial diversity and ecosystem functions provided by microbes; Preservation of microbial diversity and ecosystem functions provided by microbes Definition The use, management, and conservation of ecosystems in order to preserve microbial diversity and functioning. Introdu
Bayesian credible interval construction for Poisson statistics
Institute of Scientific and Technical Information of China (English)
ZHU Yong-Sheng
2008-01-01
The construction of the Bayesian credible (confidence) interval for a Poisson observable including both the signal and background with and without systematic uncertainties is presented.Introducing the conditional probability satisfying the requirement of the background not larger than the observed events to construct the Bayesian credible interval is also discussed.A Fortran routine,BPOCI,has been developed to implement the calculation.
Bayesian Decision Theoretical Framework for Clustering
Chen, Mo
2011-01-01
In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…
Bayesian Statistics for Biological Data: Pedigree Analysis
Stanfield, William D.; Carlton, Matthew A.
2004-01-01
The use of Bayes' formula is applied to the biological problem of pedigree analysis to show that the Bayes' formula and non-Bayesian or "classical" methods of probability calculation give different answers. First year college students of biology can be introduced to the Bayesian statistics.
Using Bayesian Networks to Improve Knowledge Assessment
Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra
2013-01-01
In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...... for complex networks can be derived and point out relevant literature....
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan
2004-01-01
We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating and ...
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark
We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by eva...
Bayesian analysis of exoplanet and binary orbits
Schulze-Hartung, Tim; Henning, Thomas
2012-01-01
We introduce BASE (Bayesian astrometric and spectroscopic exoplanet detection and characterisation tool), a novel program for the combined or separate Bayesian analysis of astrometric and radial-velocity measurements of potential exoplanet hosts and binary stars. The capabilities of BASE are demonstrated using all publicly available data of the binary Mizar A.
Computational methods for Bayesian model choice
Robert, Christian P.; Wraith, Darren
2009-01-01
In this note, we shortly survey some recent approaches on the approximation of the Bayes factor used in Bayesian hypothesis testing and in Bayesian model choice. In particular, we reassess importance sampling, harmonic mean sampling, and nested sampling from a unified perspective.
Bayesian Network Assessment Method for Civil Aviation Safety Based on Flight Delays
Huawei Wang; Jun Gao
2013-01-01
Flight delays and safety are the principal contradictions in the sound development of civil aviation. Flight delays often come up and induce civil aviation safety risk simultaneously. Based on flight delays, the random characteristics of civil aviation safety risk are analyzed. Flight delays have been deemed to a potential safety hazard. The change rules and characteristics of civil aviation safety risk based on flight delays have been analyzed. Bayesian networks (BN) have been used to build ...
2nd Bayesian Young Statisticians Meeting
Bitto, Angela; Kastner, Gregor; Posekany, Alexandra
2015-01-01
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session ...
BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION.
Khakabimamaghani, Sahand; Ester, Martin
2016-01-01
The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification. Third, as most of the proposed stratification methods are deterministic, there is a need for investigating the potential benefits of applying probabilistic methods. To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. Our experimental results demonstrate the superiority of B2PS over a popular state-of-the-art method and the benefits of Bayesian approaches. Our results agree with the intuition that transcriptomic data forms a better basis for patient stratification than genomic data. PMID:26776199
Oral chlorhexidine and microbial contamination during endoscopy
DEFF Research Database (Denmark)
Donatsky, Anders Meller; Holzknecht, Barbara Juliane; Arpi, Magnus;
2013-01-01
BACKGROUND: One of the biggest concerns associated with transgastric surgery is contamination and risk of intra-abdominal infection with microbes introduced from the access route. The purpose of this study was to evaluate the effect of oral decontamination with chlorhexidine on microbial contamin......BACKGROUND: One of the biggest concerns associated with transgastric surgery is contamination and risk of intra-abdominal infection with microbes introduced from the access route. The purpose of this study was to evaluate the effect of oral decontamination with chlorhexidine on microbial...... contamination of the endoscope. METHODS: In a prospective, randomized, single-blinded, clinical trial the effect of chlorhexidine mouth rinse was evaluated. As a surrogate for the risk of intra-abdominal contamination during transgastric surgery, microbial contamination of the endoscope during upper endoscopy...... microbial contamination of the endoscope, but micro-organisms with abscess forming capabilities were still present. PPI treatment significantly increased CFU and should be discontinued before transgastric surgery....
Institute of Scientific and Technical Information of China (English)
谭清磊; 陈国明; 付建民
2012-01-01
高含硫集气站中,井口分离器风险分析是集气站安全运行管理的重要环节.传统的因果图风险评价分析方法具有简洁直观,逻辑性强的优点,但具有一定的局限性.贝叶斯网络是一种较新的系统风险分析方法,能较好地表达变量之间的不确性关系且具有双向不确定性推理能力,但不如前者形象直观.采用因果图和贝叶斯网络对分离器液位过低事故进行分析并对比,充分利用两者的优点.结果表明,利用两种方法可以更好地对分离器进行风险分析.%The present paper is aimed at introducing the combination of cause-effect diagram and Bayesian network in hoping to improve the risk assessment of the wellhead separator in high-sulfur natural gas gathering station. As is known, conventional evaluation methods, such as the cause-effect diagram, are likely to produce ineffective and inaccurate assessment, due to their limitations in assessing events binary states and necessary logical relationship of the problems involved , due to their being too simple in structure and intuitive in slow reaction. Since Bayesian network is a kind of rather new risk analysis method, it has made it possible to better express the uncertainty a-mong the variables, reason out the two-way uncertainty, and describe multi-states of the events more clearly. Moreover, it can help to produce an image more accurately than before. This paper would like to analyze low liquid-level separator by cause-effect diagram via Bayesian network and then make a comparison between the results. What is more, we have carefully studied the advantages of the above-said methods. To be sure, first of all, we have simulated the accidents caused by the low liquid level of the* separator via the cause-effect diagram. Next, we have tested and analyzed the wellhead separator security barrier failure and the likely reasons of accident cropping-up so as to boil down the likely causes to the minimum set. In case
Process adjustment by a Bayesian approach
Directory of Open Access Journals (Sweden)
Daniel Duret
2015-12-01
Full Text Available In a production or measure situation, operators are required to make corrections to a process using the measurement of a sample. In both cases, it is always difficult to suggest a correction from a deviation. The correction is the result of two different deviations: one in set-up and the second in production. The latter is considered as noise. The objective of this paper is to propose an original approach to calculate the best correction using a Bayesian approach. A correction formula is given with three assumptions as regards adjusting the distribution: uniform, triangular and normal distribution. This paper gives a graphical interpretation of these different assumptions and a discussion of the results. Based on these results, the paper proposes a practical rule for calculating the most likely maladjustment in the case of a normal distribution. This practical rule gives the best adjustment using a simple relation (Adjustment = K*sample mean where K depends on the sample size, the ratio between the maladjustment and the short-term variability and a Type I risk of large maladjustment.
Bayesian analysis to detect abrupt changes in extreme hydrological processes
Jo, Seongil; Kim, Gwangsu; Jeon, Jong-June
2016-07-01
In this study, we develop a new method for a Bayesian change point analysis. The proposed method is easy to implement and can be extended to a wide class of distributions. Using a generalized extreme-value distribution, we investigate the annual maximum of precipitations observed at stations in the South Korean Peninsula, and find significant changes in the considered sites. We evaluate the hydrological risk in predictions using the estimated return levels. In addition, we explain that the misspecification of the probability model can lead to a bias in the number of change points and using a simple example, show that this problem is difficult to avoid by technical data transformation.
BAYESIAN ESTIMATION OF ERLANG DISTRIBUTION UNDER DIFFERENT PRIOR DISTRIBUTIONS
Directory of Open Access Journals (Sweden)
Abdul Haq
2011-01-01
Full Text Available This paper addresses the problem of Bayesian estimation of the parameters of Erlangdistribution under squared error loss function by assuming different independent informativepriors as well as joint priors for both shape and scale parameters. The motivation is to explore themost appropriate prior for Erlang distribution among different priors. A comparison of the Bayesestimates and their risks for different choices of the values of the hyperparameters is alsopresented. Finally, we illustrate the results using a simulation study as well as by doing real dataanalysis.
Implementation of a Bayesian Engine for Uncertainty Analysis
Energy Technology Data Exchange (ETDEWEB)
Leng Vang; Curtis Smith; Steven Prescott
2014-08-01
In probabilistic risk assessment, it is important to have an environment where analysts have access to a shared and secured high performance computing and a statistical analysis tool package. As part of the advanced small modular reactor probabilistic risk analysis framework implementation, we have identified the need for advanced Bayesian computations. However, in order to make this technology available to non-specialists, there is also a need of a simplified tool that allows users to author models and evaluate them within this framework. As a proof-of-concept, we have implemented an advanced open source Bayesian inference tool, OpenBUGS, within the browser-based cloud risk analysis framework that is under development at the Idaho National Laboratory. This development, the “OpenBUGS Scripter” has been implemented as a client side, visual web-based and integrated development environment for creating OpenBUGS language scripts. It depends on the shared server environment to execute the generated scripts and to transmit results back to the user. The visual models are in the form of linked diagrams, from which we automatically create the applicable OpenBUGS script that matches the diagram. These diagrams can be saved locally or stored on the server environment to be shared with other users.
Bayesian networks in educational assessment
Almond, Russell G; Steinberg, Linda S; Yan, Duanli; Williamson, David M
2015-01-01
Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as ...
Quantum Bayesianism at the Perimeter
Fuchs, Christopher A
2010-01-01
The author summarizes the Quantum Bayesian viewpoint of quantum mechanics, developed originally by C. M. Caves, R. Schack, and himself. It is a view crucially dependent upon the tools of quantum information theory. Work at the Perimeter Institute for Theoretical Physics continues the development and is focused on the hard technical problem of a finding a good representation of quantum mechanics purely in terms of probabilities, without amplitudes or Hilbert-space operators. The best candidate representation involves a mysterious entity called a symmetric informationally complete quantum measurement. Contemplation of it gives a way of thinking of the Born Rule as an addition to the rules of probability theory, applicable when one gambles on the consequences of interactions with physical systems. The article ends by outlining some directions for future work.
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online. PMID:22523437
Hedging Strategies for Bayesian Optimization
Brochu, Eric; de Freitas, Nando
2010-01-01
Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It is able to do this by sampling the objective using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We describe the method, which we call GP-Hedge, and show that this method almost always outperforms the best individual acquisition function.
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
On Bayesian System Reliability Analysis
International Nuclear Information System (INIS)
The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person's state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs
Elvira, Clément; Dobigeon, Nicolas
2015-01-01
Sparse representations have proven their efficiency in solving a wide class of inverse problems encountered in signal and image processing. Conversely, enforcing the information to be spread uniformly over representation coefficients exhibits relevant properties in various applications such as digital communications. Anti-sparse regularization can be naturally expressed through an $\\ell_{\\infty}$-norm penalty. This paper derives a probabilistic formulation of such representations. A new probability distribution, referred to as the democratic prior, is first introduced. Its main properties as well as three random variate generators for this distribution are derived. Then this probability distribution is used as a prior to promote anti-sparsity in a Gaussian linear inverse problem, yielding a fully Bayesian formulation of anti-sparse coding. Two Markov chain Monte Carlo (MCMC) algorithms are proposed to generate samples according to the posterior distribution. The first one is a standard Gibbs sampler. The seco...
State Information in Bayesian Games
Cuff, Paul
2009-01-01
Two-player zero-sum repeated games are well understood. Computing the value of such a game is straightforward. Additionally, if the payoffs are dependent on a random state of the game known to one, both, or neither of the players, the resulting value of the game has been analyzed under the framework of Bayesian games. This investigation considers the optimal performance in a game when a helper is transmitting state information to one of the players. Encoding information for an adversarial setting (game) requires a different result than rate-distortion theory provides. Game theory has accentuated the importance of randomization (mixed strategy), which does not find a significant role in most communication modems and source coding codecs. Higher rates of communication, used in the right way, allow the message to include the necessary random component useful in games.
Cooperative extensions of the Bayesian game
Ichiishi, Tatsuro
2006-01-01
This is the very first comprehensive monograph in a burgeoning, new research area - the theory of cooperative game with incomplete information with emphasis on the solution concept of Bayesian incentive compatible strong equilibrium that encompasses the concept of the Bayesian incentive compatible core. Built upon the concepts and techniques in the classical static cooperative game theory and in the non-cooperative Bayesian game theory, the theory constructs and analyzes in part the powerful n -person game-theoretical model characterized by coordinated strategy-choice with individualistic ince
Bayesian models a statistical primer for ecologists
Hobbs, N Thompson
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili
Supra-Bayesian Combination of Probability Distributions
Czech Academy of Sciences Publication Activity Database
Sečkárová, Vladimíra
Veszprém : University of Pannonia, 2010, s. 112-117. ISBN 978-615-5044-00-7. [11th International PhD Workshop on Systems and Control. Veszprém (HU), 01.09.2010-03.09.2010] R&D Projects: GA ČR GA102/08/0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : Supra-Bayesian approach * sharing of probabilistic information * Bayesian decision making Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2010/AS/seckarova-supra-bayesian combination of probability distributions.pdf
Bayesian Soft Sensing in Cold Sheet Rolling
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Jirsa, Ladislav
Praha: ÚTIA AV ČR, v.v.i, 2010. s. 45-45. [6th International Workshop on Data–Algorithms–Decision Making. 2.12.2010-4.12.2010, Jindřichův Hradec] R&D Projects: GA MŠk(CZ) 7D09008 Institutional research plan: CEZ:AV0Z10750506 Keywords : soft sensor * bayesian statistics * bayesian model averaging Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2010/AS/dedecius-bayesian soft sensing in cold sheet rolling.pdf
The Diagnosis of Reciprocating Machinery by Bayesian Networks
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
A Bayesian Network is a reasoning tool based on probability theory and has many advantages that other reasoning tools do not have. This paper discusses the basic theory of Bayesian networks and studies the problems in constructing Bayesian networks. The paper also constructs a Bayesian diagnosis network of a reciprocating compressor. The example helps us to draw a conclusion that Bayesian diagnosis networks can diagnose reciprocating machinery effectively.
Bayesian Dose-Response Modeling in Sparse Data
Kim, Steven B.
This book discusses Bayesian dose-response modeling in small samples applied to two different settings. The first setting is early phase clinical trials, and the second setting is toxicology studies in cancer risk assessment. In early phase clinical trials, experimental units are humans who are actual patients. Prior to a clinical trial, opinions from multiple subject area experts are generally more informative than the opinion of a single expert, but we may face a dilemma when they have disagreeing prior opinions. In this regard, we consider compromising the disagreement and compare two different approaches for making a decision. In addition to combining multiple opinions, we also address balancing two levels of ethics in early phase clinical trials. The first level is individual-level ethics which reflects the perspective of trial participants. The second level is population-level ethics which reflects the perspective of future patients. We extensively compare two existing statistical methods which focus on each perspective and propose a new method which balances the two conflicting perspectives. In toxicology studies, experimental units are living animals. Here we focus on a potential non-monotonic dose-response relationship which is known as hormesis. Briefly, hormesis is a phenomenon which can be characterized by a beneficial effect at low doses and a harmful effect at high doses. In cancer risk assessments, the estimation of a parameter, which is known as a benchmark dose, can be highly sensitive to a class of assumptions, monotonicity or hormesis. In this regard, we propose a robust approach which considers both monotonicity and hormesis as a possibility. In addition, We discuss statistical hypothesis testing for hormesis and consider various experimental designs for detecting hormesis based on Bayesian decision theory. Past experiments have not been optimally designed for testing for hormesis, and some Bayesian optimal designs may not be optimal under a
Determining the Bayesian optimal sampling strategy in a hierarchical system.
Energy Technology Data Exchange (ETDEWEB)
Grace, Matthew D.; Ringland, James T.; Boggs, Paul T.; Pebay, Philippe Pierre
2010-09-01
Consider a classic hierarchy tree as a basic model of a 'system-of-systems' network, where each node represents a component system (which may itself consist of a set of sub-systems). For this general composite system, we present a technique for computing the optimal testing strategy, which is based on Bayesian decision analysis. In previous work, we developed a Bayesian approach for computing the distribution of the reliability of a system-of-systems structure that uses test data and prior information. This allows for the determination of both an estimate of the reliability and a quantification of confidence in the estimate. Improving the accuracy of the reliability estimate and increasing the corresponding confidence require the collection of additional data. However, testing all possible sub-systems may not be cost-effective, feasible, or even necessary to achieve an improvement in the reliability estimate. To address this sampling issue, we formulate a Bayesian methodology that systematically determines the optimal sampling strategy under specified constraints and costs that will maximally improve the reliability estimate of the composite system, e.g., by reducing the variance of the reliability distribution. This methodology involves calculating the 'Bayes risk of a decision rule' for each available sampling strategy, where risk quantifies the relative effect that each sampling strategy could have on the reliability estimate. A general numerical algorithm is developed and tested using an example multicomponent system. The results show that the procedure scales linearly with the number of components available for testing.
An Intuitive Dashboard for Bayesian Network Inference
Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.
2014-03-01
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.
A Bayesian approach to model uncertainty
International Nuclear Information System (INIS)
A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given
An Intuitive Dashboard for Bayesian Network Inference
International Nuclear Information System (INIS)
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++
Bayesian Control for Concentrating Mixed Nuclear Waste
Welch, Robert L.; Smith, Clayton
2013-01-01
A control algorithm for batch processing of mixed waste is proposed based on conditional Gaussian Bayesian networks. The network is compiled during batch staging for real-time response to sensor input.
Learning Bayesian networks for discrete data
Liang, Faming
2009-02-01
Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.
Bayesian Variable Selection in Spatial Autoregressive Models
Jesus Crespo Cuaresma; Philipp Piribauer
2015-01-01
This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. We present two alternative approaches which can be implemented using Gibbs sampling methods in a straightforward way and allow us to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. In a simulation study we show that the variable selection approaches tend to outperform existing Bayesian model averaging tech...
Bayesian Analysis of Multivariate Probit Models
Siddhartha Chib; Edward Greenberg
1996-01-01
This paper provides a unified simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods, and maximum likelihood estimates are obtained by a Markov chain Monte Carlo version of the E-M algorithm. Computation of Bayes factors from the simulation output is also considered. The methods are applied to a bivariate data set, to a 534-subject, four-year longitudinal dat...
Kernel Bayesian Inference with Posterior Regularization
Song, Yang; Jun ZHU; Ren, Yong
2016-01-01
We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equivalence provides a new understanding of kernel Bayesian inference. Moreover, the optimization problem induces a new regularization for the posterior embedding estimator, which is faster and has comparable performance to the squared regularization in kernel Bayes' rule. This regularization coincides with a former th...
Fitness inheritance in the Bayesian optimization algorithm
Pelikan, Martin; Sastry, Kumara
2004-01-01
This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems where fitness evaluation is expensive. Bayesian networks used in BOA to model promising solutions and generate the new ones are extended to allow not only for modeling and sampling candidate solutions...
Bayesian Network Models for Adaptive Testing
Czech Academy of Sciences Publication Activity Database
Plajner, Martin; Vomlel, Jiří
Achen: Sun SITE Central Europe, 2016 - (Agosta, J.; Carvalho, R.), s. 24-33. (CEUR Workshop Proceedings. Vol 1565). ISSN 1613-0073. [The Twelfth UAI Bayesian Modeling Applications Workshop (BMAW 2015). Amsterdam (NL), 16.07.2015] R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Bayesian networks * Computerized adaptive testing Subject RIV: JD - Computer Applications, Robotics http://library.utia.cas.cz/separaty/2016/MTR/plajner-0458062.pdf
Nomograms for Visualization of Naive Bayesian Classifier
Možina, Martin; Demšar, Janez; Michael W Kattan; Zupan, Blaz
2004-01-01
Besides good predictive performance, the naive Bayesian classifier can also offer a valuable insight into the structure of the training data and effects of the attributes on the class probabilities. This structure may be effectively revealed through visualization of the classifier. We propose a new way to visualize the naive Bayesian model in the form of a nomogram. The advantages of the proposed method are simplicity of presentation, clear display of the effects of individual attribute value...
Subjective Bayesian Analysis: Principles and Practice
Goldstein, Michael
2006-01-01
We address the position of subjectivism within Bayesian statistics. We argue, first, that the subjectivist Bayes approach is the only feasible method for tackling many important practical problems. Second, we describe the essential role of the subjectivist approach in scientific analysis. Third, we consider possible modifications to the Bayesian approach from a subjectivist viewpoint. Finally, we address the issue of pragmatism in implementing the subjectivist approach.
An Entropy Search Portfolio for Bayesian Optimization
Shahriari, Bobak; Wang, Ziyu; Hoffman, Matthew W.; Bouchard-Côté, Alexandre; De Freitas, Nando
2014-01-01
Bayesian optimization is a sample-efficient method for black-box global optimization. How- ever, the performance of a Bayesian optimization method very much depends on its exploration strategy, i.e. the choice of acquisition function, and it is not clear a priori which choice will result in superior performance. While portfolio methods provide an effective, principled way of combining a collection of acquisition functions, they are often based on measures of past performance which can be misl...
A Bayesian Framework for Active Artificial Perception
Ferreira, Joao; Lobo, Jorge; Bessiere, Pierre; Castelo-Branco, M; Dias, Jorge
2012-01-01
In this text, we present a Bayesian framework for active multimodal perception of 3D structure and motion. The design of this framework finds its inspiration in the role of the dorsal perceptual pathway of the human brain. Its composing models build upon a common egocentric spatial configuration that is naturally fitting for the integration of readings from multiple sensors using a Bayesian approach. In the process, we will contribute with efficient and robust probabilistic solutions for cycl...
Bayesian Classification in Medicine: The Transferability Question *
Zagoria, Ronald J.; Reggia, James A.; Price, Thomas R.; Banko, Maryann
1981-01-01
Using probabilities derived from a geographically distant patient population, we applied Bayesian classification to categorize stroke patients by etiology. Performance was assessed both by error rate and with a new linear accuracy coefficient. This approach to patient classification was found to be surprisingly accurate when compared to classification by two neurologists and to classification by the Bayesian method using “low cost” local and subjective probabilities. We conclude that for some...
Fuzzy Functional Dependencies and Bayesian Networks
Institute of Scientific and Technical Information of China (English)
LIU WeiYi(刘惟一); SONG Ning(宋宁)
2003-01-01
Bayesian networks have become a popular technique for representing and reasoning with probabilistic information. The fuzzy functional dependency is an important kind of data dependencies in relational databases with fuzzy values. The purpose of this paper is to set up a connection between these data dependencies and Bayesian networks. The connection is done through a set of methods that enable people to obtain the most information of independent conditions from fuzzy functional dependencies.
Evaluation System for a Bayesian Optimization Service
Dewancker, Ian; McCourt, Michael; Clark, Scott; Hayes, Patrick; Johnson, Alexandra; Ke, George
2016-01-01
Bayesian optimization is an elegant solution to the hyperparameter optimization problem in machine learning. Building a reliable and robust Bayesian optimization service requires careful testing methodology and sound statistical analysis. In this talk we will outline our development of an evaluation framework to rigorously test and measure the impact of changes to the SigOpt optimization service. We present an overview of our evaluation system and discuss how this framework empowers our resea...
Bayesian target tracking based on particle filter
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter (EKF) proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo (MCMC) method, the resampling step, etc novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.
Bayesian Models of Brain and Behaviour
Penny, William
2012-01-01
This paper presents a review of Bayesian models of brain and behaviour. We first review the basic principles of Bayesian inference. This is followed by descriptions of sampling and variational methods for approximate inference, and forward and backward recursions in time for inference in dynamical models. The review of behavioural models covers work in visual processing, sensory integration, sensorimotor integration, and collective decision making. The review of brain models covers a range of...
Bayesian Approach to Handling Informative Sampling
Sikov, Anna
2015-01-01
In the case of informative sampling the sampling scheme explicitly or implicitly depends on the response variable. As a result, the sample distribution of response variable can- not be used for making inference about the population. In this research I investigate the problem of informative sampling from the Bayesian perspective. Application of the Bayesian approach permits solving the problems, which arise due to complexity of the models, being used for handling informative sampling. The main...
Comparison of Bayesian-utilitarian and maximin principle approaches.
Comba, Pietro; Martuzzi, Marco; Botti, Caterina
2004-01-01
The Precautionary Principle implies the adoption of a set of rules aimed at avoiding possible future harm associated with suspected, but not ascertained, risk factors. Several philosophical, economical and societal questions are implied by precaution-based public health decision making. The purpose of the present paper is to specify the scope of the principle examining the notion of uncertainty involved, and the implication of different approaches to the decision-making process. The Bayesian-utilitarian approach and the approach based on the maximin principle will be considered, and the different meaning of prudence in the two settings will be discussed. In the Bayesian-utilitarian approach the small number of attributable cases will end up in a low average expected value, easily regarded as acceptable in a cost-benefit analysis. In a maximin approach, on the other hand, the issue will be to consider the high etiologic fraction of a rare disease in the highest category of exposure. In the light of the aforementioned cautions in interpretation, the core difference between the two approaches has to do with the choice between averaging knowledge or equitably distributing technological risks. PMID:15212224
Bayesian demography 250 years after Bayes.
Bijak, Jakub; Bryant, John
2016-01-01
Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms. PMID:26902889
Inverse problems in the Bayesian framework
International Nuclear Information System (INIS)
The history of Bayesian methods dates back to the original works of Reverend Thomas Bayes and Pierre-Simon Laplace: the former laid down some of the basic principles on inverse probability in his classic article ‘An essay towards solving a problem in the doctrine of chances’ that was read posthumously in the Royal Society in 1763. Laplace, on the other hand, in his ‘Memoirs on inverse probability’ of 1774 developed the idea of updating beliefs and wrote down the celebrated Bayes’ formula in the form we know today. Although not identified yet as a framework for investigating inverse problems, Laplace used the formalism very much in the spirit it is used today in the context of inverse problems, e.g., in his study of the distribution of comets. With the evolution of computational tools, Bayesian methods have become increasingly popular in all fields of human knowledge in which conclusions need to be drawn based on incomplete and noisy data. Needless to say, inverse problems, almost by definition, fall into this category. Systematic work for developing a Bayesian inverse problem framework can arguably be traced back to the 1980s, (the original first edition being published by Elsevier in 1987), although articles on Bayesian methodology applied to inverse problems, in particular in geophysics, had appeared much earlier. Today, as testified by the articles in this special issue, the Bayesian methodology as a framework for considering inverse problems has gained a lot of popularity, and it has integrated very successfully with many traditional inverse problems ideas and techniques, providing novel ways to interpret and implement traditional procedures in numerical analysis, computational statistics, signal analysis and data assimilation. The range of applications where the Bayesian framework has been fundamental goes from geophysics, engineering and imaging to astronomy, life sciences and economy, and continues to grow. There is no question that Bayesian
Directory of Open Access Journals (Sweden)
Edson Zangiacomi Martinez
2008-12-01
ámetros estimados. CONCLUSIONES: El modelo Bayesiano puede ser útil en estudios longitudinales con baja adhesión al seguimiento.OBJECTIVE: To develop a statistical model based on Bayesian methods to estimate the risk of tuberculosis infection in studies including individuals lost to follow-up, and to compare it with a classic deterministic model. METHODS: The proposed stochastic model is based on a Gibbs sampling algorithm that uses information of lost to follow-up at the end of a longitudinal study. For simulating the unknown number of reactors at the end of the study and lost to follow-up, but not reactors at time 0, a latent variable was introduced in the new model. An exercise of application of both models in the comparison of the estimates of interest was presented. RESULTS: The point estimates obtained from both models are near identical; however, the Bayesian model allowed the estimation of credible intervals as measures of precision of the estimated parameters. CONCLUSIONS: The Bayesian model can be valuable in longitudinal studies with low adherence to follow-up.
Bayesian Vision for Shape Recovery
Jalobeanu, Andre
2004-01-01
We present a new Bayesian vision technique that aims at recovering a shape from two or more noisy observations taken under similar lighting conditions. The shape is parametrized by a piecewise linear height field, textured by a piecewise linear irradiance field, and we assume Gaussian Markovian priors for both shape vertices and irradiance variables. The observation process. also known as rendering, is modeled by a non-affine projection (e.g. perspective projection) followed by a convolution with a piecewise linear point spread function. and contamination by additive Gaussian noise. We assume that the observation parameters are calibrated beforehand. The major novelty of the proposed method consists of marginalizing out the irradiances considered as nuisance parameters, which is achieved by Laplace approximations. This reduces the inference to minimizing an energy that only depends on the shape vertices, and therefore allows an efficient Iterated Conditional Mode (ICM) optimization scheme to be implemented. A Gaussian approximation of the posterior shape density is computed, thus providing estimates both the geometry and its uncertainty. We illustrate the effectiveness of the new method by shape reconstruction results in a 2D case. A 3D version is currently under development and aims at recovering a surface from multiple images, reconstructing the topography by marginalizing out both albedo and shading.
Bayesian analysis of cosmic structures
Kitaura, Francisco-Shu
2011-01-01
We revise the Bayesian inference steps required to analyse the cosmological large-scale structure. Here we make special emphasis in the complications which arise due to the non-Gaussian character of the galaxy and matter distribution. In particular we investigate the advantages and limitations of the Poisson-lognormal model and discuss how to extend this work. With the lognormal prior using the Hamiltonian sampling technique and on scales of about 4 h^{-1} Mpc we find that the over-dense regions are excellent reconstructed, however, under-dense regions (void statistics) are quantitatively poorly recovered. Contrary to the maximum a posteriori (MAP) solution which was shown to over-estimate the density in the under-dense regions we obtain lower densities than in N-body simulations. This is due to the fact that the MAP solution is conservative whereas the full posterior yields samples which are consistent with the prior statistics. The lognormal prior is not able to capture the full non-linear regime at scales ...
BAYESIAN APPROACH OF DECISION PROBLEMS
Directory of Open Access Journals (Sweden)
DRAGOŞ STUPARU
2010-01-01
Full Text Available Management is nowadays a basic vector of economic development, a concept frequently used in our country as well as all over the world. Indifferently of the hierarchical level at which the managerial process is manifested, decision represents its essential moment, the supreme act of managerial activity. Its can be met in all fields of activity, practically having an unlimited degree of coverage, and in all the functions of management. It is common knowledge that the activity of any type of manger, no matter the hierarchical level he occupies, represents a chain of interdependent decisions, their aim being the elimination or limitation of the influence of disturbing factors that may endanger the achievement of predetermined objectives, and the quality of managerial decisions condition the progress and viability of any enterprise. Therefore, one of the principal characteristics of a successful manager is his ability to adopt the most optimal decisions of high quality. The quality of managerial decisions are conditioned by the manager’s general level of education and specialization, the manner in which they are preoccupied to assimilate the latest information and innovations in the domain of management’s theory and practice and the applying of modern managerial methods and techniques in the activity of management. We are presenting below the analysis of decision problems in hazardous conditions in terms of Bayesian theory – a theory that uses the probabilistic calculus.
Bayesian analysis of volcanic eruptions
Ho, Chih-Hsiang
1990-10-01
The simple Poisson model generally gives a good fit to many volcanoes for volcanic eruption forecasting. Nonetheless, empirical evidence suggests that volcanic activity in successive equal time-periods tends to be more variable than a simple Poisson with constant eruptive rate. An alternative model is therefore examined in which eruptive rate(λ) for a given volcano or cluster(s) of volcanoes is described by a gamma distribution (prior) rather than treated as a constant value as in the assumptions of a simple Poisson model. Bayesian analysis is performed to link two distributions together to give the aggregate behavior of the volcanic activity. When the Poisson process is expanded to accomodate a gamma mixing distribution on λ, a consequence of this mixed (or compound) Poisson model is that the frequency distribution of eruptions in any given time-period of equal length follows the negative binomial distribution (NBD). Applications of the proposed model and comparisons between the generalized model and simple Poisson model are discussed based on the historical eruptive count data of volcanoes Mauna Loa (Hawaii) and Etna (Italy). Several relevant facts lead to the conclusion that the generalized model is preferable for practical use both in space and time.
An introduction to Gaussian Bayesian networks.
Grzegorczyk, Marco
2010-01-01
The extraction of regulatory networks and pathways from postgenomic data is important for drug -discovery and development, as the extracted pathways reveal how genes or proteins regulate each other. Following up on the seminal paper of Friedman et al. (J Comput Biol 7:601-620, 2000), Bayesian networks have been widely applied as a popular tool to this end in systems biology research. Their popularity stems from the tractability of the marginal likelihood of the network structure, which is a consistent scoring scheme in the Bayesian context. This score is based on an integration over the entire parameter space, for which highly expensive computational procedures have to be applied when using more complex -models based on differential equations; for example, see (Bioinformatics 24:833-839, 2008). This chapter gives an introduction to reverse engineering regulatory networks and pathways with Gaussian Bayesian networks, that is Bayesian networks with the probabilistic BGe scoring metric [see (Geiger and Heckerman 235-243, 1995)]. In the BGe model, the data are assumed to stem from a Gaussian distribution and a normal-Wishart prior is assigned to the unknown parameters. Gaussian Bayesian network methodology for analysing static observational, static interventional as well as dynamic (observational) time series data will be described in detail in this chapter. Finally, we apply these Bayesian network inference methods (1) to observational and interventional flow cytometry (protein) data from the well-known RAF pathway to evaluate the global network reconstruction accuracy of Bayesian network inference and (2) to dynamic gene expression time series data of nine circadian genes in Arabidopsis thaliana to reverse engineer the unknown regulatory network topology for this domain. PMID:20824469
Computationally efficient Bayesian inference for inverse problems.
Energy Technology Data Exchange (ETDEWEB)
Marzouk, Youssef M.; Najm, Habib N.; Rahn, Larry A.
2007-10-01
Bayesian statistics provides a foundation for inference from noisy and incomplete data, a natural mechanism for regularization in the form of prior information, and a quantitative assessment of uncertainty in the inferred results. Inverse problems - representing indirect estimation of model parameters, inputs, or structural components - can be fruitfully cast in this framework. Complex and computationally intensive forward models arising in physical applications, however, can render a Bayesian approach prohibitive. This difficulty is compounded by high-dimensional model spaces, as when the unknown is a spatiotemporal field. We present new algorithmic developments for Bayesian inference in this context, showing strong connections with the forward propagation of uncertainty. In particular, we introduce a stochastic spectral formulation that dramatically accelerates the Bayesian solution of inverse problems via rapid evaluation of a surrogate posterior. We also explore dimensionality reduction for the inference of spatiotemporal fields, using truncated spectral representations of Gaussian process priors. These new approaches are demonstrated on scalar transport problems arising in contaminant source inversion and in the inference of inhomogeneous material or transport properties. We also present a Bayesian framework for parameter estimation in stochastic models, where intrinsic stochasticity may be intermingled with observational noise. Evaluation of a likelihood function may not be analytically tractable in these cases, and thus several alternative Markov chain Monte Carlo (MCMC) schemes, operating on the product space of the observations and the parameters, are introduced.
Dimensionality reduction in Bayesian estimation algorithms
Directory of Open Access Journals (Sweden)
G. W. Petty
2013-03-01
Full Text Available An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm. For this dataset, algorithm performance is found to be poor owing to an irreconcilable conflict between the need to find matches in the dependent database versus the need to exclude inappropriate matches. It is argued that the likelihood of such conflicts increases sharply with the dimensionality of the observation space of real satellite sensors, which may utilize 9 to 13 channels to retrieve precipitation, for example. An objective method is described for distilling the relevant information content from N real channels into a much smaller number (M of pseudochannels while also regularizing the background (geophysical plus instrument noise component. The pseudochannels are linear combinations of the original N channels obtained via a two-stage principal component analysis of the dependent dataset. Bayesian retrievals based on a single pseudochannel applied to the independent dataset yield striking improvements in overall performance. The differences between the conventional Bayesian retrieval and reduced-dimensional Bayesian retrieval suggest that a major potential problem with conventional multichannel retrievals – whether Bayesian or not – lies in the common but often inappropriate assumption of diagonal error covariance. The dimensional reduction technique described herein avoids this problem by, in effect, recasting the retrieval problem in a coordinate system in which the desired covariance is lower-dimensional, diagonal, and unit magnitude.
Tactile length contraction as Bayesian inference.
Tong, Jonathan; Ngo, Vy; Goldreich, Daniel
2016-08-01
To perceive, the brain must interpret stimulus-evoked neural activity. This is challenging: The stochastic nature of the neural response renders its interpretation inherently uncertain. Perception would be optimized if the brain used Bayesian inference to interpret inputs in light of expectations derived from experience. Bayesian inference would improve perception on average but cause illusions when stimuli violate expectation. Intriguingly, tactile, auditory, and visual perception are all prone to length contraction illusions, characterized by the dramatic underestimation of the distance between punctate stimuli delivered in rapid succession; the origin of these illusions has been mysterious. We previously proposed that length contraction illusions occur because the brain interprets punctate stimulus sequences using Bayesian inference with a low-velocity expectation. A novel prediction of our Bayesian observer model is that length contraction should intensify if stimuli are made more difficult to localize. Here we report a tactile psychophysical study that tested this prediction. Twenty humans compared two distances on the forearm: a fixed reference distance defined by two taps with 1-s temporal separation and an adjustable comparison distance defined by two taps with temporal separation t ≤ 1 s. We observed significant length contraction: As t was decreased, participants perceived the two distances as equal only when the comparison distance was made progressively greater than the reference distance. Furthermore, the use of weaker taps significantly enhanced participants' length contraction. These findings confirm the model's predictions, supporting the view that the spatiotemporal percept is a best estimate resulting from a Bayesian inference process. PMID:27121574
Bayesian Methods for Medical Test Accuracy
Directory of Open Access Journals (Sweden)
Lyle D. Broemeling
2011-05-01
Full Text Available Bayesian methods for medical test accuracy are presented, beginning with the basic measures for tests with binary scores: true positive fraction, false positive fraction, positive predictive values, and negative predictive value. The Bayesian approach is taken because of its efficient use of prior information, and the analysis is executed with a Bayesian software package WinBUGS®. The ROC (receiver operating characteristic curve gives the intrinsic accuracy of medical tests that have ordinal or continuous scores, and the Bayesian approach is illustrated with many examples from cancer and other diseases. Medical tests include X-ray, mammography, ultrasound, computed tomography, magnetic resonance imaging, nuclear medicine and tests based on biomarkers, such as blood glucose values for diabetes. The presentation continues with more specialized methods suitable for measuring the accuracies of clinical studies that have verification bias, and medical tests without a gold standard. Lastly, the review is concluded with Bayesian methods for measuring the accuracy of the combination of two or more tests.
Bayesian tomographic reconstruction of microsystems
Salem, Sofia Fekih; Vabre, Alexandre; Mohammad-Djafari, Ali
2007-11-01
The microtomography by X ray transmission plays an increasingly dominating role in the study and the understanding of microsystems. Within this framework, an experimental setup of high resolution X ray microtomography was developed at CEA-List to quantify the physical parameters related to the fluids flow in microsystems. Several difficulties rise from the nature of experimental data collected on this setup: enhanced error measurements due to various physical phenomena occurring during the image formation (diffusion, beam hardening), and specificities of the setup (limited angle, partial view of the object, weak contrast). To reconstruct the object we must solve an inverse problem. This inverse problem is known to be ill-posed. It therefore needs to be regularized by introducing prior information. The main prior information we account for is that the object is composed of a finite known number of different materials distributed in compact regions. This a priori information is introduced via a Gauss-Markov field for the contrast distributions with a hidden Potts-Markov field for the class materials in the Bayesian estimation framework. The computations are done by using an appropriate Markov Chain Monte Carlo (MCMC) technique. In this paper, we present first the basic steps of the proposed algorithms. Then we focus on one of the main steps in any iterative reconstruction method which is the computation of forward and adjoint operators (projection and backprojection). A fast implementation of these two operators is crucial for the real application of the method. We give some details on the fast computation of these steps and show some preliminary results of simulations.
Directory of Open Access Journals (Sweden)
Abdullah G Al Otaibi
2012-01-01
Conclusion: Children with suspected microbial keratitis require comprehensive evaluation and management. Early recognition, identifying the predisposing factors and etiological microbial organisms, and instituting appropriate treatment measures have a crucial role in outcome. Ocular trauma was the leading cause of childhood microbial keratitis in our study.
A Large Sample Study of the Bayesian Bootstrap
Lo, Albert Y.
1987-01-01
An asymptotic justification of the Bayesian bootstrap is given. Large-sample Bayesian bootstrap probability intervals for the mean, the variance and bands for the distribution, the smoothed density and smoothed rate function are also provided.
Li, Hong
2015-01-01
The thesis first examines the choice of sample size for mortality forecasting, and then deal with the hedging of longevity risk using longevity-linked instruments. Chapter 2 proposes a Bayesian learning approach to determine the (posterior distribution of) the sample sizes for mortality forecasting
Bayesian Methods for Radiation Detection and Dosimetry
Groer, Peter G
2002-01-01
We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed comp...
BAMBI: blind accelerated multimodal Bayesian inference
Graff, Philip; Hobson, Michael P; Lasenby, Anthony
2011-01-01
In this paper we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks. The blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements the MultiNest package for nested sampling as well as the training of an artificial neural network (NN) to learn the likelihood function. In the case of computationally expensive likelihoods, this allows the substitution of a much more rapid approximation in order to increase significantly the speed of the analysis. We begin by demonstrating, with a few toy examples, the ability of a NN to learn complicated likelihood surfaces. BAMBI's ability to decrease running time for Bayesian inference is then demonstrated in the context of estimating cosmological parameters from WMAP and other observations. We show that valuable speed increases are achieved in addition to obtaining NNs trained on the likelihood functions for the different model and data combinations. These NNs can then be used for an...
Dynamic Bayesian Combination of Multiple Imperfect Classifiers
Simpson, Edwin; Psorakis, Ioannis; Smith, Arfon
2012-01-01
Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications. In many situations, such as when human decisions need to be combined, the base decisions can vary enormously in reliability. A Bayesian approach to such uncertain combination allows us to infer the differences in performance between individuals and to incorporate any available prior knowledge about their abilities when training data is sparse. In this paper we explore Bayesian classifier combination, using the computationally efficient framework of variational Bayesian inference. We apply the approach to real data from a large citizen science project, Galaxy Zoo Supernovae, and show that our method far outperforms other established approaches to imperfect decision combination. We go on to analyse the putative community structure of the decision makers, based on their inferred decision making strategies, and show that natural groupings are formed. Finally we present ...
Bayesian Inference Methods for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand
2013-01-01
inference algorithms based on the proposed prior representation for sparse channel estimation in orthogonal frequency-division multiplexing receivers. The inference algorithms, which are mainly obtained from variational Bayesian methods, exploit the underlying sparse structure of wireless channel responses......This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development of...... Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation of...
Bayesian Image Reconstruction Based on Voronoi Diagrams
Cabrera, G F; Hitschfeld, N
2007-01-01
We present a Bayesian Voronoi image reconstruction technique (VIR) for interferometric data. Bayesian analysis applied to the inverse problem allows us to derive the a-posteriori probability of a novel parameterization of interferometric images. We use a variable Voronoi diagram as our model in place of the usual fixed pixel grid. A quantization of the intensity field allows us to calculate the likelihood function and a-priori probabilities. The Voronoi image is optimized including the number of polygons as free parameters. We apply our algorithm to deconvolve simulated interferometric data. Residuals, restored images and chi^2 values are used to compare our reconstructions with fixed grid models. VIR has the advantage of modeling the image with few parameters, obtaining a better image from a Bayesian point of view.
Bayesian Fusion of Multi-Band Images
Wei, Qi; Tourneret, Jean-Yves
2013-01-01
In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical consideration is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo algorithm is designed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this high-dimension distribution, a Hamiltonian Monte Carlo step is introduced in the Gibbs sampling strategy. The efficiency of the proposed fusion method is evaluated with respect to several state-of-the-art fusion techniques. In particular, low spatial resolution hyperspectral and mult...
A Bayesian Semiparametric Model for Radiation Dose-Response Estimation.
Furukawa, Kyoji; Misumi, Munechika; Cologne, John B; Cullings, Harry M
2016-06-01
In evaluating the risk of exposure to health hazards, characterizing the dose-response relationship and estimating acceptable exposure levels are the primary goals. In analyses of health risks associated with exposure to ionizing radiation, while there is a clear agreement that moderate to high radiation doses cause harmful effects in humans, little has been known about the possible biological effects at low doses, for example, below 0.1 Gy, which is the dose range relevant to most radiation exposures of concern today. A conventional approach to radiation dose-response estimation based on simple parametric forms, such as the linear nonthreshold model, can be misleading in evaluating the risk and, in particular, its uncertainty at low doses. As an alternative approach, we consider a Bayesian semiparametric model that has a connected piece-wise-linear dose-response function with prior distributions having an autoregressive structure among the random slope coefficients defined over closely spaced dose categories. With a simulation study and application to analysis of cancer incidence data among Japanese atomic bomb survivors, we show that this approach can produce smooth and flexible dose-response estimation while reasonably handling the risk uncertainty at low doses and elsewhere. With relatively few assumptions and modeling options to be made by the analyst, the method can be particularly useful in assessing risks associated with low-dose radiation exposures. PMID:26581473
Comparison of the Bayesian and Frequentist Approach to the Statistics
Hakala, Michal
2015-01-01
The Thesis deals with introduction to Bayesian statistics and comparing Bayesian approach with frequentist approach to statistics. Bayesian statistics is modern branch of statistics which provides an alternative comprehensive theory to the frequentist approach. Bayesian concepts provides solution for problems not being solvable by frequentist theory. In the thesis are compared definitions, concepts and quality of statistical inference. The main interest is focused on a point estimation, an in...
Revisiting k-means: New Algorithms via Bayesian Nonparametrics
Kulis, Brian; Jordan, Michael I.
2011-01-01
Bayesian models offer great flexibility for clustering applications---Bayesian nonparametrics can be used for modeling infinite mixtures, and hierarchical Bayesian models can be utilized for sharing clusters across multiple data sets. For the most part, such flexibility is lacking in classical clustering methods such as k-means. In this paper, we revisit the k-means clustering algorithm from a Bayesian nonparametric viewpoint. Inspired by the asymptotic connection between k-means and mixtures...
Mining data from hemodynamic simulations via Bayesian emulation
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Nair Prasanth B
2007-12-01
Full Text Available Abstract Background: Arterial geometry variability is inevitable both within and across individuals. To ensure realistic prediction of cardiovascular flows, there is a need for efficient numerical methods that can systematically account for geometric uncertainty. Methods and results: A statistical framework based on Bayesian Gaussian process modeling was proposed for mining data generated from computer simulations. The proposed approach was applied to analyze the influence of geometric parameters on hemodynamics in the human carotid artery bifurcation. A parametric model in conjunction with a design of computer experiments strategy was used for generating a set of observational data that contains the maximum wall shear stress values for a range of probable arterial geometries. The dataset was mined via a Bayesian Gaussian process emulator to estimate: (a the influence of key parameters on the output via sensitivity analysis, (b uncertainty in output as a function of uncertainty in input, and (c which settings of the input parameters result in maximum and minimum values of the output. Finally, potential diagnostic indicators were proposed that can be used to aid the assessment of stroke risk for a given patient's geometry.
Bayesian analysis of physiologically based toxicokinetic and toxicodynamic models.
Hack, C Eric
2006-04-17
Physiologically based toxicokinetic (PBTK) and toxicodynamic (TD) models of bromate in animals and humans would improve our ability to accurately estimate the toxic doses in humans based on available animal studies. These mathematical models are often highly parameterized and must be calibrated in order for the model predictions of internal dose to adequately fit the experimentally measured doses. Highly parameterized models are difficult to calibrate and it is difficult to obtain accurate estimates of uncertainty or variability in model parameters with commonly used frequentist calibration methods, such as maximum likelihood estimation (MLE) or least squared error approaches. The Bayesian approach called Markov chain Monte Carlo (MCMC) analysis can be used to successfully calibrate these complex models. Prior knowledge about the biological system and associated model parameters is easily incorporated in this approach in the form of prior parameter distributions, and the distributions are refined or updated using experimental data to generate posterior distributions of parameter estimates. The goal of this paper is to give the non-mathematician a brief description of the Bayesian approach and Markov chain Monte Carlo analysis, how this technique is used in risk assessment, and the issues associated with this approach. PMID:16466842
Bayesian joint modeling of longitudinal and spatial survival AIDS data.
Martins, Rui; Silva, Giovani L; Andreozzi, Valeska
2016-08-30
Joint analysis of longitudinal and survival data has received increasing attention in the recent years, especially for analyzing cancer and AIDS data. As both repeated measurements (longitudinal) and time-to-event (survival) outcomes are observed in an individual, a joint modeling is more appropriate because it takes into account the dependence between the two types of responses, which are often analyzed separately. We propose a Bayesian hierarchical model for jointly modeling longitudinal and survival data considering functional time and spatial frailty effects, respectively. That is, the proposed model deals with non-linear longitudinal effects and spatial survival effects accounting for the unobserved heterogeneity among individuals living in the same region. This joint approach is applied to a cohort study of patients with HIV/AIDS in Brazil during the years 2002-2006. Our Bayesian joint model presents considerable improvements in the estimation of survival times of the Brazilian HIV/AIDS patients when compared with those obtained through a separate survival model and shows that the spatial risk of death is the same across the different Brazilian states. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26990773
Bayesian Estimation of Combined Accuracy for Tests with Verification Bias
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Lyle D. Broemeling
2011-12-01
Full Text Available This presentation will emphasize the estimation of the combined accuracy of two or more tests when verification bias is present. Verification bias occurs when some of the subjects are not subject to the gold standard. The approach is Bayesian where the estimation of test accuracy is based on the posterior distribution of the relevant parameter. Accuracy of two combined binary tests is estimated employing either “believe the positive” or “believe the negative” rule, then the true and false positive fractions for each rule are computed for two tests. In order to perform the analysis, the missing at random assumption is imposed, and an interesting example is provided by estimating the combined accuracy of CT and MRI to diagnose lung cancer. The Bayesian approach is extended to two ordinal tests when verification bias is present, and the accuracy of the combined tests is based on the ROC area of the risk function. An example involving mammography with two readers with extreme verification bias illustrates the estimation of the combined test accuracy for ordinal tests.
An Improved Algorithm of Bayesian Text Categorization
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Tao Dong
2011-08-01
Full Text Available Text categorization is a fundamental methodology of text mining and a hot topic of the research of data mining and web mining in recent years. It plays an important role in building traditional information retrieval, web indexing architecture, Web information retrieval, and so on. This paper presents an improved algorithm of text categorization that combines the feature weighting technique with Naïve Bayesian classifier. Experimental results show that using the improved Gini index algorithm to feature weight can improve the performance of Naïve Bayesian classifier effectively. This algorithm obtains good application in the sensitive information recognition system.
Bayesian Optimisation Algorithm for Nurse Scheduling
Li, Jingpeng
2008-01-01
Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA) for the nurse scheduling problem that chooses such suitable scheduling rules from a set for each nurses assignment. Based on the idea of using probabilistic models, the BOA builds a Bayesian network for the set of promising solutions and samples these networks to generate new candidate solutions. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed algorithm may be suitable for other scheduling problems.
Bayesian estimation and tracking a practical guide
Haug, Anton J
2012-01-01
A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation
Cross-Site Soil Microbial Communities under Tillage Regimes: Fungistasis and Microbial Biomarkers
Sipilä, Timo P.; Yrjälä, Kim; Alakukku, Laura; Palojärvi, Ansa
2012-01-01
The exploitation of soil ecosystem services by agricultural management strategies requires knowledge of microbial communities in different management regimes. Crop cover by no-till management protects the soil surface, reducing the risk of erosion and nutrient leaching, but might increase straw residue-borne and soilborne plant-pathogenic fungi. A cross-site study of soil microbial communities and Fusarium fungistasis was conducted on six long-term agricultural fields with no-till and moldboa...
Bayesian Just-So Stories in Psychology and Neuroscience
Bowers, Jeffrey S.; Davis, Colin J.
2012-01-01
According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak.…
A Gentle Introduction to Bayesian Analysis : Applications to Developmental Research
Van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A G
2014-01-01
Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, t
A SAS Interface for Bayesian Analysis with WinBUGS
Zhang, Zhiyong; McArdle, John J.; Wang, Lijuan; Hamagami, Fumiaki
2008-01-01
Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model…
A Fast Iterative Bayesian Inference Algorithm for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand; Manchón, Carles Navarro; Fleury, Bernard Henri
2013-01-01
representation of the Bessel K probability density function; a highly efficient, fast iterative Bayesian inference method is then applied to the proposed model. The resulting estimator outperforms other state-of-the-art Bayesian and non-Bayesian estimators, either by yielding lower mean squared estimation error...
Terrorism risk concern in Europe
Drakos, Konstantinos; Müller, Cathérine
2011-01-01
We explore whether differences of terrorism risk perception across all European countries reflect their underlying differences in terrorism risk, which we decompose into a long term and innovation component. We employ longitudinal country-level data on terrorism risk concern and our modeling approach is motivated by the Bayesian framework. We conclude that the observed risk perception variation is significantly explained by the long term terrorism countries face, while the cyclical part of te...
Improving Local and Regional Flood Quantile Estimates Using a Hierarchical Bayesian GEV Model
Ribeiro Lima, C. H.; Lall, U.; Devineni, N.; Troy, T.
2013-12-01
Flood risk management usually relies on local and regional flood frequency analysis, which tends to suffer from lack of data and parameter uncertainties. Here we estimate local and regional Generalized Extreme Value (GEV) distribution parameters in a hierarchical Bayesian framework, which helps reduce uncertainties by pooling more information in the estimation process and provides a simple topology to propagate model and parameter uncertainties to flood quantile estimates. As prior information for the Bayesian model, it is assumed for each site that the GEV location and scale parameters come from independent log-normal distributions, whose mean parameter follows the well known log-log scaling law with the drainage area. The shape parameter for each site is shrunk towards a common mean. Non-informative prior distributions are assumed for the hyperparameters and the MCMC method is used to sample from the posterior distributions. The model is tested using annual maximum series from 20 streamflow gauges located in an 83.000 km2 basin in southeastern Brazil. The results show a significant improvement of flood quantile estimates over the traditional GEV model, particularly for sites with few data. For return periods within the range of the data (around 50 years), the Bayesian credible intervals for the flood quantiles are narrower than the classical confidence limits based on the delta method. As the return period increases beyond the range of the data, the confidence limits from the delta method become unreliable and the Bayesian credible intervals provide a way to estimate satisfactory confidence bands for the flood quantiles considering the parameter uncertainties. In order to evaluate the applicability of the proposed hierarchical Bayesian model for flood frequency regional analysis, we estimate flood quantiles for three randomly chosen out-of-sample sites and compare with classical estimates using the index flood method. The posterior distributions of the scaling
Jones, Matt; Love, Bradley C
2011-08-01
The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls
Integer variables estimation problems: the Bayesian approach
Directory of Open Access Journals (Sweden)
G. Venuti
1997-06-01
Full Text Available In geodesy as well as in geophysics there are a number of examples where the unknown parameters are partly constrained to be integer numbers, while other parameters have a continuous range of possible values. In all such situations the ordinary least square principle, with integer variates fixed to the most probable integer value, can lead to paradoxical results, due to the strong non-linearity of the manifold of admissible values. On the contrary an overall estimation procedure assigning the posterior distribution to all variables, discrete and continuous, conditional to the observed quantities, like the so-called Bayesian approach, has the advantage of weighting correctly the possible errors in choosing different sets of integer values, thus providing a more realistic and stable estimate even of the continuous parameters. In this paper, after a short recall of the basics of Bayesian theory in section 2, we present the natural Bayesian solution to the problem of assessing the estimable signal from noisy observations in section 3 and the Bayesian solution to cycle slips detection and repair for a stream of GPS measurements in section 4. An elementary synthetic example is discussed in section 3 to illustrate the theory presented and more elaborate, though synthetic, examples are discussed in section 4 where realistic streams of GPS observations, with cycle slips, are simulated and then back processed.
Von Neumann was not a Quantum Bayesian.
Stacey, Blake C
2016-05-28
Wikipedia has claimed for over 3 years now that John von Neumann was the 'first quantum Bayesian'. In context, this reads as stating that von Neumann inaugurated QBism, the approach to quantum theory promoted by Fuchs, Mermin and Schack. This essay explores how such a claim is, historically speaking, unsupported. PMID:27091166
Von Neumann Was Not a Quantum Bayesian
Blake C. Stacey
2014-01-01
Wikipedia has claimed for over three years now that John von Neumann was the "first quantum Bayesian." In context, this reads as stating that von Neumann inaugurated QBism, the approach to quantum theory promoted by Fuchs, Mermin and Schack. This essay explores how such a claim is, historically speaking, unsupported.
A Bayesian Approach to Interactive Retrieval
Tague, Jean M.
1973-01-01
A probabilistic model for interactive retrieval is presented. Bayesian statistical decision theory principles are applied: use of prior and sample information about the relationship of document descriptions to query relevance; maximization of expected value of a utility function, to the problem of optimally restructuring search strategies in an…
Bayesian Averaging is Well-Temperated
DEFF Research Database (Denmark)
Hansen, Lars Kai
2000-01-01
Bayesian predictions are stochastic just like predictions of any other inference scheme that generalize from a finite sample. While a simple variational argument shows that Bayes averaging is generalization optimal given that the prior matches the teacher parameter distribution the situation is l...
Perfect Bayesian equilibrium. Part II: epistemic foundations
Bonanno, Giacomo
2011-01-01
In a companion paper we introduced a general notion of perfect Bayesian equilibrium which can be applied to arbitrary extensive-form games. The essential ingredient of the proposed definition is the qualitative notion of AGM-consistency. In this paper we provide an epistemic foundation for AGM-consistency based on the AGM theory of belief revision.
Explanation mode for Bayesian automatic object recognition
Hazlett, Thomas L.; Cofer, Rufus H.; Brown, Harold K.
1992-09-01
One of the more useful techniques to emerge from AI is the provision of an explanation modality used by the researcher to understand and subsequently tune the reasoning of an expert system. Such a capability, missing in the arena of statistical object recognition, is not that difficult to provide. Long standing results show that the paradigm of Bayesian object recognition is truly optimal in a minimum probability of error sense. To a large degree, the Bayesian paradigm achieves optimality through adroit fusion of a wide range of lower informational data sources to give a higher quality decision--a very 'expert system' like capability. When various sources of incoming data are represented by C++ classes, it becomes possible to automatically backtrack the Bayesian data fusion process, assigning relative weights to the more significant datums and their combinations. A C++ object oriented engine is then able to synthesize 'English' like textural description of the Bayesian reasoning suitable for generalized presentation. Key concepts and examples are provided based on an actual object recognition problem.
Von Neumann Was Not a Quantum Bayesian
Stacey, Blake C
2014-01-01
Wikipedia has claimed for over two years now that John von Neumann was the "first quantum Bayesian." In context, this reads as stating that von Neumann inaugurated QBism, the approach to quantum theory promoted by Fuchs, Mermin and Schack. This essay explores how such a claim is, historically speaking, unsupported.
Scaling Bayesian network discovery through incremental recovery
Castelo, J.R.; Siebes, A.P.J.M.
1999-01-01
Bayesian networks are a type of graphical models that, e.g., allow one to analyze the interaction among the variables in a database. A well-known problem with the discovery of such models from a database is the ``problem of high-dimensionality''. That is, the discovery of a network from a database w
On Bayesian Nonparametric Continuous Time Series Models
Karabatsos, George; Walker, Stephen G.
2013-01-01
This paper is a note on the use of Bayesian nonparametric mixture models for continuous time series. We identify a key requirement for such models, and then establish that there is a single type of model which meets this requirement. As it turns out, the model is well known in multiple change-point problems.
Bayesian semiparametric dynamic Nelson-Siegel model
C. Cakmakli
2011-01-01
This paper proposes the Bayesian semiparametric dynamic Nelson-Siegel model where the density of the yield curve factors and thereby the density of the yields are estimated along with other model parameters. This is accomplished by modeling the error distributions of the factors according to a Diric
A Bayesian Bootstrap for a Finite Population
Lo, Albert Y.
1988-01-01
A Bayesian bootstrap for a finite population is introduced; its small-sample distributional properties are discussed and compared with those of the frequentist bootstrap for a finite population. It is also shown that the two are first-order asymptotically equivalent.
Bayesian analysis of Markov point processes
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
2006-01-01
Recently Møller, Pettitt, Berthelsen and Reeves introduced a new MCMC methodology for drawing samples from a posterior distribution when the likelihood function is only specified up to a normalising constant. We illustrate the method in the setting of Bayesian inference for Markov point processes...
Bayesian calibration of car-following models
Van Hinsbergen, C.P.IJ.; Van Lint, H.W.C.; Hoogendoorn, S.P.; Van Zuylen, H.J.
2010-01-01
Recent research has revealed that there exist large inter-driver differences in car-following behavior such that different car-following models may apply to different drivers. This study applies Bayesian techniques to the calibration of car-following models, where prior distributions on each model p
Inverse Problems in a Bayesian Setting
Matthies, Hermann G.
2016-02-13
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.
Optimized Bayesian dynamic advising theory and algorithms
Karny, Miroslav
2006-01-01
Written by one of the world''s leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. It is accompanied by a CD that contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the important areas.
Bayesian Estimation of Thermonuclear Reaction Rates
Iliadis, Christian; Coc, Alain; Timmes, Frank; Starrfield, Sumner
2016-01-01
The problem of estimating non-resonant astrophysical S-factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied in the past to this problem, all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extra-solar planets, gravitational waves, and type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present the first astrophysical S-factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the d(p,$\\gamma$)$^3$He, $^3$He($^3$He,2p)$^4$He, and $^3$He($\\alpha$,$\\gamma$)$^7$Be reactions,...
An Approximate Bayesian Fundamental Frequency Estimator
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
Joint fundamental frequency and model order estimation is an important problem in several applications such as speech and music processing. In this paper, we develop an approximate estimation algorithm of these quantities using Bayesian inference. The inference about the fundamental frequency and...
Basics of Bayesian Learning - Basically Bayes
DEFF Research Database (Denmark)
Larsen, Jan
Tutorial presented at the IEEE Machine Learning for Signal Processing Workshop 2006, Maynooth, Ireland, September 8, 2006. The tutorial focuses on the basic elements of Bayesian learning and its relation to classical learning paradigms. This includes a critical discussion of the pros and cons. The...
Sensitivity to Sampling in Bayesian Word Learning
Xu, Fei; Tenenbaum, Joshua B.
2007-01-01
We report a new study testing our proposal that word learning may be best explained as an approximate form of Bayesian inference (Xu & Tenenbaum, in press). Children are capable of learning word meanings across a wide range of communicative contexts. In different contexts, learners may encounter different sampling processes generating the examples…
LI Hong
2015-01-01
The thesis first examines the choice of sample size for mortality forecasting, and then deal with the hedging of longevity risk using longevity-linked instruments. Chapter 2 proposes a Bayesian learning approach to determine the (posterior distribution of) the sample sizes for mortality forecasting using mortality models based on linear extrapolation approaches. Chapter 3 studies the static robust management of longevity risk in the situation that the hedger does not have precise knowledge of...
CEO Emotional Intelligence and Firms’ Financial Policies. Bayesian Network Method
Directory of Open Access Journals (Sweden)
Mohamed Ali Azouzi
2014-03-01
Full Text Available The aim of this paper is to explore the determinants of firms’ financial policies according to the manager’s psychological characteristics. More specifically, it examines the links between emotional intelligence, decision biases and the effectiveness of firms’ financial policies. The article finds that the main cause of an organization’s problems is the CEO’s emotional intelligence level. We introduce an approach based on Bayesian network techniques with a series of semi-directive interviews. The research paper represents an original approach because it characterizes behavioral corporate policy choices in emerging markets. To the best of our knowledge, this is the first study in the Tunisian context to explore this area of research. Our results show that Tunisian leaders adjust their decisions (on investments and distributions to minimize the risk of loss of compensation or reputation. They opt for decisions that minimize agency costs, transaction costs, and cognitive costs.
Safety Analysis of Liquid Rocket Engine Using Bayesian Networks
Institute of Scientific and Technical Information of China (English)
WANG Hua-wei; YAN Zhi-qiang
2007-01-01
Safety analysis for liquid rocket engine has a great meaning for shortening development cycle, saving development expenditure and reducing development risk. The relationship between the structure and component of liquid rocket engine is much more complex, furthermore test data are absent in development phase. Thereby, the uncertainties exist in safety analysis for liquid rocket engine. A safety analysis model integrated with FMEA(failure mode and effect analysis)based on Bayesian networks (BN) is brought forward for liquid rocket engine, which can combine qualitative analysis with quantitative decision. The method has the advantages of fusing multi-information, saving sample amount and having high veracity. An example shows that the method is efficient.
Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data
Schetinin, Vitaly; Fieldsend, Jonathan E.; Partridge, Derek; Krzanowski, Wojtek J.; Everson, Richard M.; Bailey, Trevor C; Hernandez, Adolfo
2005-01-01
Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in applications such as financial in which risks have to be estimated. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the diversity of a classifier ensemble and the required performance. The interpretability of classification models can also give useful in...
Growth of Screen-Detected Abdominal Aortic Aneurysms in Men: A Bayesian Analysis
Sherer, E A; Bies, R R; Clancy, P; Norman, P. E.; Golledge, J
2012-01-01
There is considerable interindividual variability in the growth of abdominal aortic aneurysms (AAAs), but an individual's growth observations, risk factors, and biomarkers could potentially be used to tailor surveillance. To assess the potential for tailoring surveillance, this study determined the accuracy of individualized predictions of AAA size at the next surveillance observation. A hierarchical Bayesian model was fitted to a total of 1,732 serial ultrasound measurements from 299 men in ...
Determinants of Low Birth Weight in Malawi: Bayesian Geo-Additive Modelling
Ngwira, Alfred; Stanley, Christopher C.
2015-01-01
Studies on factors of low birth weight in Malawi have neglected the flexible approach of using smooth functions for some covariates in models. Such flexible approach reveals detailed relationship of covariates with the response. The study aimed at investigating risk factors of low birth weight in Malawi by assuming a flexible approach for continuous covariates and geographical random effect. A Bayesian geo-additive model for birth weight in kilograms and size of the child at birth (less than ...
An examination of disparities in cancer incidence in Texas using Bayesian random coefficient models
Sparks, Corey
2015-01-01
Disparities in cancer risk exist between ethnic groups in the United States. These disparities often result from differential access to healthcare, differences in socioeconomic status and differential exposure to carcinogens. This study uses cancer incidence data from the population based Texas Cancer Registry to investigate the disparities in digestive and respiratory cancers from 2000 to 2008. A Bayesian hierarchical regression approach is used. All models are fit using the INLA method of B...
A Bayesian spatio-temporal analysis of forest fires in Portugal
Silva, Giovani Loiola; Dias, Maria Inês
2013-01-01
In the last decade, forest fires have become a natural disaster in Portugal, causing great forest devastation, leading to both economic and environmental losses and putting at risk populations and the livelihoods of the forest itself. In this work, we present Bayesian hierarchical models to analyze spatio-temporal fire data on the proportion of burned area in Portugal, by municipalities and over three decades. Mixture of distributions was employed to model jointly the proportion of area burn...
Thakur, Raman; Shankar, Jata
2016-01-01
Pentraxin-3 (PTX-3) protein is an evolutionary conserved protein that acts as a soluble pattern-recognition receptor for pathogens and plays important role in innate immune response. It recognizes various pathogens by interacting with extracellular moieties such as glactomannan of conidia (Aspergillus fumigatus), lipopolysaccharide of Pseudomonas aeruginosa, Streptococcus pneumonia and Salmonella typhimurium. Thus, PTX-3 protein helps to clear these pathogens by activating downstream innate immune process. In this study, computational methods were used to analyze various non-synonymous single nucleotide polymorphisms (nsSNPs) in PTX-3 gene. Three different databases were used to retrieve SNP data sets followed by seven different in silico algorithms to screen nsSNPs in PTX-3 gene. Sequence homology based approach was used to identify nsSNPs. Conservation profile of PTX-3 protein amino acid residues were predicted by ConSurf web server. In total, 10 high-risk nsSNPs were identified in pentraxin-domain of PTX-3 gene. Out of these 10 high-risk nsSNPs, 4 were present in the conserved structural and functional residues of the pentraxin-domain, hence, selected for structural analyses. The results showed alteration in the putative structure of pentraxin-domain. Prediction of protein-protein interactions analysis showed association of PTX-3 protein with C1q component of complement pathway. Different functional and structural residues along with various putative phosphorylation sites and evolutionary relationship were also predicted for PTX-3 protein. This is the first extensive computational analyses of pentraxin protein family with nsSNPs and will serve as a valuable resource for future population based studies. PMID:26941719
A tutorial on Bayesian Normal linear regression
Klauenberg, Katy; Wübbeler, Gerd; Mickan, Bodo; Harris, Peter; Elster, Clemens
2015-12-01
Regression is a common task in metrology and often applied to calibrate instruments, evaluate inter-laboratory comparisons or determine fundamental constants, for example. Yet, a regression model cannot be uniquely formulated as a measurement function, and consequently the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements are not applicable directly. Bayesian inference, however, is well suited to regression tasks, and has the advantage of accounting for additional a priori information, which typically robustifies analyses. Furthermore, it is anticipated that future revisions of the GUM shall also embrace the Bayesian view. Guidance on Bayesian inference for regression tasks is largely lacking in metrology. For linear regression models with Gaussian measurement errors this tutorial gives explicit guidance. Divided into three steps, the tutorial first illustrates how a priori knowledge, which is available from previous experiments, can be translated into prior distributions from a specific class. These prior distributions have the advantage of yielding analytical, closed form results, thus avoiding the need to apply numerical methods such as Markov Chain Monte Carlo. Secondly, formulas for the posterior results are given, explained and illustrated, and software implementations are provided. In the third step, Bayesian tools are used to assess the assumptions behind the suggested approach. These three steps (prior elicitation, posterior calculation, and robustness to prior uncertainty and model adequacy) are critical to Bayesian inference. The general guidance given here for Normal linear regression tasks is accompanied by a simple, but real-world, metrological example. The calibration of a flow device serves as a running example and illustrates the three steps. It is shown that prior knowledge from previous calibrations of the same sonic nozzle enables robust predictions even for extrapolations.
Universal Darwinism as a process of Bayesian inference
Campbell, John O
2016-01-01
Many of the mathematical frameworks describing natural selection are equivalent to Bayes Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment". Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description clo...
International Nuclear Information System (INIS)
Microbial fuel cells (MFC) are a promising technology for sustainable production of alternative energy and waste treatment. A microbial fuel cell transformation chemical energy in the chemical bonds in organic compounds to electrical energy through catalytic reactions of microorganisms under anaerobic conditions. It has been known for many years that it is possible to generate electricity directly by using bacteria to break down organic substrates. Key words: microbial fuel cells (MFC), biosensor, wastewater treatment