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
Framework for microbial food-safety risk assessments amenable to Bayesian modeling.
Williams, Michael S; Ebel, Eric D; Vose, David
2011-04-01
Regulatory agencies often perform microbial risk assessments to evaluate the change in the number of human illnesses as the result of a new policy that reduces the level of contamination in the food supply. These agencies generally have regulatory authority over the production and retail sectors of the farm-to-table continuum. Any predicted change in contamination that results from new policy that regulates production practices occurs many steps prior to consumption of the product. This study proposes a framework for conducting microbial food-safety risk assessments; this framework can be used to quantitatively assess the annual effects of national regulatory policies. Advantages of the framework are that estimates of human illnesses are consistent with national disease surveillance data (which are usually summarized on an annual basis) and some of the modeling steps that occur between production and consumption can be collapsed or eliminated. The framework leads to probabilistic models that include uncertainty and variability in critical input parameters; these models can be solved using a number of different Bayesian methods. The Bayesian synthesis method performs well for this application and generates posterior distributions of parameters that are relevant to assessing the effect of implementing a new policy. An example, based on Campylobacter and chicken, estimates the annual number of illnesses avoided by a hypothetical policy; this output could be used to assess the economic benefits of a new policy. Empirical validation of the policy effect is also examined by estimating the annual change in the numbers of illnesses observed via disease surveillance systems.
Schmidt, P J; Pintar, K D M; Fazil, A M; Flemming, C A; Lanthier, M; Laprade, N; Sunohara, M D; Simhon, A; Thomas, J L; Topp, E; Wilkes, G; Lapen, D R
2013-06-15
Human campylobacteriosis is the leading bacterial gastrointestinal illness in Canada; environmental transmission has been implicated in addition to transmission via consumption of contaminated food. Information about Campylobacter spp. occurrence at the watershed scale will enhance our understanding of the associated public health risks and the efficacy of source water protection strategies. The overriding purpose of this study is to provide a quantitative framework to assess and compare the relative public health significance of watershed microbial water quality associated with agricultural BMPs. A microbial monitoring program was expanded from fecal indicator analyses and Campylobacter spp. presence/absence tests to the development of a novel, 11-tube most probable number (MPN) method that targeted Campylobacter jejuni, Campylobacter coli, and Campylobacter lari. These three types of data were used to make inferences about theoretical risks in a watershed in which controlled tile drainage is widely practiced, an adjacent watershed with conventional (uncontrolled) tile drainage, and reference sites elsewhere in the same river basin. E. coli concentrations (MPN and plate count) in the controlled tile drainage watershed were statistically higher (2008-11), relative to the uncontrolled tile drainage watershed, but yearly variation was high as well. Escherichia coli loading for years 2008-11 combined were statistically higher in the controlled watershed, relative to the uncontrolled tile drainage watershed, but Campylobacter spp. loads for 2010-11 were generally higher for the uncontrolled tile drainage watershed (but not statistically significant). Using MPN data and a Bayesian modelling approach, higher mean Campylobacter spp. concentrations were found in the controlled tile drainage watershed relative to the uncontrolled tile drainage watershed (2010, 2011). A second-order quantitative microbial risk assessment (QMRA) was used, in a relative way, to identify
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 modelling of geostatistical malaria risk data
Directory of Open Access Journals (Sweden)
L. Gosoniu
2006-11-01
Full Text Available Bayesian geostatistical models applied to malaria risk data quantify the environment-disease relations, identify significant environmental predictors of malaria transmission and provide model-based predictions of malaria risk together with their precision. These models are often based on the stationarity assumption which implies that spatial correlation is a function of distance between locations and independent of location. We relax this assumption and analyse malaria survey data in Mali using a Bayesian non-stationary model. Model fit and predictions are based on Markov chain Monte Carlo simulation methods. Model validation compares the predictive ability of the non-stationary model with the stationary analogue. Results indicate that the stationarity assumption is important because it influences the significance of environmental factors and the corresponding malaria risk maps.
Bayesian modelling of geostatistical malaria risk data.
Gosoniu, L; Vounatsou, P; Sogoba, N; Smith, T
2006-11-01
Bayesian geostatistical models applied to malaria risk data quantify the environment-disease relations, identify significant environmental predictors of malaria transmission and provide model-based predictions of malaria risk together with their precision. These models are often based on the stationarity assumption which implies that spatial correlation is a function of distance between locations and independent of location. We relax this assumption and analyse malaria survey data in Mali using a Bayesian non-stationary model. Model fit and predictions are based on Markov chain Monte Carlo simulation methods. Model validation compares the predictive ability of the non-stationary model with the stationary analogue. Results indicate that the stationarity assumption is important because it influences the significance of environmental factors and the corresponding malaria risk maps.
MATHEMATICAL RISK ANALYSIS: VIA NICHOLAS RISK MODEL AND BAYESIAN ANALYSIS
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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.
Risk Based Maintenance of Offshore Wind Turbines Using Bayesian Networks
DEFF Research Database (Denmark)
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 Bayesian decision theory. The method is demonstrated through an application example....
Center for Advancing Microbial Risk Assessment
Federal Laboratory Consortium — The Center for Advancing Microbial Risk Assessment (CAMRA), based at Michigan State University and jointly funded by the U.S. Department of Homeland Security and the...
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.
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 upd...... 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....
Assessing Requirements Volatility and Risk Using Bayesian Networks
Russell, Michael S.
2010-01-01
There are many factors that affect the level of requirements volatility a system experiences over its lifecycle and the risk that volatility imparts. Improper requirements generation, undocumented user expectations, conflicting design decisions, and anticipated / unanticipated world states are representative of these volatility factors. Combined, these volatility factors can increase programmatic risk and adversely affect successful system development. This paper proposes that a Bayesian Network can be used to support reasonable judgments concerning the most likely sources and types of requirements volatility a developing system will experience prior to starting development and by doing so it is possible to predict the level of requirements volatility the system will experience over its lifecycle. This assessment offers valuable insight to the system's developers, particularly by providing a starting point for risk mitigation planning and execution.
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.
A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks
Murphy, Finbarr; Sheehan, Barry; Mullins, Martin; Bouwmeester, Hans; Marvin, Hans J. P.; Bouzembrak, Yamine; Costa, Anna L.; Das, Rasel; Stone, Vicki; Tofail, Syed A. M.
2016-11-01
While control banding has been identified as a suitable framework for the evaluation and the determination of potential human health risks associated with exposure to nanomaterials (NMs), the approach currently lacks any implementation that enjoys widespread support. Large inconsistencies in characterisation data, toxicological measurements and exposure scenarios make it difficult to map and compare the risk associated with NMs based on physicochemical data, concentration and exposure route. Here we demonstrate the use of Bayesian networks as a reliable tool for NM risk estimation. This tool is tractable, accessible and scalable. Most importantly, it captures a broad span of data types, from complete, high quality data sets through to data sets with missing data and/or values with a relatively high spread of probability distribution. The tool is able to learn iteratively in order to further refine forecasts as the quality of data available improves. We demonstrate how this risk measurement approach works on NMs with varying degrees of risk potential, namely, carbon nanotubes, silver and titanium dioxide. The results afford even non-experts an accurate picture of the occupational risk probabilities associated with these NMs and, in doing so, demonstrated how NM risk can be evaluated into a tractable, quantitative risk comparator.
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).
Mapping malaria risk in Bangladesh using Bayesian geostatistical models.
Reid, Heidi; Haque, Ubydul; Clements, Archie C A; Tatem, Andrew J; Vallely, Andrew; Ahmed, Syed Masud; Islam, Akramul; Haque, Rashidul
2010-10-01
Background malaria-control programs are increasingly dependent on accurate risk maps to effectively guide the allocation of interventions and resources. Advances in model-based geostatistics and geographical information systems (GIS) have enabled researchers to better understand factors affecting malaria transmission and thus, more accurately determine the limits of malaria transmission globally and nationally. Here, we construct Plasmodium falciparum risk maps for Bangladesh for 2007 at a scale enabling the malaria-control bodies to more accurately define the needs of the program. A comprehensive malaria-prevalence survey (N = 9,750 individuals; N = 354 communities) was carried out in 2007 across the regions of Bangladesh known to be endemic for malaria. Data were corrected to a standard age range of 2 to less than 10 years. Bayesian geostatistical logistic regression models with environmental covariates were used to predict P. falciparum prevalence for 2- to 10-year-old children (PfPR(2-10)) across the endemic areas of Bangladesh. The predictions were combined with gridded population data to estimate the number of individuals living in different endemicity classes. Across the endemic areas, the average PfPR(2-10) was 3.8%. Environmental variables selected for prediction were vegetation cover, minimum temperature, and elevation. Model validation statistics revealed that the final Bayesian geostatistical model had good predictive ability. Risk maps generated from the model showed a heterogeneous distribution of PfPR(2-10) ranging from 0.5% to 50%; 3.1 million people were estimated to be living in areas with a PfPR(2-10) greater than 1%. Contemporary GIS and model-based geostatistics can be used to interpolate malaria risk in Bangladesh. Importantly, malaria risk was found to be highly varied across the endemic regions, necessitating the targeting of resources to reduce the burden in these areas.
Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment
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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.
Bayesian Framework for Water Quality Model Uncertainty Estimation and Risk Management
A formal Bayesian methodology is presented for integrated model calibration and risk-based water quality management using Bayesian Monte Carlo simulation and maximum likelihood estimation (BMCML). The primary focus is on lucid integration of model calibration with risk-based wat...
Bayesian network as a modelling tool for risk management in agriculture
DEFF Research Database (Denmark)
Rasmussen, Svend; Madsen, Anders Læsø; Lund, Mogens
. 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 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...
Bayesian network as a modelling tool for risk management in agriculture
DEFF Research Database (Denmark)
Rasmussen, Svend; Madsen, Anders L.; Lund, Mogens
. 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 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......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...
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...
Greiner, Matthias; Smid, Joost; Havelaar, Arie H; Müller-Graf, Christine
2013-05-15
Quantitative microbiological risk assessment (QMRA) models are used to reflect knowledge about complex real-world scenarios for the propagation of microbiological hazards along the feed and food chain. The aim is to provide insight into interdependencies among model parameters, typically with an interest to characterise the effect of risk mitigation measures. A particular requirement is to achieve clarity about the reliability of conclusions from the model in the presence of uncertainty. To this end, Monte Carlo (MC) simulation modelling has become a standard in so-called probabilistic risk assessment. In this paper, we elaborate on the application of Bayesian computational statistics in the context of QMRA. It is useful to explore the analogy between MC modelling and Bayesian inference (BI). This pertains in particular to the procedures for deriving prior distributions for model parameters. We illustrate using a simple example that the inability to cope with feedback among model parameters is a major limitation of MC modelling. However, BI models can be easily integrated into MC modelling to overcome this limitation. We refer a BI submodel integrated into a MC model to as a "Bayes domain". We also demonstrate that an entire QMRA model can be formulated as Bayesian graphical model (BGM) and discuss the advantages of this approach. Finally, we show example graphs of MC, BI and BGM models, highlighting the similarities among the three approaches.
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...
Cai, Baoping; Liu, Yonghong; Liu, Zengkai; Tian, Xiaojie; Zhang, Yanzhen; Ji, Renjie
2013-07-01
This article proposes a methodology for the application of Bayesian networks in conducting quantitative risk assessment of operations in offshore oil and gas industry. The method involves translating a flow chart of operations into the Bayesian network directly. The proposed methodology consists of five steps. First, the flow chart is translated into a Bayesian network. Second, the influencing factors of the network nodes are classified. Third, the Bayesian network for each factor is established. Fourth, the entire Bayesian network model is established. Lastly, the Bayesian network model is analyzed. Subsequently, five categories of influencing factors, namely, human, hardware, software, mechanical, and hydraulic, are modeled and then added to the main Bayesian network. The methodology is demonstrated through the evaluation of a case study that shows the probability of failure on demand in closing subsea ram blowout preventer operations. The results show that mechanical and hydraulic factors have the most important effects on operation safety. Software and hardware factors have almost no influence, whereas human factors are in between. The results of the sensitivity analysis agree with the findings of the quantitative analysis. The three-axiom-based analysis partially validates the correctness and rationality of the proposed Bayesian network model.
Risks Analysis of Logistics Financial Business Based on Evidential Bayesian Network
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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.
A Bayesian network approach for causal inferences in pesticide risk assessment and management
Pesticide risk assessment and management must balance societal benefits and ecosystem protection, based on quantified risks and the strength of the causal linkages between uses of the pesticide and socioeconomic and ecological endpoints of concern. A Bayesian network (BN) is a gr...
Bayesian spatial modeling of disease risk in relation to multivariate environmental risk fields.
Kim, Ji-in; Lawson, Andrew B; McDermott, Suzanne; Aelion, C Marjorie
2010-01-15
The relationship between exposure to environmental chemicals during pregnancy and early childhood development is an important issue that has a spatial risk component. In this context, we have examined mental retardation and developmental delay (MRDD) outcome measures for children in a Medicaid population in South Carolina and sampled measures of soil chemistry (e.g. As, Hg, etc.) on a network of sites that are misaligned to the outcome residential addresses during pregnancy. The true chemical concentration at the residential addresses is not observed directly and must be interpolated from soil samples. In this study, we have developed a Bayesian joint model that interpolates soil chemical fields and estimates the associated MRDD risk simultaneously. Having multiple spatial fields to interpolate, we have considered a low-rank Kriging method for the interpolation that requires less computation than the Bayesian Kriging. We performed a sensitivity analysis for a bivariate smoothing, changing the number of knots and the smoothing parameter. These analyses show that a low-rank Kriging method can be used as an alternative to a full-rank Kriging, reducing the computational burden. However, the number of knots for the low-rank Kriging model needs to be selected with caution as a bivariate surface estimation can be sensitive to the choice of the number of knots.
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.
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.
Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.
Directory of Open Access Journals (Sweden)
Elise Payzan-LeNestour
Full Text Available Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating.
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...... combination of adaptation measures and the best time to invest in flood adaptation. IDs use Bayesian statistics which apply prior probabilities to produce posterior probabilities and, hence, use Bayesian probabilistic thinking to describe relationships between variables in a system. . Hence, we allow...... for assessing the risk of something we ?believe? may occur in the future. An ID has two layers; 1) a graphical description of the system built up by system variables, adaptation measures, costs/benefits of these measures and the dependencies of all these, which is an effective means to communicate the system...
Risk Forecasting of Karachi Stock Exchange: A Comparison of Classical and Bayesian GARCH Models
Directory of Open Access Journals (Sweden)
Farhat Iqbal
2016-09-01
Full Text Available This paper is concerned with the estimation, forecasting and evaluation of Value-at-Risk (VaR of Karachi Stock Exchange before and after the global financial crisis of 2008 using Bayesian method. The generalized autoregressive conditional heteroscedastic (GARCH models under the assumption of normal and heavy-tailed errors are used to forecast one-day-ahead risk estimates. Various measures and backtesting methods are employed to evaluate VaR forecasts. The observed number of VaR violations using Bayesian method is found close to the expected number of violations. The losses are also found smaller than the competing Maximum Likelihood method. The results showed that the Bayesian method produce accurate and reliable VaR forecasts and can be preferred over other methods.
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.
A comparison of least-squares and Bayesian minimum risk edge parameter estimation
Mulder, Nanno J.; Abkar, Ali A.
1999-01-01
The problem considered here is to compare two methods for finding a common boundary between two objects with two unknown geometric parameters, such as edge position and edge orientation. We compare two model-based approaches: the least squares and the minimum Bayesian risk method. An expression is d
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.
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.
Iskandar, Ismed; Satria Gondokaryono, Yudi
2016-02-01
In reliability theory, the most important problem is to determine the reliability of a complex system from the reliability of its components. The weakness of most reliability theories is that the systems are described and explained as simply functioning or failed. In many real situations, the failures may be from many causes depending upon the age and the environment of the system and its components. Another problem in reliability theory is one of estimating the parameters of the assumed failure models. The estimation may be based on data collected over censored or uncensored life tests. In many reliability problems, the failure data are simply quantitatively inadequate, especially in engineering design and maintenance system. The Bayesian analyses are more beneficial than the classical one in such cases. The Bayesian estimation analyses allow us to combine past knowledge or experience in the form of an apriori distribution with life test data to make inferences of the parameter of interest. In this paper, we have investigated the application of the Bayesian estimation analyses to competing risk systems. The cases are limited to the models with independent causes of failure by using the Weibull distribution as our model. A simulation is conducted for this distribution with the objectives of verifying the models and the estimators and investigating the performance of the estimators for varying sample size. The simulation data are analyzed by using Bayesian and the maximum likelihood analyses. The simulation results show that the change of the true of parameter relatively to another will change the value of standard deviation in an opposite direction. For a perfect information on the prior distribution, the estimation methods of the Bayesian analyses are better than those of the maximum likelihood. The sensitivity analyses show some amount of sensitivity over the shifts of the prior locations. They also show the robustness of the Bayesian analysis within the range
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
A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks
Murphy, Finbarr; Sheehan, Barry; Mullins, Martin; Bouwmeester, Hans; Marvin, Hans J.P.; Bouzembrak, Yamine; Costa, Anna L.; Das, Rasel; Stone, Vicki; Tofail, Syed A.M.
2016-01-01
While control banding has been identified as a suitable framework for the evaluation and the determination of potential human health risks associated with exposure to nanomaterials (NMs), the approach currently lacks any implementation that enjoys widespread support. Large inconsistencies in char
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.
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.
Xu, Chengcheng; Wang, Wei; Liu, Pan; Li, Zhibin
2015-12-01
This study aimed to develop a real-time crash risk model with limited data in China by using Bayesian meta-analysis and Bayesian inference approach. A systematic review was first conducted by using three different Bayesian meta-analyses, including the fixed effect meta-analysis, the random effect meta-analysis, and the meta-regression. The meta-analyses provided a numerical summary of the effects of traffic variables on crash risks by quantitatively synthesizing results from previous studies. The random effect meta-analysis and the meta-regression produced a more conservative estimate for the effects of traffic variables compared with the fixed effect meta-analysis. Then, the meta-analyses results were used as informative priors for developing crash risk models with limited data. Three different meta-analyses significantly affect model fit and prediction accuracy. The model based on meta-regression can increase the prediction accuracy by about 15% as compared to the model that was directly developed with limited data. Finally, the Bayesian predictive densities analysis was used to identify the outliers in the limited data. It can further improve the prediction accuracy by 5.0%.
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.
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.
Dynamic Bayesian modeling for risk prediction in credit operations
DEFF Research Database (Denmark)
Borchani, Hanen; Martinez, Ana Maria; 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 streaming data analysis: the class labels are not immediately available and the relevant predictive features and entities under study (in this case the set of customers) may vary over time. In order to address these problems, we propose to use a dynamic classifier with a wrapper feature subset selection...
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
A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs
DEFF Research Database (Denmark)
Petersen, Peter Kersten; Ganz, Melanie; Mysling, Peter
2012-01-01
the score. Since the aorta is invisible on X-ray images, its position is reasoned from (1) the shape and location of the lumbar vertebrae and (2) the location, shape, and orientation of potential calcifications. The proposed framework follows the principle of Bayesian inference, which has several advantages......We present a fully automated framework for scoring a patients risk of cardiovascular disease (CVD) and mortality from a standard lateral radiograph of the lumbar aorta. The framework segments abdominal aortic calcifications for computing a CVD risk score and performs a survival analysis to validate...
Development of Bayesian network models for risk-based ship design
Konovessis, Dimitris; Cai, Wenkui; Vassalos, Dracos
2013-06-01
In the past fifteen years, the attention of ship safety treatment as an objective rather than a constraint has started to sweep through the whole maritime industry. The risk-based ship design (RBD) methodology, advocating systematic integration of risk assessment within the conventional design process has started to takeoff. Despite this wide recognition and increasing popularity, important factors that could potentially undermine the quality of the results come from both quantitative and qualitative aspects during the risk assessment process. This paper details a promising solution by developing a formalized methodology for risk assessment through effective storing and processing of historical data combined with data generated through first-principle approaches. This method should help to generate appropriate risk models in the selected platform (Bayesian networks) which can be employed for decision making at design stage.
Development of Bayesian Network Models for Risk-Based Ship Design
Institute of Scientific and Technical Information of China (English)
Dimitris Konovessis; Wenkui Cai; Dracos Vassalos
2013-01-01
In the past fifteen years,the attention of ship safety treatment as an objective rather than a constraint has started to sweep through the whole maritime industry.The risk-based ship design (RBD) methodology,advocating systematic integration of risk assessment within the conventional design process has started to takeoff.Despite this wide recognition and increasing popularity,important factors that could potentially undermine the quality of the results come from both quantitative and qualitative aspects during the risk assessment process.This paper details a promising solution by developing a formalized methodology for risk assessment through effective storing and processing of historical data combined with data generated through first-principle approaches.This method should help to generate appropriate risk models in the selected platform (Bayesian networks) which can be employed for decision making at design stage.
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......-inflated Poisson-lognormal distributed data and an existing QMRA model from retail to consumer level, it was possible to assess the difference between expected risk and the risk estimated with using a lognormal, a zero-inflated lognormal, a Poisson-gamma, a zero-inflated Poisson-gamma and a zero-inflated Poisson...... 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...
Modeling Logistic Performance in Quantitative Microbial Risk Assessment
Rijgersberg, H.; Tromp, S.O.; Jacxsens, L.; Uyttendaele, M.
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 ti
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
Estimation model of life insurance claims risk for cancer patients by using Bayesian method
Sukono; Suyudi, M.; Islamiyati, F.; Supian, S.
2017-01-01
This paper discussed the estimation model of the risk of life insurance claims for cancer patients using Bayesian method. To estimate the risk of the claim, the insurance participant data is grouped into two: the number of policies issued and the number of claims incurred. Model estimation is done using a Bayesian approach method. Further, the estimator model was used to estimate the risk value of life insurance claims each age group for each sex. The estimation results indicate that a large risk premium for insured males aged less than 30 years is 0.85; for ages 30 to 40 years is 3:58; for ages 41 to 50 years is 1.71; for ages 51 to 60 years is 2.96; and for those aged over 60 years is 7.82. Meanwhile, for insured women aged less than 30 years was 0:56; for ages 30 to 40 years is 3:21; for ages 41 to 50 years is 0.65; for ages 51 to 60 years is 3:12; and for those aged over 60 years is 9.99. This study is useful in determining the risk premium in homogeneous groups based on gender and age.
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.
An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.
Directory of Open Access Journals (Sweden)
Rowena Syn Yin Wong
Full Text Available 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.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.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.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 in-ICU mortality outcomes.
2012-07-31
... Assessment Guideline: Pathogenic Microorganisms with Focus on Food and Water (MRA Guideline). The MRA... document, Microbial Risk Assessment Guideline: Pathogenic Microorganisms with Focus on Food and Water will... AGENCY Notice of Availability of Microbial Risk Assessment Guideline: Pathogenic Microorganisms...
Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach.
Yu, Rongjie; Wang, Xuesong; Yang, Kui; Abdel-Aty, Mohamed
2016-10-01
Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded.
Directory of Open Access Journals (Sweden)
Stephen W Hartley
2012-09-01
Full Text Available Genome-wide association studies (GWAS have identified numerous associations between genetic loci and individual phenotypes; however, relatively few GWAS have attempted to detect pleiotropic associations, in which loci are simultaneously associated with multiple distinct phenotypes. We show that pleiotropic associations can be directly modeled via the construction of simple Bayesian networks, and that these models can be applied to produce single or ensembles of Bayesian classifiers that leverage pleiotropy to improve genetic risk prediction.The proposed method includes two phases: (1 Bayesian model comparison, to identify SNPs associated with one or more traits; and (2 cross validation feature selection, in which a final set of SNPs is selected to optimize prediction.To demonstrate the capabilities and limitations of the method, a total of 1600 case-control GWAS datasets with 2 dichotomous phenotypes were simulated under 16 scenarios, varying the association strengths of causal SNPs, the size of the discovery sets, the balance between cases and controls, and the number of pleiotropic causal SNPs.Across the 16 scenarios, prediction accuracy varied from 90% to 50%. In the 14 scenarios that included pleiotropically-associated SNPs, the pleiotropic model search and prediction methods consistently outperformed the naive model search and prediction. In the 2 scenarios in which there were no true pleiotropic SNPs, the differences between the pleiotropic and naive model searches were minimal.
Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks.
Zhang, Jinfen; Teixeira, Ângelo P; Guedes Soares, C; Yan, Xinping; Liu, Kezhong
2016-06-01
This article develops a Bayesian belief network model for the prediction of accident consequences in the Tianjin port. The study starts with a statistical analysis of historical accident data of six years from 2008 to 2013. Then a Bayesian belief network is constructed to express the dependencies between the indicator variables and accident consequences. The statistics and expert knowledge are synthesized in the Bayesian belief network model to obtain the probability distribution of the consequences. By a sensitivity analysis, several indicator variables that have influence on the consequences are identified, including navigational area, ship type and time of the day. The results indicate that the consequences are most sensitive to the position where the accidents occurred, followed by time of day and ship length. The results also reflect that the navigational risk of the Tianjin port is at the acceptable level, despite that there is more room of improvement. These results can be used by the Maritime Safety Administration to take effective measures to enhance maritime safety in the Tianjin port.
Capra, B.; Le Drogo, J.; Wolff, V.
2006-11-01
Degradation modelling of concrete structures uses uncertain variables and leads, using reliability assessment, to time dependant evolution of failure probabilities. However, only few data are generally available to feed models leading to two types of uncertainties: an intrinsic one depending on the modelled phenomena and one related to the precision of the measurement. Each new data available is a piece of information which allows to update the initial prediction. In this article, an example of updating process, based on a Bayesian network, is presented and applied on the corrosion risk of a cooling tower.
Energy Technology Data Exchange (ETDEWEB)
Capra, B.; Le Drogo, J.; Wolff, V. [OXAND S.A., 36 bis avenue F. Roosevelt, 77210 Avon (France)
2006-07-01
Degradation modelling of concrete structures uses uncertain variables and leads, using reliability assessment, to time dependant evolution of failure probabilities. However, only few data are generally available to feed models leading to two types of uncertainties: an intrinsic one depending on the modelled phenomena and one related to the precision of the measurement. Each new data available is a piece of information which allows to update the initial prediction. In this article, an example of updating process, based on a Bayesian network, is presented and applied on the corrosion risk of a cooling tower. (authors)
Busschaert, P; Geeraerd, A H; Uyttendaele, M; Van Impe, J F
2011-06-01
Microbiological contamination data often is censored because of the presence of non-detects or because measurement outcomes are known only to be smaller than, greater than, or between certain boundary values imposed by the laboratory procedures. Therefore, it is not straightforward to fit distributions that summarize contamination data for use in quantitative microbiological risk assessment, especially when variability and uncertainty are to be characterized separately. In this paper, distributions are fit using Bayesian analysis, and results are compared to results obtained with a methodology based on maximum likelihood estimation and the non-parametric bootstrap method. The Bayesian model is also extended hierarchically to estimate the effects of the individual elements of a covariate such as, for example, on a national level, the food processing company where the analyzed food samples were processed, or, on an international level, the geographical origin of contamination data. Including this extra information allows a risk assessor to differentiate between several scenario's and increase the specificity of the estimate of risk of illness, or compare different scenario's to each other. Furthermore, inference is made on the predictive importance of several different covariates while taking into account uncertainty, allowing to indicate which covariates are influential factors determining contamination.
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.
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.
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 (pbreast cancer risk than with a single feature.
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
2016-04-07
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.
Gardner, Ian A
2004-09-01
Data deficiencies are impeding the development and validation of microbial risk assessment models. One such deficiency is the failure to adjust test-based (apparent) prevalence estimates to true prevalence estimates by correcting for the imperfect accuracy of tests that are used. Such adjustments will facilitate comparability of data from different populations and from the same population over time as tests change and the unbiased quantification of effects of mitigation strategies. True prevalence can be estimated from apparent prevalence using frequentist and Bayesian methods, but the latter are more flexible and can incorporate uncertainty in test accuracy and prior prevalence data. Both approaches can be used for single or multiple populations, but the Bayesian approach can better deal with clustered data, inferences for rare events, and uncertainty in multiple variables. Examples of prevalence inferences based on results of Salmonella culture are presented. The opportunity to adjust test-based prevalence estimates is predicated on the availability of sensitivity and specificity estimates. These estimates can be obtained from studies using archived gold standard (reference) samples, by screening with the new test and follow-up of test-positive and test-negative samples with a gold standard test, and by use of latent class methods, which make no assumptions about the true status of each sampling unit. Latent class analysis can be done with maximum likelihood and Bayesian methods, and an example of their use in the evaluation of tests for Toxoplasma gondii in pigs is presented. Guidelines are proposed for more transparent incorporation of test data into microbial risk assessments.
Urban flooding and health risk analysis by use of quantitative microbial risk assessment
DEFF Research Database (Denmark)
Andersen, Signe Tanja
according to location, as demonstrated for microbial, chemical and physical parameters between sub-catchments. Variations in wastewater quality, as well as rainfall, system and environmental effects (solar radiation), cause knock-on variations in floodwater composition, because they are functions...... of these parameters. Variations between locations have been demonstrated through measurements of microbial concentrations in flooding episodes, while changes in microbial concentrations over time have been demonstrated through a survival and decay study, where decay was substantial in the presence of UV light......, the drainage model was validated by comparison of modelled and measured microbial concentrations in CSOs. The model result was used in the analysis of the risk of infection from exposure to urban flooding which resulted in a risk of 10.3 to 10-1 from exposure to flooding, both from cleaning up flooding...
The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network
Directory of Open Access Journals (Sweden)
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.
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....
[New approach for managing microbial risks in food].
Augustin, Jean-Christophe
2015-01-01
The aim of the food legislation is to ensure the protection of human health. Traditionally, the food legislation requires food business operators to apply good hygiene practices and specific procedures to control foodborne pathogens. These regulations allowed reaching a high level of health protection. The improvement of the system will require risk-based approaches. Firstly, risk assessment should allow the identification of high-risk situations where resources should be allocated for a better targeting of risk management. Then, management measures should be adapted to the health objective. In this approach, the appropriate level of protection is converted intofood safety and performance objectives on the food chain, i.e., maximum microbial contamination to fulfil the acceptable risk level. When objectives are defined, the food business operators and competent authorities can identify control options to comply with the objectives and establish microbiological criteria to verify compliance with these objectives. This approach, described for approximately 10 years, operative thanks to the development of quantitative risk assessment techniques, is still difficult to use in practical terms since it requires a commitment of competent authorities to define the acceptable risk and needs also the implementation of sometimes complex risk models.
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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.
Bayesian updating in a fault tree model for shipwreck risk assessment.
Landquist, H; Rosén, L; Lindhe, A; Norberg, T; Hassellöv, I-M
2017-03-14
Shipwrecks containing oil and other hazardous substances have been deteriorating on the seabeds of the world for many years and are threatening to pollute the marine environment. The status of the wrecks and the potential volume of harmful substances present in the wrecks are affected by a multitude of uncertainties. Each shipwreck poses a unique threat, the nature of which is determined by the structural status of the wreck and possible damage resulting from hazardous activities that could potentially cause a discharge. Decision support is required to ensure the efficiency of the prioritisation process and the allocation of resources required to carry out risk mitigation measures. Whilst risk assessments can provide the requisite decision support, comprehensive methods that take into account key uncertainties related to shipwrecks are limited. The aim of this paper was to develop a method for estimating the probability of discharge of hazardous substances from shipwrecks. The method is based on Bayesian updating of generic information on the hazards posed by different activities in the surroundings of the wreck, with information on site-specific and wreck-specific conditions in a fault tree model. Bayesian updating is performed using Monte Carlo simulations for estimating the probability of a discharge of hazardous substances and formal handling of intrinsic uncertainties. An example application involving two wrecks located off the Swedish coast is presented. Results show the estimated probability of opening, discharge and volume of the discharge for the two wrecks and illustrate the capability of the model to provide decision support. Together with consequence estimations of a discharge of hazardous substances, the suggested model enables comprehensive and probabilistic risk assessments of shipwrecks to be made.
DEFF Research Database (Denmark)
Ranta, Jukka; Lindqvist, Roland; Hansson, Ingrid
2015-01-01
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 concentrations of microbes in production batches. We used Bayesian modeling for statistical inference......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...... and 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...
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.
Microbial risk and removal--a utility perspective.
Stanger, M; Agutter, P A; Lake, R C; Ashbolt, N J; Roser, D J
2006-01-01
In this paper the results of a sampling programme, undertaken as part of the EU MicroRisk project, are described. This project was undertaken to ascertain the occurrence of pathogens and indicators in the River Thames and their subsequent removal through a treatment works. Appropriate physico-chemical surrogates, as determined by statistical correlation are proposed for the microorganisms identified in the raw water. This study shows that under normal raw water conditions the treatment works is able to remove microbial contamination with a significant margin of safety.
DEFF Research Database (Denmark)
Koop, Gerrit; Collar, Carol A.; Toft, Nils
2013-01-01
are imperfect tests, particularly lacking sensitivity, which leads to misclassification and thus to biased estimates of odds ratios in risk factor studies. The objective of this study was to evaluate risk factors for the true (latent) IMI status of major pathogens in dairy goats. We used Bayesian logistic......, caprine arthritis encephalitis-virus infection status, and kidding season), and uncontrollable risk factors (parity, lactation stage, milk yield, pregnancy status, and breed) were measured in the Dutch study, the Californian study or in both studies. Bayesian logistic regression models were constructed...... in which the true (but latent) infection status was linked to the joint test results, as functions of test sensitivity and specificity. The latent IMI status was the dependent variable in the logistic regression model with risk factors as independent variables and with random herd and goat effects...
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.
Taylor, Jonathon; Biddulph, Phillip; Davies, Michael; Lai, Ka man
2013-01-01
London is expected to experience more frequent periods of intense rainfall and tidal surges, leading to an increase in the risk of flooding. Damp and flooded dwellings can support microbial growth, including mould, bacteria, and protozoa, as well as persistence of flood-borne microorganisms. The amount of time flooded dwellings remain damp will depend on the duration and height of the flood, the contents of the flood water, the drying conditions, and the building construction, leading to particular properties and property types being prone to lingering damp and human pathogen growth or persistence. The impact of flooding on buildings can be simulated using Heat Air and Moisture (HAM) models of varying complexity in order to understand how water can be absorbed and dry out of the building structure. This paper describes the simulation of the drying of building archetypes representative of the English building stock using the EnergyPlus based tool 'UCL-HAMT' in order to determine the drying rates of different abandoned structures flooded to different heights and during different seasons. The results are mapped out using GIS in order to estimate the spatial risk across London in terms of comparative flood vulnerability, as well as for specific flood events. Areas of South and East London were found to be particularly vulnerable to long-term microbial exposure following major flood events.
Williams, Michael S.; Ebel, Eric D.; Jennifer A Hoeting
2011-01-01
Bayesian methods are becoming increasingly popular in the field of food-safety risk assessment. Risk assessment models often require the integration of a dose-response function over the distribution of all possible doses of a pathogen ingested with a specific food. This requires the evaluation of an integral for every sample for a Markov chain Monte Carlo analysis of a model. While many statistical software packages have functions that allow for the evaluation of the integral, this functional...
A Bayesian framework for early risk prediction in traumatic brain injury
Chaganti, Shikha; Plassard, Andrew J.; Wilson, Laura; Smith, Miya A.; Patel, Mayur B.; Landman, Bennett A.
2016-03-01
Early detection of risk is critical in determining the course of treatment in traumatic brain injury (TBI). Computed tomography (CT) acquired at admission has shown latent prognostic value in prior studies; however, no robust clinical risk predictions have been achieved based on the imaging data in large-scale TBI analysis. The major challenge lies in the lack of consistent and complete medical records for patients, and an inherent bias associated with the limited number of patients samples with high-risk outcomes in available TBI datasets. Herein, we propose a Bayesian framework with mutual information-based forward feature selection to handle this type of data. Using multi-atlas segmentation, 154 image-based features (capturing intensity, volume and texture) were computed over 22 ROIs in 1791 CT scans. These features were combined with 14 clinical parameters and converted into risk likelihood scores using Bayes modeling. We explore the prediction power of the image features versus the clinical measures for various risk outcomes. The imaging data alone were more predictive of outcomes than the clinical data (including Marshall CT classification) for discharge disposition with an area under the curve of 0.81 vs. 0.67, but less predictive than clinical data for discharge Glasgow Coma Scale (GCS) score with an area under the curve of 0.65 vs. 0.85. However, in both cases, combining imaging and clinical data increased the combined area under the curve with 0.86 for discharge disposition and 0.88 for discharge GCS score. In conclusion, CT data have meaningful prognostic value for TBI patients beyond what is captured in clinical measures and the Marshall CT classification.
Stojadinovic, Alexander; Nissan, Aviram; Eberhardt, John; Chua, Terence C; Pelz, Joerg O W; Esquivel, Jesus
2011-02-01
Multimodality therapy in selected patients with peritoneal carcinomatosis is gaining acceptance. Treatment-directing decision support tools are needed to individualize care and select patients best suited for cytoreductive surgery +/- hyperthermic intraperitoneal chemotherapy (CRS +/- HIPEC). The purpose of this study is to develop a predictive model that could support surgical decisions in patients with colon carcinomatosis. Fifty-three patients were enrolled in a prospective study collecting 31 clinical-pathological, treatment-related, and outcome data. The population was characterized by disease presentation, performance status, extent of peritoneal cancer (Peritoneal Cancer Index, PCI), primary tumor histology, and nodal staging. These preoperative parameters were analyzed using step-wise machine-learned Bayesian Belief Networks (BBN) to develop a predictive model for overall survival (OS) in patients considered for CRS +/- HIPEC. Area-under-the-curve from receiver-operating-characteristics curves of OS predictions was calculated to determine the model's positive and negative predictive value. Model structure defined three predictors of OS: severity of symptoms (performance status), PCI, and ability to undergo CRS +/- HIPEC. Patients with PCI 20, who were not considered surgical candidates. Cross validation of the BBN model robustly classified OS (area-under-the-curve = 0.71). The model's positive predictive value and negative predictive value are 63.3 per cent and 68.3 per cent, respectively. This exploratory study supports the utility of Bayesian classification for developing decision support tools, which assess case-specific relative risk for a given patient for oncological outcomes based on clinically relevant classifiers of survival. Further prospective studies to validate the BBN model-derived prognostic assessment tool are warranted.
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
The objective of the study is to perform an integrated analysis of microbiological risks and nutritional benefits in a fish product, Cold Smoked Salmon (CSS). Literature study identified the major health risks and benefits in connection with CSS consumption. The reduction of the risk of Coronary......, except the storage time: the adverse effect of consumption of CSS prevails over the beneficial effect if the storage time of CSS is increased from two weeks to five weeks or more, due to an increased risk of listeriosis. This study demonstrates how microbial risks can be integrated in risk...... 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...
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
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Pavlos A. Kassomenos
2009-02-01
Full Text Available The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural. Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations.
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.
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 in a spat......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...... in a spatially explicit and cost-effective manner. We analysed a large ensemble of georeferenced survey data derived from an open-access neglected tropical diseases database to create smooth empirical prevalence maps for Schistosoma mansoni and Schistosoma haematobium for a total of 13 countries of eastern...... Africa. Bayesian geostatistical models based on climatic and other environmental data were used to account for potential spatial clustering in spatially structured exposures. Geostatistical variable selection was employed to reduce the set of covariates. Alignment factors were implemented to combine...
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.
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)
Attallah, Omneya; Ma, Xianghong
2014-09-01
This article proposes a Bayesian neural network approach to determine the risk of re-intervention after endovascular aortic aneurysm repair surgery. The target of proposed technique is to determine which patients have high chance to re-intervention (high-risk patients) and which are not (low-risk patients) after 5 years of the surgery. Two censored datasets relating to the clinical conditions of aortic aneurysms have been collected from two different vascular centers in the United Kingdom. A Bayesian network was first employed to solve the censoring issue in the datasets. Then, a back propagation neural network model was built using the uncensored data of the first center to predict re-intervention on the second center and classify the patients into high-risk and low-risk groups. Kaplan-Meier curves were plotted for each group of patients separately to show whether there is a significant difference between the two risk groups. Finally, the logrank test was applied to determine whether the neural network model was capable of predicting and distinguishing between the two risk groups. The results show that the Bayesian network used for uncensoring the data has improved the performance of the neural networks that were built for the two centers separately. More importantly, the neural network that was trained with uncensored data of the first center was able to predict and discriminate between groups of low risk and high risk of re-intervention after 5 years of endovascular aortic aneurysm surgery at center 2 (p = 0.0037 in the logrank test).
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.
Myte, Robin; Gylling, Björn; Häggström, Jenny; Schneede, Jörn; Magne Ueland, Per; Hallmans, Göran; Johansson, Ingegerd; Palmqvist, Richard; Van Guelpen, Bethany
2017-01-01
The role of one-carbon metabolism (1CM), particularly folate, in colorectal cancer (CRC) development has been extensively studied, but with inconclusive results. Given the complexity of 1CM, the conventional approach, investigating components individually, may be insufficient. We used a machine learning-based Bayesian network approach to study, simultaneously, 14 circulating one-carbon metabolites, 17 related single nucleotide polymorphisms (SNPs), and several environmental factors in relation to CRC risk in 613 cases and 1190 controls from the prospective Northern Sweden Health and Disease Study. The estimated networks corresponded largely to known biochemical relationships. Plasma concentrations of folate (direct), vitamin B6 (pyridoxal 5-phosphate) (inverse), and vitamin B2 (riboflavin) (inverse) had the strongest independent associations with CRC risk. Our study demonstrates the importance of incorporating B-vitamins in future studies of 1CM and CRC development, and the usefulness of Bayesian network learning for investigating complex biological systems in relation to disease. PMID:28233834
Bayesian spatial modeling of disease risk in relation to multivariate environmental risk fields
Kim, Ji-In; Lawson, Andrew B.; McDermott, Suzanne; Aelion, C. Marjorie
2010-01-01
The relationship between exposure to environmental chemicals during pregnancy and early childhood development is an important issue which has a spatial risk component. In this context, we have examined mental retardation and developmental delay (MRDD) outcome measures for children in a Medicaid population in South Carolina and sampled measures of soil chemistry (e.g. As, Hg, etc.) on a network of sites which are misaligned to the outcome residential addresses during pregnancy. The true chemic...
Hwang, Beom Seuk; Pennell, Michael L
2014-03-30
Many dose-response studies collect data on correlated outcomes. For example, in developmental toxicity studies, uterine weight and presence of malformed pups are measured on the same dam. Joint modeling can result in more efficient inferences than independent models for each outcome. Most methods for joint modeling assume standard parametric response distributions. However, in toxicity studies, it is possible that response distributions vary in location and shape with dose, which may not be easily captured by standard models. To address this issue, we propose a semiparametric Bayesian joint model for a binary and continuous response. In our model, a kernel stick-breaking process prior is assigned to the distribution of a random effect shared across outcomes, which allows flexible changes in distribution shape with dose shared across outcomes. The model also includes outcome-specific fixed effects to allow different location effects. In simulation studies, we found that the proposed model provides accurate estimates of toxicological risk when the data do not satisfy assumptions of standard parametric models. We apply our method to data from a developmental toxicity study of ethylene glycol diethyl ether.
Applications of Bayesian approach in modelling risk of malaria-related hospital mortality
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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.
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.
Bayesian objective classification of extreme UK daily rainfall for flood risk applications
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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.
Harris, Meagan J; Stinson, Jonah; Landis, Wayne G
2017-01-25
We conducted a regional-scale integrated ecological and human health risk assessment by applying the relative risk model with Bayesian networks (BN-RRM) to a case study of the South River, Virginia mercury-contaminated site. Risk to four ecological services of the South River (human health, water quality, recreation, and the recreational fishery) was evaluated using a multiple stressor-multiple endpoint approach. These four ecological services were selected as endpoints based on stakeholder feedback and prioritized management goals for the river. The BN-RRM approach allowed for the calculation of relative risk to 14 biotic, human health, recreation, and water quality endpoints from chemical and ecological stressors in five risk regions of the South River. Results indicated that water quality and the recreational fishery were the ecological services at highest risk in the South River. Human health risk for users of the South River was low relative to the risk to other endpoints. Risk to recreation in the South River was moderate with little spatial variability among the five risk regions. Sensitivity and uncertainty analysis identified stressors and other parameters that influence risk for each endpoint in each risk region. This research demonstrates a probabilistic approach to integrated ecological and human health risk assessment that considers the effects of chemical and ecological stressors across the landscape.
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.
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
Directory of Open Access Journals (Sweden)
Xian Shan
2017-01-01
Full Text Available Pipeline is the major mode of natural gas transportation. Leakage of natural gas pipelines may cause explosions and fires, resulting in casualties, environmental damage, and material loss. Efficient risk analysis is of great significance for preventing and mitigating such potential accidents. The objective of this study is to present a practical risk assessment method based on Bow-tie model and Bayesian network for risk analysis of natural gas pipeline leakage. Firstly, identify the potential risk factors and consequences of the failure. Then construct the Bow-tie model, use the quantitative analysis of Bayesian network to find the weak links in the system, and make a prediction of the control measures to reduce the rate of the accident. In order to deal with the uncertainty existing in the determination of the probability of basic events, fuzzy logic method is used. Results of a case study show that the most likely causes of natural gas pipeline leakage occurrence are parties ignore signage, implicit signage, overload, and design defect of auxiliaries. Once the leakage occurs, it is most likely to result in fire and explosion. Corresponding measures taken on time will reduce the disaster degree of accidents to the least extent.
Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, and human health effect...
The importance of virulence prediction and gene networks in microbial risk assessment
DEFF Research Database (Denmark)
Wassenaar, Gertrude Maria; Gamieldien, Junaid; Shatkin, JoAnne
2007-01-01
For microbial risk assessment, it is necessary to recognize and predict Virulence of bacterial pathogens, including their ability to contaminate foods. Hazard characterization requires data on strain variability regarding virulence and survival during food processing. Moreover, information on vir...
Hagos, Seifu; Hailemariam, Damen; WoldeHanna, Tasew; Lindtjørn, Bernt
2017-01-01
Background Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia. Methods A community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0–59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran's I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area. Results Overall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child’s age increased (OR 4.74; 95% Bayesian credible
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
Rezaianzadeh, Abbas; Sepandi, Mojtaba; Rahimikazerooni, Salar
2016-11-01
Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.
Application of bayesian networks to real-time flood risk estimation
Garrote, L.; Molina, M.; Blasco, G.
2003-04-01
This paper presents the application of a computational paradigm taken from the field of artificial intelligence - the bayesian network - to model the behaviour of hydrologic basins during floods. The final goal of this research is to develop representation techniques for hydrologic simulation models in order to define, develop and validate a mechanism, supported by a software environment, oriented to build decision models for the prediction and management of river floods in real time. The emphasis is placed on providing decision makers with tools to incorporate their knowledge of basin behaviour, usually formulated in terms of rainfall-runoff models, in the process of real-time decision making during floods. A rainfall-runoff model is only a step in the process of decision making. If a reliable rainfall forecast is available and the rainfall-runoff model is well calibrated, decisions can be based mainly on model results. However, in most practical situations, uncertainties in rainfall forecasts or model performance have to be incorporated in the decision process. The computation paradigm adopted for the simulation of hydrologic processes is the bayesian network. A bayesian network is a directed acyclic graph that represents causal influences between linked variables. Under this representation, uncertain qualitative variables are related through causal relations quantified with conditional probabilities. The solution algorithm allows the computation of the expected probability distribution of unknown variables conditioned to the observations. An approach to represent hydrologic processes by bayesian networks with temporal and spatial extensions is presented in this paper, together with a methodology for the development of bayesian models using results produced by deterministic hydrologic simulation models
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.
A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk
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Lewei Duan
2013-01-01
Full Text Available A variety of methods have been proposed for studying the association of multiple genes thought to be involved in a common pathway for a particular disease. Here, we present an extension of a Bayesian hierarchical modeling strategy that allows for multiple SNPs within each gene, with external prior information at either the SNP or gene level. The model involves variable selection at the SNP level through latent indicator variables and Bayesian shrinkage at the gene level towards a prior mean vector and covariance matrix that depend on external information. The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. The method is applied to data on 504 SNPs in 38 candidate genes involved in DNA damage response in the WECARE study of second breast cancers in relation to radiotherapy exposure.
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...
Landis, Wayne G; Markiewicz, April J; Ayre, Kim K; Johns, Annie F; Harris, Meagan J; Stinson, Jonah M; Summers, Heather M
2017-01-01
Adaptive management has been presented as a method for the remediation, restoration, and protection of ecological systems. Recent reviews have found that the implementation of adaptive management has been unsuccessful in many instances. We present a modification of the model first formulated by Wyant and colleagues that puts ecological risk assessment into a central role in the adaptive management process. This construction has 3 overarching segments. Public engagement and governance determine the goals of society by identifying endpoints and specifying constraints such as costs. The research, engineering, risk assessment, and management section contains the decision loop estimating risk, evaluating options, specifying the monitoring program, and incorporating the data to re-evaluate risk. The 3rd component is the recognition that risk and public engagement can be altered by various externalities such as climate change, economics, technological developments, and population growth. We use the South River, Virginia, USA, study area and our previous research to illustrate each of these components. In our example, we use the Bayesian Network Relative Risk Model to estimate risks, evaluate remediation options, and provide lists of monitoring priorities. The research, engineering, risk assessment, and management loop also provides a structure in which data and the records of what worked and what did not, the learning process, can be stored. The learning process is a central part of adaptive management. We conclude that risk assessment can and should become an integral part of the adaptive management process. Integr Environ Assess Manag 2017;13:115-126. © 2016 SETAC.
Lalande, Laure; Bourguignon, Laurent; Carlier, Chloé; Ducher, Michel
2013-06-01
Falls in geriatry are associated with important morbidity, mortality and high healthcare costs. Because of the large number of variables related to the risk of falling, determining patients at risk is a difficult challenge. The aim of this work was to validate a tool to detect patients with high risk of fall using only bibliographic knowledge. Thirty articles corresponding to 160 studies were used to modelize fall risk. A retrospective case-control cohort including 288 patients (88 ± 7 years) and a prospective cohort including 106 patients (89 ± 6 years) from two geriatric hospitals were used to validate the performances of our model. We identified 26 variables associated with an increased risk of fall. These variables were split into illnesses, medications, and environment. The combination of the three associated scores gives a global fall score. The sensitivity and the specificity were 31.4, 81.6, 38.5, and 90 %, respectively, for the retrospective and the prospective cohort. The performances of the model are similar to results observed with already existing prediction tools using model adjustment to data from numerous cohort studies. This work demonstrates that knowledge from the literature can be synthesized with Bayesian networks.
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Cheng J
2016-10-01
Full Text Available Ji Cheng,1,2 Alfonso Iorio,2,3 Maura Marcucci,4 Vadim Romanov,5 Eleanor M Pullenayegum,6,7 John K Marshall,3,8 Lehana Thabane1,2 1Biostatistics Unit, St Joseph’s Healthcare Hamilton, 2Department of Clinical Epidemiology and Biostatistics, 3Department of Medicine, McMaster University, Hamilton, ON, Canada; 4Geriatrics, Fondazione Ca’ Granda Ospedale Maggiore Policlinico, Università degli Studi di Milano, Milan, Italy; 5Baxter HealthCare, Global Medical Affairs, Westlake Village, CA, USA; 6Child Health Evaluation Sciences, Hospital for Sick Children, 7Dalla Lana School of Public Health, University of Toronto, Toronto, 8Division of Gastroenterology, Hamilton Health Science, Hamilton, ON, Canada Background: Developing inhibitors is a rare event during the treatment of hemophilia A. The multifacets and uncertainty surrounding the development of inhibitors further complicate the process of estimating inhibitor rate from the limited data. Bayesian statistical modeling provides a useful tool in generating, enhancing, and exploring the evidence through incorporating all the available information.Methods: We built our Bayesian analysis using three study cases to estimate the inhibitor rates of patients with hemophilia A in three different scenarios: Case 1, a single cohort of previously treated patients (PTPs or previously untreated patients; Case 2, a meta-analysis of PTP cohorts; and Case 3, a previously unexplored patient population – patients with baseline low-titer inhibitor or history of inhibitor development. The data used in this study were extracted from three published ADVATE (antihemophilic factor [recombinant] is a product of Baxter for treating hemophilia A post-authorization surveillance studies. Noninformative and informative priors were applied to Bayesian standard (Case 1 or random-effects (Case 2 and Case 3 logistic models. Bayesian probabilities of satisfying three meaningful thresholds of the risk of developing a clinical
Johns, Annie F; Graham, Scarlett E; Harris, Meagan J; Markiewicz, April J; Stinson, Jonah M; Landis, Wayne G
2017-01-01
We have conducted a series of regional scale risk assessments using the Bayesian Network Relative Risk Model (BN-RRM) to evaluate the efficacy of 2 remediation options in the reduction of risks to the South River and upper Shenandoah River study area. The 2 remediation options were 1) bank stabilization (BST) and 2) the implementation of best management practices for agriculture (AgBMPs) to reduce Hg input in to the river. Eight endpoints were chosen to be part of the risk assessment, based on stakeholder input. Although Hg contamination was the original impetus for the site being remediated, multiple chemical and physical stressors were evaluated in this analysis. Specific models were built that incorporated the changes expected from AgBMP and BST and were based on our previous research. Changes in risk were calculated, and sensitivity and influence analyses were conducted on the models. The assessments indicated that AgBMP would only slightly change risk in the study area but that negative impacts were also unlikely. Bank stabilization would reduce risk to Hg for the smallmouth bass and belted kingfisher and increase risk to abiotic water quality endpoints. However, if care were not taken to prevent loss of nesting habitat to belted kingfisher, an increase in risk to that species would occur. Because Hg was only one of several stressors contributing to risk, the change in risk depended on the specific endpoint. Sensitivity analysis provided a list of variables to be measured as part of a monitoring program. Influence analysis provided the range of maximum and minimum risk values for each endpoint and remediation option. This research demonstrates the applicability of ecological risk assessment and specifically the BN-RRM as part of a long-term adaptive management scheme for managing contaminated sites. Integr Environ Assess Manag 2017;13:100-114. © 2016 SETAC.
Landis, Wayne G; Ayre, Kimberley K; Johns, Annie F; Summers, Heather M; Stinson, Jonah; Harris, Meagan J; Herring, Carlie E; Markiewicz, April J
2017-01-01
We have conducted a regional scale risk assessment using the Bayesian Network Relative Risk Model (BN-RRM) to calculate the ecological risks to the South River and upper Shenandoah River study area. Four biological endpoints (smallmouth bass, white sucker, Belted Kingfisher, and Carolina Wren) and 4 abiotic endpoints (Fishing River Use, Swimming River Use, Boating River Use, and Water Quality Standards) were included in this risk assessment, based on stakeholder input. Although mercury (Hg) contamination was the original impetus for the site being remediated, other chemical and physical stressors were evaluated. There were 3 primary conclusions from the BN-RRM results. First, risk varies according to location, type and quality of habitat, and exposure to stressors within the landscape. The patterns of risk can be evaluated with reasonable certitude. Second, overall risk to abiotic endpoints was greater than overall risk to biotic endpoints. By including both biotic and abiotic endpoints, we are able to compare risk to endpoints that represent a wide range of stakeholder values. Third, whereas Hg reduction is the regulatory priority for the South River, Hg is not the only stressor driving risk to the endpoints. Ecological and habitat stressors contribute risk to the endpoints and should be considered when managing this site. This research provides the foundation for evaluating the risks of multiple stressors of the South River to a variety of endpoints. From this foundation, tools for the evaluation of management options and an adaptive management tools have been forged. Integr Environ Assess Manag 2017;13:85-99. © 2016 SETAC.
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S. Balbi
2015-10-01
Full Text Available 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.
Schaffner, Donald W; Bowman, James P; English, Donald J; Fischler, George E; Fuls, Janice L; Krowka, John F; Kruszewski, Francis H
2014-04-01
There are conflicting reports on whether antibacterial hand hygiene products are more effective than nonantibacterial products in reducing bacteria on hands and preventing disease. This research used new laboratory data, together with simulation techniques, to compare the ability of nonantibacterial and antibacterial products to reduce shigellosis risk. One hundred sixtythree subjects were used to compare five different hand treatments: two nonantibacterial products and three antibacterial products, i.e., 0.46% triclosan, 4% chlorhexidine gluconate, or 62% ethyl alcohol. Hands were inoculated with 5.5 to 6 log CFU Shigella; the simulated food handlers then washed their hands with one of the five products before handling melon balls. Each simulation scenario represented an event in which 100 people would be exposed to Shigella from melon balls that had been handled by food workers with Shigella on their hands. Analysis of experimental data showed that the two nonantibacterial treatments produced about a 2-log reduction on hands. The three antibacterial treatments showed log reductions greater than 3 but less than 4 on hands. All three antibacterial treatments resulted in statistically significantly lower concentration on the melon balls relative to the nonantibacterial treatments. A simulation that assumed 1 million Shigella bacteria on the hands and the use of a nonantibacterial treatment predicted that 50 to 60 cases of shigellosis would result (of 100 exposed). Each of the antibacterial treatments was predicted to result in an appreciable number of simulations for which the number of illness cases would be 0, with the most common number of illness cases being 5 (of 100 exposed). These effects maintained statistical significance from 10(6) Shigella per hand down to as low as 100 Shigella per hand, with some evidence to support lower levels. This quantitative microbial risk assessment shows that antibacterial hand treatments can significantly reduce Shigella risk.
Fire risk in San Diego County, California: A weighted Bayesian model approach
Kolden, Crystal A.; Weigel, Timothy J.
2007-01-01
Fire risk models are widely utilized to mitigate wildfire hazards, but models are often based on expert opinions of less understood fire-ignition and spread processes. In this study, we used an empirically derived weights-of-evidence model to assess what factors produce fire ignitions east of San Diego, California. We created and validated a dynamic model of fire-ignition risk based on land characteristics and existing fire-ignition history data, and predicted ignition risk for a future urbanization scenario. We then combined our empirical ignition-risk model with a fuzzy fire behavior-risk model developed by wildfire experts to create a hybrid model of overall fire risk. We found that roads influence fire ignitions and that future growth will increase risk in new rural development areas. We conclude that empirically derived risk models and hybrid models offer an alternative method to assess current and future fire risk based on management actions.
Monitoring of Microbial Loads During Long Duration Missions as a Risk Reduction Tool
Roman, Monsi C.
2011-01-01
Humans have been exploring space for more than 40 years. For all those years microorganisms have accompanied, first un-manned spacecraft/cargo and later manned vessels. Microorganisms are everywhere on Earth, could easily adapt to new environments and/or can rapidly mutate to survive in very harsh conditions. Their presence in spacecraft and cargo have caused a few inconveniences over the years of humans spaceflight, ranging from crew health, life support systems challenges and material degradation. The sterilization of spacecraft that will host humans in long duration mission would be a costly operation that will not provide a long-term solution to the microbial colonization of the vessels. As soon as a human is exposed to the spacecraft, during the mission, microorganisms will start to populate the new environment. As the hum an presence in space increases in length, the risk from the microbial load, to hardware and crew will also increase. Mitigation of this risk includes several different strategies that will include minimizing the microbial load (in numbers and diversity) and monitoring. This presentation will provide a list of the risk mitigation strategies that should be implemented during ground processing, and during the mission. It will also discuss the areas that should be discussed before an effective in-flight microbial monitoring regimen is implemented. Microbial monitoring technologies will also be presented.
Conditionality and risk for the pedestrian: modelling with the Bayesian networks.
Gaymard, Sandrine; Tiplica, Teodor
2015-01-01
The conditional script questionnaire (CSQ) makes possible to study the conditions under which drivers find it legitimate to transgress the Highway Code. In this paper, we propose to analyse conditional respect towards the pedestrian with a new methodology based on Bayesian networks (BN). This methodology is designed to give a useful decision support tool for the analyst. Starting from data encoded in the CSQ, we use structure learning algorithms in order to build a BN. Then, we exploit it for two purposes: to extract new knowledge about the main topics expressed in the CSQ and to make inferences. This methodology helps to better understand the behaviour of drivers interacting with pedestrians and what might be the cause of their decisions of legitimate transgressions. The efficiency of the methodology proposed here is illustrated and a context-dependent 'mapping' of the legitimate transgressions established.
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
A new approach for supply chain risk management: Mapping SCOR into Bayesian network
Mahdi Abolghasemi; Vahid Khodakarami; Hamid Tehranifard
2015-01-01
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...
Directory of Open Access Journals (Sweden)
David Ardia
2016-03-01
Full Text Available We investigate the direct connection between the uncertainty related to estimated stable ratios of stock prices and risk and return of two pairs trading strategies: a conditional statistical arbitrage method and an implicit arbitrage one. A simulation-based Bayesian procedure is introduced for predicting stable stock price ratios, defined in a cointegration model. Using this class of models and the proposed inferential technique, we are able to connect estimation and model uncertainty with risk and return of stock trading. In terms of methodology, we show the effect that using an encompassing prior, which is shown to be equivalent to a Jeffreys’ prior, has under an orthogonal normalization for the selection of pairs of cointegrated stock prices and further, its effect for the estimation and prediction of the spread between cointegrated stock prices. We distinguish between models with a normal and Student t distribution since the latter typically provides a better description of daily changes of prices on financial markets. As an empirical application, stocks are used that are ingredients of the Dow Jones Composite Average index. The results show that normalization has little effect on the selection of pairs of cointegrated stocks on the basis of Bayes factors. However, the results stress the importance of the orthogonal normalization for the estimation and prediction of the spread—the deviation from the equilibrium relationship—which leads to better results in terms of profit per capital engagement and risk than using a standard linear normalization.
Foodborne microbial risks in the press: the framing of listeriosis in Canadian newspapers.
Gauthier, Elisabeth
2011-03-01
Outbreaks of foodborne illness generally receive abundant print media coverage. However, the framing of outbreaks and representations of foodborne pathogens in the media discourse are not necessarily homogeneous. Drawing on previous research on media coverage of emerging diseases and on the conceptual tools of framing theory, this paper explores the diversity of frames and representations used in the media coverage of two listeriosis outbreaks that occurred in Canada in fall 2008. In the dominant war against microbes frame, microbes are portrayed as posing serious risks that call for stringent control measures. This frame coexists with other frames which rather emphasize economic, ecosystem or nutrition issues and which are supported by representations of microbial risks that either mitigate these risks, present them as inevitable or as less serious than others. The implications of these observations for the public understanding of foodborne microbial risks are discussed.
2012-07-31
... 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: Notice of availability. SUMMARY: The Food Safety and Inspection Service (FSIS) and Environmental...
2011-11-01
... AGENCY External Peer Review Meeting for Draft Microbial Risk Assessment Guideline: Pathogenic... attend this peer review meeting as observers. Time will be set aside at the meeting for observers to give... the draft document, EPA intends to consider the comments from the external peer review meeting,...
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......, where all the CPMs were analyzed using one single input distribution of concentrations at retail, and the same dose-response relationship. It was found that, between CPMs, there may be a considerable difference in the estimated probability of illness per serving. However, the estimated relative risk...... reductions are less different for scenarios modeling the implementation of control measures. For control measures affecting the Campylobacter prevalence, the relative risk is proportional irrespective of the CPM used. However, for control measures affecting the concentration the CPMs show some difference...
Bashari, Hossein; Naghipour, Ali Asghar; Khajeddin, Seyed Jamaleddin; Sangoony, Hamed; Tahmasebi, Pejman
2016-09-01
Identifying areas that have a high risk of burning is a main component of fire management planning. Although the available tools can predict the fire risks, these are poor in accommodating uncertainties in their predictions. In this study, we accommodated uncertainty in wildfire prediction using Bayesian belief networks (BBNs). An influence diagram was developed to identify the factors influencing wildfire in arid and semi-arid areas of Iran, and it was populated with probabilities to produce a BBNs model. The behavior of the model was tested using scenario and sensitivity analysis. Land cover/use, mean annual rainfall, mean annual temperature, elevation, and livestock density were recognized as the main variables determining wildfire occurrence. The produced model had good accuracy as its ROC area under the curve was 0.986. The model could be applied in both predictive and diagnostic analysis for answering "what if" and "how" questions. The probabilistic relationships within the model can be updated over time using observation and monitoring data. The wildfire BBN model may be updated as new knowledge emerges; hence, it can be used to support the process of adaptive management.
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.
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
Lee, Heeyoung; Kim, Kyunga; Choi, Kyoung-Hee; Yoon, Yohan
2015-09-01
This study quantitatively assessed the microbial risk of Staphylococcus aureus in cheese in Korea. The quantitative microbial risk assessment was carried out for natural and processed cheese from factory to consumption. Hazards for S. aureus in cheese were identified through the literature. For exposure assessment, the levels of S. aureus contamination in cheeses were evaluated, and the growth of S. aureus was predicted by predictive models at the surveyed temperatures, and at the time of cheese processing and distribution. For hazard characterization, a dose-response model for S. aureus was found, and the model was used to estimate the risk of illness. With these data, simulation models were prepared with @RISK (Palisade Corp., Ithaca, NY) to estimate the risk of illness per person per day in risk characterization. Staphylococcus aureus cell counts on cheese samples from factories and markets were below detection limits (0.30-0.45 log cfu/g), and pert distribution showed that the mean temperature at markets was 6.63°C. Exponential model [P=1 - exp(7.64×10(-8) × N), where N=dose] for dose-response was deemed appropriate for hazard characterization. Mean temperature of home storage was 4.02°C (log-logistic distribution). The results of risk characterization for S. aureus in natural and processed cheese showed that the mean values for the probability of illness per person per day were higher in processed cheese (mean: 2.24×10(-9); maximum: 7.97×10(-6)) than in natural cheese (mean: 7.84×10(-10); maximum: 2.32×10(-6)). These results indicate that the risk of S. aureus-related foodborne illness due to cheese consumption can be considered low under the present conditions in Korea. In addition, the developed stochastic risk assessment model in this study can be useful in establishing microbial criteria for S. aureus in cheese.
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.)
Energy Technology Data Exchange (ETDEWEB)
Trucco, P. [Department of Management, Economics and Industrial Engineering-Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milan (Italy)], E-mail: paolo.trucco@polimi.it; Cagno, E. [Department of Management, Economics and Industrial Engineering-Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milan (Italy); Ruggeri, F. [CNR IMATI, via E.Bassini, 15, I-20133 Milan (Italy); Grande, O. [Department of Management, Economics and Industrial Engineering-Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milan (Italy)
2008-06-15
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.
An urban flood risk assessment method using the Bayesian Network approach
DEFF Research Database (Denmark)
Åström, Helena Lisa Alexandra
to flooding, because these areas comprise large amounts of valuable assets. Flooding in urban areas can grow into significant disruptions and national threats unless appropriate flood risk management (FRM) plans are developed and timely adaptation options are implemented. FRM is a well-established process....... Currently, FRA is mainly based on single hazard events, but with expected climate change impacts there may be a need to include several hazards into FRA to assure that risk is described correctly for identification of important adaptation. This thesis shows that IDs may serve as a good approach...
Directory of Open Access Journals (Sweden)
Ying Cao
Full Text Available Childhood acute lymphoblastic leukemia (ALL is a condition that arises from complex etiologies. The absence of consistent environmental risk factors and the presence of modest familial associations suggest ALL is a complex trait with an underlying genetic component. The identification of genetic factors associated with disease is complicated by complex genetic covariance structures and multiple testing issues. Both issues can be resolved with appropriate Bayesian variable selection methods. The present study was undertaken to extend our hierarchical Bayesian model for case-parent triads to incorporate single nucleotide polymorphisms (SNPs and incorporate the biological grouping of SNPs within genes. Based on previous evidence that genetic variation in the folate metabolic pathway influences ALL risk, we evaluated 128 tagging SNPs in 16 folate metabolic genes among 118 ALL case-parent triads recruited from the Texas Children's Cancer Center (Houston, TX between 2003 and 2010. We used stochastic search gene suggestion (SSGS in hierarchical Bayesian models to evaluate the association between folate metabolic SNPs and ALL. Using Bayes factors among these variants in childhood ALL case-parent triads, two SNPs were identified with a Bayes factor greater than 1. There was evidence that the minor alleles of NOS3 rs3918186 (OR = 2.16; 95% CI: 1.51-3.15 and SLC19A1 rs1051266 (OR = 2.07; 95% CI: 1.25-3.46 were positively associated with childhood ALL. Our findings are suggestive of the role of inherited genetic variation in the folate metabolic pathway on childhood ALL risk, and they also suggest the utility of Bayesian variable selection methods in the context of case-parent triads for evaluating the role of SNPs on disease risk.
Microbial risk assessment of Vibrio spp. in seafood products in Mexico
Directory of Open Access Journals (Sweden)
Karla M López-Hernández
2014-05-01
Full Text Available Food-borne diseases are among the major public health problems that currently exist. Microbiological risk assessment is a process used to evaluate the hidden hazards in food, the likelihood of exposure to these hazards and their impact on public health. Risk assessment is performed in four steps: hazard identification, hazard characterization, assessment of exposure and risk characterization. According to the process/response microbial risk assessment is classified in two categories, qualitative and quantitative. The aim of this review is to underline the importance of implementing assessments in seafood that is usually consumed raw, strengthening access to good quality and safe food for the consumer’s benefit and to stress the necessity of microbiological risks assessments in Mexico.
[Microbial risk assessment of Vibrio spp. in seafood products in Mexico].
López-Hernández, Karla M; Pardío-Sedas, Violeta T; Williams, José de Jesús
2014-01-01
Food-borne diseases are among the major public health problems that currently exist. Microbiological risk assessment is a process used to evaluate the hidden hazards in food, the likelihood of exposure to these hazards and their impact on public health. Risk assessment is performed in four steps: hazard identification, hazard characterization, assessment of exposure and risk characterization. According to the process/response microbial risk assessment is classified in two categories, qualitative and quantitative. The aim of this review is to underline the importance of implementing assessments in seafood that is usually consumed raw, strengthening access to good quality and safe food for the consumer's benefit and to stress the necessity of microbiological risks assessments in Mexico.
Microbial risks associated with exposure to pathogens in contaminated urban flood water.
ten Veldhuis, J A E; Clemens, F H L R; Sterk, G; Berends, B R
2010-05-01
Urban flood incidents induced by heavy rainfall in many cases entail flooding of combined sewer systems. These flood waters are likely to be contaminated and may pose potential health risks to citizens exposed to pathogens in these waters. The purpose of this study was to evaluate the microbial risk associated with sewer flooding incidents. Concentrations of Escherichia coli, intestinal enterococci and Campylobacter were measured in samples from 3 sewer flooding incidents. The results indicate faecal contamination: faecal indicator organism concentrations were similar to those found in crude sewage under high-flow conditions and Campylobacter was detected in all samples. Due to infrequent occurrence of such incidents only a small number of samples could be collected; additional data were collected from controlled flooding experiments and analyses of samples from combined sewers. The results were used for a screening-level quantitative microbial risk assessment (QMRA). Calculated annual risks values vary from 5 x 10(-6) for Cryptosporidium assuming a low exposure scenario to 0.03 for Giardia assuming a high exposure scenario. The results of this screening-level risk assessment justify further research and data collection to allow more reliable quantitative assessment of health risks related to contaminated urban flood waters.
2011-07-26
... part of the comment that is placed in the public docket and made available on the Internet. If you... document addresses the full range of microbial risk assessment topics: Definition of the roles...
Wei, Z; Zhang, X L; Rao, H X; Wang, H F; Wang, X; Qiu, L X
2016-06-01
Under the available data gathered from a coronary study questionnaires with 10 792 cases, this article constructs a Bayesian network model based on the tabu search algorithm and calculates the conditional probability of each node, using the Maximum-likelihood. Pros and cons of the Bayesian network model are evaluated to compare against the logistic regression model in the analysis of coronary factors. Applicability of this network model in clinical study is also investigated. Results show that Bayesian network model can reveal the complex correlations among influencing factors on the coronary and the relationship with coronary heart diseases. Bayesian network model seems promising and more practical than the logistic regression model in analyzing the influencing factors of coronary heart disease.
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.
Directory of Open Access Journals (Sweden)
Mohammed Hussni O
2010-06-01
Full Text Available Abstract Background Cryptosporidium parvum is one of the most important biological contaminants in drinking water that produces life threatening infection in people with compromised immune systems. Dairy calves are thought to be the primary source of C. parvum contamination in watersheds. Understanding the spatial and temporal variation in the risk of C. parvum infection in dairy cattle is essential for designing cost-effective watershed management strategies to protect drinking water sources. Crude and Bayesian seasonal risk estimates for Cryptosporidium in dairy calves were used to investigate the spatio-temporal dynamics of C. parvum infection on dairy farms in the New York City watershed. Results Both global (Global Moran's I and specific (SaTScan cluster analysis methods revealed a significant (p C. parvum infection in all herds in the summer (p = 0.002, compared to the rest of the year. Bayesian estimates did not show significant spatial autocorrelation in any season. Conclusions Although we were not able to identify seasonal clusters using Bayesian approach, crude estimates highlighted both temporal and spatial clusters of C. parvum infection in dairy herds in a major watershed. We recommend that further studies focus on the factors that may lead to the presence of C. parvum clusters within the watershed, so that monitoring and prevention practices such as stream monitoring, riparian buffers, fencing and manure management can be prioritized and improved, to protect drinking water supplies and public health.
Danyluk, Michelle D; Schaffner, Donald W
2011-05-01
This project was undertaken to relate what is known about the behavior of Escherichia coli O157:H7 under laboratory conditions and integrate this information to what is known regarding the 2006 E. coli O157:H7 spinach outbreak in the context of a quantitative microbial risk assessment. The risk model explicitly assumes that all contamination arises from exposure in the field. Extracted data, models, and user inputs were entered into an Excel spreadsheet, and the modeling software @RISK was used to perform Monte Carlo simulations. The model predicts that cut leafy greens that are temperature abused will support the growth of E. coli O157:H7, and populations of the organism may increase by as much a 1 log CFU/day under optimal temperature conditions. When the risk model used a starting level of -1 log CFU/g, with 0.1% of incoming servings contaminated, the predicted numbers of cells per serving were within the range of best available estimates of pathogen levels during the outbreak. The model predicts that levels in the field of -1 log CFU/g and 0.1% prevalence could have resulted in an outbreak approximately the size of the 2006 E. coli O157:H7 outbreak. This quantitative microbial risk assessment model represents a preliminary framework that identifies available data and provides initial risk estimates for pathogenic E. coli in leafy greens. Data gaps include retail storage times, correlations between storage time and temperature, determining the importance of E. coli O157:H7 in leafy greens lag time models, and validation of the importance of cross-contamination during the washing process.
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
DEFF Research Database (Denmark)
Daley, Kiley; Jamieson, Rob; Rainham, Daniel
2017-01-01
. This review offers a conceptual framework and evaluation of current knowledge to enable the first microbial risk assessment of exposure scenarios associated with food-harvesting and recreational activities in Arctic communities, where simplified wastewater systems are being operated.......Wastewater management in Canadian Arctic communities is influenced by several geographical factors including climate, remoteness, population size, and local food-harvesting practices. Most communities use trucked collection services and basic treatment systems, which are capable of only low...... into the terrestrial and aquatic environment at random times. Northern communities rely heavily on their local surroundings as a source of food, drinking water, and recreation, thus creating the possibility of human exposure to wastewater effluent. Human exposure to microbial hazards present in municipal wastewater...
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
新家, 健精
2013-01-01
© 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
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.
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.
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}.
Verdin, A.; Rajagopalan, B.; Kleiber, W.; Podesta, G. P.; Bert, F.
2015-12-01
We present a space-time stochastic weather generator for daily precipitation and temperature, developed within a Bayesian hierarchical framework (hereafter BayGEN). This framework offers a unique advantage: it provides robust estimation of uncertainty that is typically under-represented in traditional weather generators. Realistic estimates of uncertainty are of utmost importance for studying climate variability and change, impacts on land use, and crop production. BayGEN is applied to a network of weather stations in the Salado basin of the Argentine Pampas, a region that saw immense agricultural expansion towards climatically marginal (i.e., semi-arid) regions, in part due to significant trends in annual precipitation from 1970-2000. Since the turn of the century, observed conditions suggest a decrease in precipitation, which begs the question: "Are the existing agricultural production systems viable in a drier future?" The use of process based (i.e., hydrologic, crop simulation) models in conjunction with BayGEN will allow for complete analysis of the system's response to an ensemble of plausible future scenarios. Precipitation occurrence at each site is modeled at the first level of hierarchy using probit regression with covariates for seasonality, where the latent process is Gaussian -- positivity in the latent process implies occurrence. The precipitation amounts are modeled using a transformed gamma regression (i.e., gamma generalized linear model), similarly with seasonality covariates. Minimum and maximum temperatures are conditional on precipitation occurrence and are decomposed into two processes: (i) climate -- linear regressions on seasonality covariates, and (ii) weather -- realizations from mean-zero Gaussian random fields. The use of seasonality covariates allows the generation of daily weather sequences conditioned on seasonal forecasts or projected multi-annual trends, an increasingly important practice for risk assessment in climatically marginal
Beck-Wörner, Christian; Raso, Giovanna; Vounatsou, Penelope; N'Goran, Eliézer K; Rigo, Gergely; Parlow, Eberhard; Utzinger, Jürg
2007-05-01
An important epidemiologic feature of schistosomiasis is the focal distribution of the disease. Thus, the identification of high-risk communities is an essential first step for targeting interventions in an efficient and cost-effective manner. We used a remotely-sensed digital elevation model (DEM), derived hydrologic features (i.e., stream order, and catchment area), and fitted Bayesian geostatistical models to assess associations between environmental factors and infection with Schistosoma mansoni among more than 4,000 school children from the region of Man in western Côte d'Ivoire. At the unit of the school, we found significant correlations between the infection prevalence of S. mansoni and stream order of the nearest river, water catchment area, and altitude. In conclusion, the use of a freely available 90 m high-resolution DEM, geographic information system applications, and Bayesian spatial modeling facilitates risk prediction for S. mansoni, and is a powerful approach for risk profiling of other neglected tropical diseases that are pervasive in the developing world.
Vázquez-Polo, Francisco-Jose; Moreno, Elías; Negrín, Miguel A; Martel, Maria
2015-04-01
In most cases, including those of discrete random variables, statistical meta-analysis is carried out using the normal random effect model. The authors argue that normal approximation does not always properly reflect the underlying uncertainty of the original discrete data. Furthermore, in the presence of rare events the results from this approximation can be very poor. This review proposes a Bayesian meta-analysis to address binary outcomes from sparse data and also introduces a simple way to examine the sensitivity of the quantities of interest in the meta-analysis with respect to the structure dependence selected. The findings suggest that for binary outcomes data it is possible to develop a Bayesian procedure, which can be directly applied to sparse data without ad hoc corrections. By choosing a suitable class of linking distributions, the authors found that a Bayesian robustness study can be easily implemented. For illustrative purposes, an example with real data is analyzed using the proposed Bayesian meta-analysis for binomial sparse data.
R.W. Strachan (Rodney); H.K. van Dijk (Herman)
2008-01-01
textabstractA Bayesian model averaging procedure is presented that makes use of a finite mixture of many model structures within the class of vector autoregressive (VAR) processes. It is applied to two empirical issues. First, stability of the Great Ratios in U.S. macro-economic time series is inves
Early infancy microbial and metabolic alterations affect risk of childhood asthma.
Arrieta, Marie-Claire; Stiemsma, Leah T; Dimitriu, Pedro A; Thorson, Lisa; Russell, Shannon; Yurist-Doutsch, Sophie; Kuzeljevic, Boris; Gold, Matthew J; Britton, Heidi M; Lefebvre, Diana L; Subbarao, Padmaja; Mandhane, Piush; Becker, Allan; McNagny, Kelly M; Sears, Malcolm R; Kollmann, Tobias; Mohn, William W; Turvey, Stuart E; Finlay, B Brett
2015-09-30
Asthma is the most prevalent pediatric chronic disease and affects more than 300 million people worldwide. Recent evidence in mice has identified a "critical window" early in life where gut microbial changes (dysbiosis) are most influential in experimental asthma. However, current research has yet to establish whether these changes precede or are involved in human asthma. We compared the gut microbiota of 319 subjects enrolled in the Canadian Healthy Infant Longitudinal Development (CHILD) Study, and show that infants at risk of asthma exhibited transient gut microbial dysbiosis during the first 100 days of life. The relative abundance of the bacterial genera Lachnospira, Veillonella, Faecalibacterium, and Rothia was significantly decreased in children at risk of asthma. This reduction in bacterial taxa was accompanied by reduced levels of fecal acetate and dysregulation of enterohepatic metabolites. Inoculation of germ-free mice with these four bacterial taxa ameliorated airway inflammation in their adult progeny, demonstrating a causal role of these bacterial taxa in averting asthma development. These results enhance the potential for future microbe-based diagnostics and therapies, potentially in the form of probiotics, to prevent the development of asthma and other related allergic diseases in children.
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
Vicedo-Cabrera, Ana M; Biggeri, Annibale; Grisotto, Laura; Barbone, Fabio; Catelan, Dolores
2013-11-01
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/m(3) of NO2 and 4.10 (± 2.71) µg/m(3) 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.
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.
Bernardo, Jose M
2000-01-01
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critica
Quantitative Microbial Risk Assessment for Clostridium perfringens in Natural and Processed Cheeses.
Lee, Heeyoung; Lee, Soomin; Kim, Sejeong; Lee, Jeeyeon; Ha, Jimyeong; Yoon, Yohan
2016-08-01
This study evaluated the risk of Clostridium perfringens (C. perfringens) foodborne illness from natural and processed cheeses. Microbial risk assessment in this study was conducted according to four steps: hazard identification, hazard characterization, exposure assessment, and risk characterization. The hazard identification of C. perfringens on cheese was identified through literature, and dose response models were utilized for hazard characterization of the pathogen. For exposure assessment, the prevalence of C. perfringens, storage temperatures, storage time, and annual amounts of cheese consumption were surveyed. Eventually, a simulation model was developed using the collected data and the simulation result was used to estimate the probability of C. perfringens foodborne illness by cheese consumption with @RISK. C. perfringens was determined to be low risk on cheese based on hazard identification, and the exponential model (r = 1.82×10(-11)) was deemed appropriate for hazard characterization. Annual amounts of natural and processed cheese consumption were 12.40±19.43 g and 19.46±14.39 g, respectively. Since the contamination levels of C. perfringens on natural (0.30 Log CFU/g) and processed cheeses (0.45 Log CFU/g) were below the detection limit, the initial contamination levels of natural and processed cheeses were estimated by beta distribution (α 1 = 1, α 2 = 91; α 1 = 1, α 2 = 309)×uniform distribution (a = 0, b = 2; a = 0, b = 2.8) to be -2.35 and -2.73 Log CFU/g, respectively. Moreover, no growth of C. perfringens was observed for exposure assessment to simulated conditions of distribution and storage. These data were used for risk characterization by a simulation model, and the mean values of the probability of C. perfringens foodborne illness by cheese consumption per person per day for natural and processed cheeses were 9.57×10(-14) and 3.58×10(-14), respectively. These results indicate that probability of C. perfringens foodborne illness
Eleye-Datubo, A G; Wall, A; Wang, J
2008-02-01
The incorporation of the human element into a probabilistic risk-based model is one that requires a possibilistic integration of appropriate techniques and/or that of vital inputs of linguistic nature. While fuzzy logic is an excellent tool for such integration, it tends not to cross its boundaries of possibility theory, except via an evidential reasoning supposition. Therefore, a fuzzy-Bayesian network (FBN) is proposed to enable a bridge to be made into a probabilistic setting of the domain. This bridge is formalized by way of the mass assignment theory. A framework is also proposed for its use in maritime safety assessment. Its implementation has been demonstrated in a maritime human performance case study that utilizes performance-shaping factors as the input variables of this groundbreaking FBN risk model.
Colonna, M
2006-12-01
Bayesian estimates of disease relative risks is currently the gold standard in disease mapping when the disease is rare and/or when the geographical area is small. Its use has become quite easy with adhoc software. However, the implicit mechanisms of the choices made by the user must be clearly identified. We were interested here in the consequences of the choice of the hyper a priori parameters. We have compared results obtained using various hyper a priori parameters. The consequences of these choices are illustrated through the example of the incidence of bladder cancer among men in the urban area of Grenoble. We show that the risks can appear weak from a statistical point of view but important from an epidemiologic point of view in the presentation of the results.
Villalba, Jesús
2015-01-01
In this document we are going to derive the equations needed to implement a Variational Bayes estimation of the parameters of the simplified probabilistic linear discriminant analysis (SPLDA) model. This can be used to adapt SPLDA from one database to another with few development data or to implement the fully Bayesian recipe. Our approach is similar to Bishop's VB PPCA.
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.
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
Buse, Helen Y; Schoen, Mary E; Ashbolt, Nicholas J
2012-03-15
While it is well-established that Legionella are able to colonize engineered water systems, the number of interacting factors contributing to their occurrence, proliferation, and persistence are unclear. This review summarizes current methods used to detect and quantify legionellae as well as the current knowledge of engineered water system characteristics that both favour and promote legionellae growth. Furthermore, the use of quantitative microbial risk assessment (QMRA) models to predict potentially critical human exposures to legionellae are also discussed. Understanding the conditions favouring Legionella occurrence in engineered systems and their overall ecology (growth in these systems/biofilms, biotic interactions and release) will aid in developing new treatment technologies and/or systems that minimize or eliminate human exposure to potentially pathogenic legionellae.
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)
Vinnerås, Björn; Schönning, Caroline; Nordin, Annika
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(5) cfu ml-1 were found in the condensate water throughout the system, while in the gas between 10 and 100 cfu m-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-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.
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.
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
Hedlund, Jonas
2014-01-01
This paper introduces private sender information into a sender-receiver game of Bayesian persuasion with monotonic sender preferences. I derive properties of increasing differences related to the precision of signals and use these to fully characterize the set of equilibria robust to the intuitive criterion. In particular, all such equilibria are either separating, i.e., the sender's choice of signal reveals his private information to the receiver, or fully disclosing, i.e., the outcome of th...
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)
Evangelina López de Maturana
Full Text Available The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL, a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.
Martins, Marcelo Ramos; Schleder, Adriana Miralles; Droguett, Enrique López
2014-12-01
This article presents an iterative six-step risk analysis methodology based on hybrid Bayesian networks (BNs). In typical risk analysis, systems are usually modeled as discrete and Boolean variables with constant failure rates via fault trees. Nevertheless, in many cases, it is not possible to perform an efficient analysis using only discrete and Boolean variables. The approach put forward by the proposed methodology makes use of BNs and incorporates recent developments that facilitate the use of continuous variables whose values may have any probability distributions. Thus, this approach makes the methodology particularly useful in cases where the available data for quantification of hazardous events probabilities are scarce or nonexistent, there is dependence among events, or when nonbinary events are involved. The methodology is applied to the risk analysis of a regasification system of liquefied natural gas (LNG) on board an FSRU (floating, storage, and regasification unit). LNG is becoming an important energy source option and the world's capacity to produce LNG is surging. Large reserves of natural gas exist worldwide, particularly in areas where the resources exceed the demand. Thus, this natural gas is liquefied for shipping and the storage and regasification process usually occurs at onshore plants. However, a new option for LNG storage and regasification has been proposed: the FSRU. As very few FSRUs have been put into operation, relevant failure data on FSRU systems are scarce. The results show the usefulness of the proposed methodology for cases where the risk analysis must be performed under considerable uncertainty.
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
Fai, Patricia Bi; Grant, Alastair
2010-11-01
Rapid microscale toxicity tests make it possible to screen large numbers of compounds and greatly simplify toxicity identification evaluation and other effect directed chemical analyses of effluents or environmental samples. Tests using Vibrio fischeri (such as Microtox®) detect toxicants that cause non-specific narcosis, but are insensitive to other important classes of contaminants. The microbial assay for risk assessment (MARA) is a 24 h multi-species test that seeks to address this problem by using a battery of ten bacteria and a fungus. But there has been little independent evaluation of this test, and there is no published information on its sensitivity to pesticides. Here, we assess the performance of MARA using a range of toxicants including reference chemicals, fungicides and environmental samples. Mean MARA microbial toxic concentrations and IC(20)s (20% Inhibitory concentrations) indicate the toxicant concentrations affecting the more sensitive micro-organisms, while the mean IC(50) (50% Inhibitory concentration) was found to be the concentration that was toxic to most MARA species. For the two fungicides tested, the yeast (Pichia anomalia) was the most sensitive of the ten MARA species, and was more sensitive than the nine other yeasts tested. The test may be particularly valuable for work with fungicides. Mean MARA IC(50)s were comparable to values for nine other yeast species and the lowest individual IC(50)s for each toxicant were comparable to reported IC(50)s for Daphnia magna, Selenastrum capricornutum and Microtox® bioassays. MARA organisms exhibited more variable sensitivities, with the most sensitive organism being different for different samples, enhancing the likelihood of toxicity detection and giving a toxicity "fingerprint" that may help identify toxicants. The test, therefore, has great potential and would be valuable for ecotoxicological testing of pollutants.
Chen, Yuhuan; Dennis, Sherri B; Hartnett, Emma; Paoli, Greg; Pouillot, Régis; Ruthman, Todd; Wilson, Margaret
2013-03-01
Stakeholders in the system of food safety, in particular federal agencies, need evidence-based, transparent, and rigorous approaches to estimate and compare the risk of foodborne illness from microbial and chemical hazards and the public health impact of interventions. FDA-iRISK (referred to here as iRISK), a Web-based quantitative risk assessment system, was developed to meet this need. The modeling tool enables users to assess, compare, and rank the risks posed by multiple food-hazard pairs at all stages of the food supply system, from primary production, through manufacturing and processing, to retail distribution and, ultimately, to the consumer. Using standard data entry templates, built-in mathematical functions, and Monte Carlo simulation techniques, iRISK integrates data and assumptions from seven components: the food, the hazard, the population of consumers, process models describing the introduction and fate of the hazard up to the point of consumption, consumption patterns, dose-response curves, and health effects. Beyond risk ranking, iRISK enables users to estimate and compare the impact of interventions and control measures on public health risk. iRISK provides estimates of the impact of proposed interventions in various ways, including changes in the mean risk of illness and burden of disease metrics, such as losses in disability-adjusted life years. Case studies for Listeria monocytogenes and Salmonella were developed to demonstrate the application of iRISK for the estimation of risks and the impact of interventions for microbial hazards. iRISK was made available to the public at http://irisk.foodrisk.org in October 2012.
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...
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
2012-01-01
InflamAndOtherInfection” Due to the relationship between antibiotic use and the development of C-Diff colitis, this risk factor was critical for the data analysis...available clinical and disease-specific factors can, in a codependent manner, collectively influence postoperative outcomes through preoperative risk
Research on Risk Management of Engineering Quality Based on Bayesian Network%基于贝叶斯网络的工程项目质量风险管理
Institute of Scientific and Technical Information of China (English)
胡书香; 莫俊文; 赵延龙
2013-01-01
提出基于贝叶斯网络的工程项目质量风险管理.首先,对工程质量形成过程中的风险因素进行识别,其次进行风险分析确定主要风险因素,构建贝叶斯网络结构.依据问卷调查中得到的数据和专家经验确定贝叶斯网络中的参数,以此为基础对工程项目质量进行风险评估、风险诊断,分析确定影响工程质量的关键风险因素.最后,依据分析得到的结果进行风险控制.%The quality of project is lifeblood of entire project, and there are a lot of risks in formation of quality. In order to effectively manage engineer quality risk, this paper proposes the quality risk management model based on Bayesian network. Firstly, risk factors in formation of the quality are identified, then risks are analyzed to identify the main risk factors and to build Bayesian network structure. According to survey data and expert experience, the parameters of Bayesian network, and based on these, the risk assessment and risk diagnostic for the quality of the project are carried out and to determine the key risk factors that affect quality of project are determined. Finally, the risk is controlled in accordance with the result
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) ...
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.
2016-01-01
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 Escherichia coli, a human-associated Bacteroidetes microbial source tracking (MST) marker, enterovirus, norovirus, Campylobacter, and Cryptosporidium 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
Ahasan, Md Shamim; Rahman, Md Siddiqur; Rahman, A K M Anisur; Berkvens, Dirk
2017-01-01
A cross-sectional study was carried out to estimate the true prevalence of Brucella spp. and identify allied risk factors/indicators associated with brucellosis in the Dinajpur and Mymensingh districts of Bangladesh. A total 320 stratified random blood samples were collected and tested in parallel for Brucella antibodies using Rose Bengal (RBT), slow agglutination (SAT), and indirect and competitive ELISA. In addition, a structured questionnaire was administered to each household herd owner to gather information regarding potential risk factors. Both univariate and multivariate logistic regression analyses were used to identify potential risk factors or indicators at animal level. A Bayesian approach was used to estimate the true prevalence of brucellosis along with the test performances (Se and Sp). The estimated animal level true prevalence in cattle was 9.70 % (95 % CPI 5.0-16 %) and in goat 6.3 % (95 % CPI 2.8-11.0 %). The highest sensitivity was achieved by SAT ranges from 69.6 to 78.9 %, and iELISA was found to be more specific (97.4 to 98.8 %) in comparison with other tests. On the other hand, a significant level of (P Brucella seropositivity was found in cattle that breed naturally compared with those that undergo artificial insemination. In goats, exotic breeds were significantly associated (P Brucella seroprevalence compared with indigenous breeds. Goats with a previous records of abortion and/or retained placenta were also found to have significant levels (P < 0.05). Cows with previous abortion records showed higher odds (18 times) of being seropositive. None of the evaluated tests can be recommended to apply alone for the diagnosis of bovine and caprine brucellosis.
Introduction to Bayesian statistics
Bolstad, William M
2017-01-01
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...
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
2003-01-01
As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.
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
Directory of Open Access Journals (Sweden)
Raphael E Arku
2016-06-01
Full Text Available Under-five mortality is declining in Ghana and many other countries. Very few studies have measured under-five mortality-and its social and environmental risk factors-at fine spatial resolutions, which is relevant for policy purposes. Our aim was to estimate under-five mortality and its social and environmental risk factors at the district level in Ghana.We used 10% random samples of Ghana's 2000 and 2010 National Population and Housing Censuses. We applied indirect demographic methods and a Bayesian spatial model to the information on total number of children ever born and children surviving to estimate under-five mortality (probability of dying by 5 y of age, 5q0 for each of Ghana's 110 districts. We also used the census data to estimate the distributions of households or persons in each district in terms of fuel used for cooking, sanitation facility, drinking water source, and parental education. Median district 5q0 declined from 99 deaths per 1,000 live births in 2000 to 70 in 2010. The decline ranged from 40% in southern districts, where it had been lower in 2000, exacerbating existing inequalities. Primary education increased in men and women, and more households had access to improved water and sanitation and cleaner cooking fuels. Higher use of liquefied petroleum gas for cooking was associated with lower 5q0 in multivariate analysis.Under-five mortality has declined in all of Ghana's districts, but the cross-district inequality in mortality has increased. There is a need for additional data, including on healthcare, and additional environmental and socioeconomic measurements, to understand the reasons for the variations in mortality levels and trends.
Pouillot, Régis; Lubran, Meryl B
2011-06-01
Predictive microbiology models are essential tools to model bacterial growth in quantitative microbial risk assessments. Various predictive microbiology models and sets of parameters are available: it is of interest to understand the consequences of the choice of the growth model on the risk assessment outputs. Thus, an exercise was conducted to explore the impact of the use of several published models to predict Listeria monocytogenes growth during food storage in a product that permits growth. Results underline a gap between the most studied factors in predictive microbiology modeling (lag, growth rate) and the most influential parameters on the estimated risk of listeriosis in this scenario (maximum population density, bacterial competition). The mathematical properties of an exponential dose-response model for Listeria accounts for the fact that the mean number of bacteria per serving and, as a consequence, the highest achievable concentrations in the product under study, has a strong influence on the estimated expected number of listeriosis cases in this context.
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
贝叶斯方法在信用风险度量中的应用研究综述%Bayesian Methods in Credit Risk Measurement: A Survey
Institute of Scientific and Technical Information of China (English)
丁东洋; 周丽莉; 刘乐平
2013-01-01
贝叶斯方法可以有效的处理信用风险度量中常见的数据缺失问题,而且为科学使用专家意见等主观经验提供了有效途径,已被广泛应用于信用风险度量领域.本文从模型构建、估计方法及模型比较三个方面对应用贝叶斯方法度量信用风险的重要文献进行综述,重点关注信用风险的违约相关性和风险蔓延性等最新研究热点,为深入研究信用风险度量问题提供参考,并引起国内风险分析人员对贝叶斯方法的兴趣.%Bayesian methods can effectively deal with the common problem of missing data, and provide a formal way for the scientific use of subjective experience, has been widely used in credit risk measurement. This paper presents a survey of important literature of credit risk measurement using Bayesian methods in three aspects of modeling, estimation methods and model comparison, and focus on the hot issues of default correlation and risk contagion of the latest credit risk research, in order to provide reference for further research in the credit risk measurement, and raise the domestic risk analyzer interest in Bayesian methods.
Applied Bayesian Hierarchical Methods
Congdon, Peter D
2010-01-01
Bayesian methods facilitate the analysis of complex models and data structures. Emphasizing data applications, alternative modeling specifications, and computer implementation, this book provides a practical overview of methods for Bayesian analysis of hierarchical models.
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
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]).
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
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.
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
Raybould, Alan; Kilby, Peter; Graser, Gerson
2013-04-01
Most commercial transgenic crops are genetically engineered to produce new proteins. Studies to assess the risks to human and animal health, and to the environment, from the use of these crops require grams of the transgenic proteins. It is often extremely difficult to produce sufficient purified transgenic protein from the crop. Nevertheless, ample protein of acceptable purity may be produced by over-expressing the protein in microbes such as Escherichia coli. When using microbial proteins in a study for risk assessment, it is essential that their suitability as surrogates for the plant-produced transgenic proteins is established; that is, the proteins are equivalent for the purposes of the study. Equivalence does not imply that the plant and microbial proteins are identical, but that the microbial protein is sufficiently similar biochemically and functionally to the plant protein such that studies using the microbial protein provide reliable information for risk assessment of the transgenic crop. Equivalence is a judgement based on a weight of evidence from comparisons of relevant properties of the microbial and plant proteins, including activity, molecular weight, amino acid sequence, glycosylation and immuno-reactivity. We describe a typical set of methods used to compare proteins in regulatory risk assessments for transgenic crops, and discuss how risk assessors may use comparisons of proteins to judge equivalence.
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.
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.
Microbial colonisation of orthopaedic tourniquets: A potential risk for surgical site infection
Sahu, S. K.; B Tudu; P K Mall
2015-01-01
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 t...
Increased microbial activity in a warmer and wetter climate enhances the risk of coastal hypoxia.
Nydahl, Anna; Panigrahi, Satya; Wikner, Johan
2013-08-01
The coastal zone is the most productive area of the marine environment and the area that is most exposed to environmental drivers associated with human pressures in a watershed. In dark bottle incubation experiments, we investigated the short-term interactive effects of changes in salinity, temperature and riverine dissolved organic matter (rDOM) on microbial respiration, growth and abundance in an estuarine community. An interaction effect was found for bacterial growth, where the assimilation of rDOM increased at higher salinities. A 3 °C rise in the temperature had a positive effect on microbial respiration. A higher concentration of DOM consistently enhanced respiration and bacterial abundance, while an increase in temperature reduced bacterial abundance. The latter result was most likely caused by a positive interaction effect of temperature, salinity and rDOM on the abundance of bacterivorous flagellates. Elevated temperature and precipitation, causing increased discharges of rDOM and an associated lowered salinity, will therefore primarily promote bacterial respiration, growth and bacterivore abundance. Our results suggest a positive net outcome for microbial activity under the projected climate change, driven by different, partially interacting environmental factors. Thus, hypoxia in coastal zones may increase due to enhanced respiration caused by higher temperatures and rDOM discharge acting synergistically.
Bayesian Network Enhanced with Structural Reliability Methods: Methodology
Straub, Daniel; Der Kiureghian, Armen
2012-01-01
We combine Bayesian networks (BNs) and structural reliability methods (SRMs) to create a new computational framework, termed enhanced Bayesian network (eBN), for reliability and risk analysis of engineering structures and infrastructure. BNs are efficient in representing and evaluating complex probabilistic dependence structures, as present in infrastructure and structural systems, and they facilitate Bayesian updating of the model when new information becomes available. On the other hand, SR...
Bayesian analysis of rare events
Energy Technology Data Exchange (ETDEWEB)
Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang
2016-06-01
In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into 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.
Energy Technology Data Exchange (ETDEWEB)
Capra, B.; Le Drogo, J.; Wolff, V. [OXAND S.A., 77 - Avon (France)
2007-07-01
Degradation modeling of concrete structures uses uncertain variables and leads, using reliability assessment, to time dependant evolution of failure probabilities. However, only few data are generally available to feed models leading to two types of uncertainties: an intrinsic one depending on the modeled phenomena and one related to the precision of the measurements. Each new data available is a piece of information which allows updating the initial prediction. In this article, an example of updating process, based on a Bayesian network, is presented and applied on the corrosion risk of a cooling tower in a nuclear plant. (authors)
Incorporation of probiotic bacteria in whey cheese: decreasing the risk of microbial contamination.
Madureira, A Raquel; Pintado, Manuela E; Gomes, Ana M P; Malcata, F Xavier
2011-07-01
For dairy products that are consumed fresh, contamination by spoilage microorganisms and pathogens from the environment is a major concern. Contamination has been associated with a number of outbreaks of foodborne illnesses; however, consistent data pertaining to the microbial safety of whey cheeses specifically have not been reported. Hence, the goals of this research effort were (i) to manufacture a probiotic whey cheese with Bifidobacterium animalis and Lactobacillus casei and (ii) to assess the antimicrobial activity of these probiotics against a set of foodborne pathogens (Listeria innocua, Salmonella Enteritidis, and Staphylococcus aureus) and food spoilage microorganisms (Pseudomonas aeruginosa and Escherichia coli). Three ranges of these microbial contaminants were used for inoculation of cheeses: 10(3) to 10(4), 10(4) to 10(6), and 10(6) to 10(8) CFU/g. Inoculation in plain culture medium served as a control. The inhibition produced by the probiotics was calculated, and the major effect was found to be bacteriostatic. In specific cases, full inhibition was observed, i.e., by B. animalis against P. aeruginosa and by L. casei against Salmonella Enteritidis and L. innocua. Conversely, the least inhibition was detected for L. casei against P. aeruginosa. Our results suggest that use of these probiotic strains can extend the shelf life of whey cheeses and make them safer by delaying or preventing growth of common contaminant bacteria.
Bayesian Games with Intentions
Directory of Open Access Journals (Sweden)
Adam Bjorndahl
2016-06-01
Full Text Available 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.
Risk Assessment of Ship Oil Spill Based on Bayesian Network%基于贝叶斯网络的船舶溢油风险评价研究
Institute of Scientific and Technical Information of China (English)
刘克中; 干伟东; 黄明; 邓健; 杨星
2012-01-01
船舶溢油事故已成为导致海洋污染重要的因素之一,采用科学方法对船舶溢油风险进行有效的预测与评估具有重要意义.将船舶溢油风险分为操作性溢油风险与事故性溢油风险两类,通过分析历史数据与借助专家经 验识别风险因素,构建了船舶溢油风险的贝叶斯网络模型和条件概率表CPT,并利用HUGIN软件进行了概率推理和风险因素灵敏度分析,定量评估了船舶溢油风险,找出了影响最突出的风险因素.将贝叶斯网络模型应用于我国沿海港口水域,得出两类船舶溢油风险概率分别为0.013 8和0.000 3,指出了加燃油、装卸油品、人员疏忽和船舶密度等风险因素对船舶溢油风险影响最突出.%The accidents of ship oil spill have been one of the most significant factors causing marine pollution, so appropriate approaches to forecast ship oil spill risk has important significance. A novel Bayesian model for risk assessment of ship oil spill is presented. Ship oil spill is divided into two kinds, I. E. 'operation spill' and 'accident spill'. The Bayesian network model and the statistics CPT are constructed by analyzing historic accident data and expert experience. The software HUGIN is used to compute accident probability of ship oil spill and to analyze sensitivity of risk factors so as to evaluate ship oil spill risks quantitatively and find out main risk factors. The presented Bayesian network model is applied to study the ship oil spill risk in China's coastal harbor waters, which led to the conclusion that probabilities for the two ship oil spill kinds are 0.013 8 and 0. 000 3 respectively and that bunkering, loading or discharging operation, personnel negligence and ship density etc. Are the major factors which influence ship oil spill risks.
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
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...
Franz, E.; Tromp, S.O.; Rijgersberg, H.; Fels-Klerx, van der H.J.
2010-01-01
Fresh vegetables are increasingly recognized as a source of foodborne outbreaks in many parts of the world. The purpose of this study was to conduct a quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes infection from consumption of leafy green
Pérez-Rodríguez, F.; Zwietering, M.H.
2012-01-01
The Central Limit Theorem (CLT) is proposed as a means of understanding microbial risk in foods from a Public Health perspective. One variant of the CLT states that as the number of random variables, each with a finite mean and variance, increases (¿8), the distribution of the sum (or mean) of those
Microbial colonisation of orthopaedic tourniquets: a potential risk for surgical site infection.
Sahu, S K; Tudu, B; Mall, P K
2015-02-01
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.
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
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…
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.
Pouillot, Régis; Delignette-Muller, Marie Laure
2010-09-01
Quantitative risk assessment has emerged as a valuable tool to enhance the scientific basis of regulatory decisions in the food safety domain. This article introduces the use of two new computing resources (R packages) specifically developed to help risk assessors in their projects. The first package, "fitdistrplus", gathers tools for choosing and fitting a parametric univariate distribution to a given dataset. The data may be continuous or discrete. Continuous data may be right-, left- or interval-censored as is frequently obtained with analytical methods, with the possibility of various censoring thresholds within the dataset. Bootstrap procedures then allow the assessor to evaluate and model the uncertainty around the parameters and to transfer this information into a quantitative risk assessment model. The second package, "mc2d", helps to build and study two dimensional (or second-order) Monte-Carlo simulations in which the estimation of variability and uncertainty in the risk estimates is separated. This package easily allows the transfer of separated variability and uncertainty along a chain of conditional mathematical and probabilistic models. The usefulness of these packages is illustrated through a risk assessment of hemolytic and uremic syndrome in children linked to the presence of Escherichia coli O157:H7 in ground beef. These R packages are freely available at the Comprehensive R Archive Network (cran.r-project.org).
von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo
2014-06-01
Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.
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.
Done, Hansa Y; Halden, Rolf U
2015-01-23
Aquaculture production has nearly tripled in the last two decades, bringing with it a significant increase in the use of antibiotics. Using liquid chromatography/tandem mass spectrometry (LC-MS/MS), the presence of 47 antibiotics was investigated in U.S. purchased shrimp, salmon, catfish, trout, tilapia, and swai originating from 11 different countries. All samples (n=27) complied with U.S. FDA regulations and five antibiotics were detected above the limits of detection: oxytetracycline (in wild shrimp, 7.7ng/g of fresh weight; farmed tilapia, 2.7; farmed salmon, 8.6; farmed trout with spinal deformities, 3.9), 4-epioxytetracycline (farmed salmon, 4.1), sulfadimethoxine (farmed shrimp, 0.3), ormetoprim (farmed salmon, 0.5), and virginiamycin (farmed salmon marketed as antibiotic-free, 5.2). A literature review showed that sub-regulatory levels of antibiotics, as found here, can promote resistance development; publications linking aquaculture to this have increased more than 8-fold from 1991 to 2013. Although this study was limited in size and employed sample pooling, it represents the largest reconnaissance of antibiotics in U.S. seafood to date, providing data on previously unmonitored antibiotics and on farmed trout with spinal deformities. Results indicate low levels of antibiotic residues and general compliance with U.S. regulations. The potential for development of microbial drug resistance was identified as a key concern and research priority.
Reconnaissance of 47 Antibiotics and Associated Microbial Risks in Seafood Sold in the United States
Done, Hansa Y.; Halden, Rolf U.
2014-01-01
Aquaculture production has nearly tripled in the last two decades, bringing with it a significant increase in the use of antibiotics. Using liquid chromatography/tandem mass spectrometry (LC-MS/MS), the presence of 47 antibiotics was investigated in U.S. purchased shrimp, salmon, catfish, trout, tilapia, and swai originating from 11 different countries. All samples (n= 27) complied with U.S. FDA regulations and five antibiotics were detected above the limits of detection: oxytetracycline (in wild shrimp, 7.7 ng/g of fresh weight; farmed tilapia, 2.7; farmed salmon, 8.6; farmed trout with spinal deformities, 3.9), 4-epioxytetracycline (farmed salmon, 4.1), sulfadimethoxine (farmed shrimp, 0.3), ormetoprim (farmed salmon, 0.5), and virginiamycin (farmed salmon marketed as antibiotic-free, 5.2). A literature review showed that sub-regulatory levels of antibiotics, as found here, can promote resistance development and publications linking aquaculture to this have increased more than 8-fold from 1991–2013. Although this study was limited in size and employed sample pooling, it represents the largest reconnaissance of antibiotics in U.S. seafood to date, providing data on previously unmonitored antibiotics and on farmed trout with spinal deformities. Results indicate low levels of antibiotic residues and general compliance with U.S. regulations. The potential for development of microbial drug resistance was identified as a key concern and research priority. PMID:25449970
Konstruksi Bayesian Network Dengan Algoritma Bayesian Association Rule Mining Network
Octavian
2015-01-01
Beberapa tahun terakhir, Bayesian Network telah menjadi konsep yang populer digunakan dalam berbagai bidang kehidupan seperti dalam pengambilan sebuah keputusan dan menentukan peluang suatu kejadian dapat terjadi. Sayangnya, pengkonstruksian struktur dari Bayesian Network itu sendiri bukanlah hal yang sederhana. Oleh sebab itu, penelitian ini mencoba memperkenalkan algoritma Bayesian Association Rule Mining Network untuk memudahkan kita dalam mengkonstruksi Bayesian Network berdasarkan data ...
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...
Abdalla Abbas, Mohammed; Guenther, Albrecht; Galantucci, Sebastiano; Fawi, Gharib; Comi, Giancarlo; Kwan, Joseph; Corea, Francesco
2008-01-01
Infection and inflammation may have a crucial role in the pathogenesis of atherosclerosis. This hypothesis is supported by an increasing number of reports on the interaction between chronic infection, inflammation, and atherogenesis. Assessment of serological and inflammatory markers of infection may be useful adjuncts in identifying those patients who are at a higher risk of developing vascular events, and in whom more aggressive treatments might be warranted.
Model Diagnostics for Bayesian Networks
Sinharay, Sandip
2006-01-01
Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…
Bayesian Lensing Shear Measurement
Bernstein, Gary M
2013-01-01
We derive an estimator of weak gravitational lensing shear from background galaxy images that avoids noise-induced biases through a rigorous Bayesian treatment of the measurement. The Bayesian formalism requires a prior describing the (noiseless) distribution of the target galaxy population over some parameter space; this prior can be constructed from low-noise images of a subsample of the target population, attainable from long integrations of a fraction of the survey field. We find two ways to combine this exact treatment of noise with rigorous treatment of the effects of the instrumental point-spread function and sampling. The Bayesian model fitting (BMF) method assigns a likelihood of the pixel data to galaxy models (e.g. Sersic ellipses), and requires the unlensed distribution of galaxies over the model parameters as a prior. The Bayesian Fourier domain (BFD) method compresses galaxies to a small set of weighted moments calculated after PSF correction in Fourier space. It requires the unlensed distributi...
Fox, G.J.A.; Berg, van den S.M.; Veldkamp, B.P.; Irwing, P.; Booth, T.; Hughes, D.
2015-01-01
In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item resp
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...
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.
d'Alessandro, Eugénie
2015-07-01
In hospital care, management of the risk of infection represents a crucial issue. Nevertheless, this question remains a neglected area in anthropological research, especially in African countries. To shed new light on this question, we conducted an anthropological investigation in the infectious disease department of a hospital in Niger. Daily observation of the work of the hospital staff for a total period of 6 months was spread out over 2008 and 2009. During our prolonged stay, we also collected 64 in-depth interviews of health care workers and attendants in the department. This study method made it possible to describe many of the practices and discourses related to the issues of medical and personal care and hospital hygiene and to compare the practices observed to standard principles for preventing hospital-acquired infections. Our ethnographic attention to the behavior of the actors showed the absence of formal spatial segmentations between different activities. The care provided by the untrained relatives serving as personal attendants introduced territorial enclaves governed by home hygiene standards into the interior of technical spaces. At the same time, privatizing equipment and space for their diverse activities, the medical staff disrupted technical chains and generated the recurrent crossing of microbial geographies. These results allow us to offer two principal guidelines for improving the quality of care and the management of risks of infection in hospitals in West Africa: (1) the essential role of the attendants in the care provided to hospital inpatients must be officially taken into account, especially by including them in the organization of medical hygiene procedures; (2) the different overlapping technical activities and social activities in the work space must be limited by their geographic and architectural segmentation.
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.
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.
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.
Learning Bayesian Networks from Correlated Data.
Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H; Perls, Thomas T; Sebastiani, Paola
2016-05-05
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.
Microbial Source Tracking: Current and Future Molecular Tools in Microbial Water Quality Forensics
Current regulations in the United States stipulate that the microbial quality of waters used for consumption and recreational activities should be determined regularly by measuring microbial indicators of fecal pollution. Hence, the microbial risk associated with these waters is...
Bahk, Gyung-Jin; Todd, Ewen C D
2007-01-01
Microbial risk assessment (MRA) is becoming increasingly used in the management of food safety because it can be used to quantify risks and help rank intervention strategies. The exposure assessment components of the assessments have become complex with many aspects of the contamination, survival, and growth of a pathogen in a food being taken into consideration. Insufficient consumption data constitutes an important data gap and consequently one of many sources of uncertainty in MRA even though the effects of uncertainty are smaller than those affecting bacterial concentration in foods. Therefore, food consumption data also play an important role in exposure assessment of MRA. In the United States, there are large-scale, nationwide sets of consumption data available for use in MRA, i.e., the National Health and Nutrition Examination Survey (NHANES). Newly released dietary interview data in the NHANES 2001 to 2002 survey show that it has been redesigned and that the data were sufficiently updated from previous versions to have more value for MRAs. We propose a model that can effectively use the new data sets and be incorporated into MRAs, using as an example consumption of Cheddar cheese/American-type cheese. This model included the prevalence of food eaten as well as the amount and frequency. We determined the amount of Cheddar/American cheese consumed per day with probability distribution (e.g., lognormal distribution). These could be further determined by gender, age, pregnancy, and combination food type, which we plan to do in the future. The frequency of the range of serving numbers for Cheddar/American cheese consumed per person per day and prevalence as the proportion of a population (e.g., survey respondents) eating a certain food in a day are also presented. Unlike traditional published mean values, the results of this model provide probability distribution intakes that can be compared with mean and median intakes. This allows values in the upper
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.
Bhosale, Prakash; Bernstein, Paul S
2005-09-01
Xanthophylls are oxygenated carotenoids abundant in the human food supply. Lutein, zeaxanthin, and cryptoxanthin are major xanthophyll carotenoids in human plasma. The consumption of these xanthophylls is directly associated with reduction in the risk of cancers, cardiovascular disease, age-related macular degeneration, and cataract formation. Canthaxanthin and astaxanthin also have considerable importance in aquaculture for salmonid and crustacean pigmentation, and are of commercial interest for the pharmaceutical and food industries. Chemical synthesis is a major source for the heavy demand of xanthophylls in the consumer market; however, microbial producers also have potential as commercial sources. In this review, we discuss the biosynthesis, commercial utility, and major microbial sources of xanthophylls. We also present a critical review of current research and technologies involved in promoting microbes as potential commercial sources for mass production.
Bayesian Face Sketch Synthesis.
Wang, Nannan; Gao, Xinbo; Sun, Leiyu; Li, Jie
2017-03-01
Exemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical models. The proposed Bayesian framework consists of two parts: the neighbor selection model and the weight computation model. Within the proposed framework, we further propose a Bayesian face sketch synthesis method. The essential rationale behind the proposed Bayesian method is that we take the spatial neighboring constraint between adjacent image patches into consideration for both aforementioned models, while the state-of-the-art methods neglect the constraint either in the neighbor selection model or in the weight computation model. Extensive experiments on the Chinese University of Hong Kong face sketch database demonstrate that the proposed Bayesian method could achieve superior performance compared with the state-of-the-art methods in terms of both subjective perceptions and objective evaluations.
Monaghan, J M; Augustin, J C; Bassett, J; Betts, R; Pourkomailian, B; Zwietering, M H
2017-03-28
Over the last 10 years, some high-profile foodborne illness outbreaks have been linked to the consumption of leafy greens. Growers are required to complete microbiological risk assessments (RAs) for the production of leafy crops supplied either to retail or for further processing. These RAs are based primarily on qualitative judgements of hazard and risks at various stages in the production process but lack many of the steps defined for quantitative microbiological RAs by the Codex Alimentarius Commission. This article is based on the discussions of an industry expert group and proposes a grower RA approach based on a structured qualitative assessment, which requires all decisions to be based on evidence and a framework for describing the decision process that can be challenged and defended within the supply chain. In addition, this article highlights the need for evidence to be more easily available and accessible to primary producers and identifies the need to develop hygiene criteria to aid validation of proposed interventions.
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.
Hybrid Batch Bayesian Optimization
Azimi, Javad; Fern, Xiaoli
2012-01-01
Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function evaluations are possible. Under such a setting, BO could choose to either sequentially evaluate the function, one input at a time and wait for the output of the function before making the next selection, or evaluate the function at a batch of multiple inputs at once. These two different settings are commonly referred to as the sequential and batch settings of Bayesian Optimization. In general, the sequential setting leads to better optimization performance as each function evaluation is selected with more information, whereas the batch setting has an advantage in terms of the total experimental time (the number of iterations). In this work, our goal is to combine the strength of both settings. Specifically, we systematically analyze Bayesian optimization using Gaussian process as the posterior estimator and provide a hybrid algorithm t...
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.
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 corr......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...
Center, Julian L.; Knuth, Kevin H.
2011-03-01
Visual odometry refers to tracking the motion of a body using an onboard vision system. Practical visual odometry systems combine the complementary accuracy characteristics of vision and inertial measurement units. The Mars Exploration Rovers, Spirit and Opportunity, used this type of visual odometry. The visual odometry algorithms in Spirit and Opportunity were based on Bayesian methods, but a number of simplifying approximations were needed to deal with onboard computer limitations. Furthermore, the allowable motion of the rover had to be severely limited so that computations could keep up. Recent advances in computer technology make it feasible to implement a fully Bayesian approach to visual odometry. This approach combines dense stereo vision, dense optical flow, and inertial measurements. As with all true Bayesian methods, it also determines error bars for all estimates. This approach also offers the possibility of using Micro-Electro Mechanical Systems (MEMS) inertial components, which are more economical, weigh less, and consume less power than conventional inertial components.
Directory of Open Access Journals (Sweden)
D P Narayan
2015-01-01
Full Text Available Introduction: Accumulated data obtained over the last 20 years on the microbial status and survival of pathogens on currency notes indicate that this could represent a potential cause of sporadic cases of food borne illness. Objectives: To identify the micro-organisms present on the Indian currency notes and the oral health risks due to microbial contamination of Indian currency notes circulating in Bengaluru city. Materials and Methods: A cross-sectional survey was conducted and the Indian currency notes of various denominations (Rs. 10, Rs. 20, Rs. 50, Rs. 100, Rs. 500, and Rs. 1000 were collected from fruit vendors, hawkers, vegetable vendors, bus conductors, railway ticket counters, hotel counters, and butchers. Sample size was determined to be 70 Indian currency notes. Convenience sampling technique was used. Microbiological analysis of the collected currency notes was done. Results: The contamination rate of collected currency notes from the butchers and hawkers were 80% and 60% respectively. Staphylococcus aureus was present on 15 currency notes (21.42% and was found to be higher in Rs. 10 than in other currency denominations. Streptococcus pyogenes was present on four currency notes (5.714% of Rs. 10. Conclusion: The Indian currency notes circulating in Bengaluru city were contaminated with pathogenic bacteria. The oral health risks due to microbial contamination of Indian currency notes are acute pharyngitis, peritonsillar or retropharyngeal abscess, mastoiditis, sinusitis, otitis media, mild cellulitis, angular cheilitis, some endodontic infections, osteomyelitis of the jaw, parotitis, and oral mucositis.
Probabilistic Inferences in Bayesian Networks
Ding, Jianguo
2010-01-01
This chapter summarizes the popular inferences methods in Bayesian networks. The results demonstrates that the evidence can propagated across the Bayesian networks by any links, whatever it is forward or backward or intercausal style. The belief updating of Bayesian networks can be obtained by various available inference techniques. Theoretically, exact inferences in Bayesian networks is feasible and manageable. However, the computing and inference is NP-hard. That means, in applications, in ...
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
Risk assessment model for mobile payment based on Bayesian net-work%基于贝叶斯网络的移动支付风险评估模型
Institute of Scientific and Technical Information of China (English)
张璇; 林逸风; 白川; 王旭; 马暮婷; 于倩
2014-01-01
With the development of the information technology and the networks, more and more payment businesses, techniques and tools are provided, as one of them, mobile payments are also promoted. Mobile payments make people’s lives easier and faster, however, potential risk, vulnerabilities and malicious attacks are also aroused. In this paper, risk analysis and assessment of mobile payment are presented. Based on Bayesian networks, by analyzing the entities of mobile payment system, it proposes a risk assessment model for mobile payment. By using this model, the risk of mobile pay-ment can be calculated and the result can be used to help proposing risk control solutions. The risk value before and after risk control can be compared to show the feasibility of the risk control solution. The last case study shows that the model meets the needs of risk assessment of mobile payment.%随着信息技术和网络的迅猛发展，支付业务、技术及工具不断创新，移动支付的发展在逐渐加快。移动支付给人们生活带来方便和快捷的同时，也存在着较高的潜在风险，容易遭受非法入侵和恶意攻击。就移动支付风险的分析及风险值的计算理论方面开展工作，在贝叶斯网络的基础上，针对移动支付的主要组成主体，提出移动支付风险评估模型，通过使用该模型进行移动支付风险评估不仅可以对目前移动支付的风险进行评估，还可以根据风险评估结果引导风险控制，对比风险控制前后的风险值判断风险控制的效果，通过案例分析，提出的移动支付风险评估模型可以很好地完成移动支付的风险评估要求。
Bayesian methods for hackers probabilistic programming and Bayesian inference
Davidson-Pilon, Cameron
2016-01-01
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...
DEFF Research Database (Denmark)
Jensen, Finn Verner; Nielsen, Thomas Dyhre
2016-01-01
and edges. The nodes represent variables, which may be either discrete or continuous. An edge between two nodes A and B indicates a direct influence between the state of A and the state of B, which in some domains can also be interpreted as a causal relation. The wide-spread use of Bayesian networks...
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 experimental...
Bayesian belief networks in business continuity.
Phillipson, Frank; Matthijssen, Edwin; Attema, Thomas
2014-01-01
Business continuity professionals aim to mitigate the various challenges to the continuity of their company. The goal is a coherent system of measures that encompass detection, prevention and recovery. Choices made in one part of the system affect other parts as well as the continuity risks of the company. In complex organisations, however, these relations are far from obvious. This paper proposes the use of Bayesian belief networks to expose these relations, and presents a modelling framework for this approach.
Institute of Scientific and Technical Information of China (English)
孙傅; 沙婧; 张一帆; 刘彦华
2013-01-01
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.%随着城市居民亲水娱乐活动日益增加,开展景观娱乐水体微生物风险评价,并以此为基础开展有效的风险管理,对于人口密集的城市区域尤为重要.以我国南方某城市3个典型景观娱乐水体为案例,采用定量微生物风险评价的方法框架,综合运用水质监测、社会调查、文献调研等方法开展暴露评价,并由此开展基于粪大肠菌(FC)、大肠埃希氏菌(EC)和肠球菌(ENT)的多微生
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.
Rigaux, Clémence; André, Stéphane; Albert, Isabelle; Carlin, Frédéric
2014-02-03
Microbial spoilage of canned foods by thermophilic and highly heat-resistant spore-forming bacteria, such as Geobacillus stearothermophilus, is a persistent problem in the food industry. An incubation test at 55 °C for 7 days, then validation of biological stability, is used as an indicator of compliance with good manufacturing practices. We propose a microbial risk assessment model predicting the percentage of non-stability due to G. stearothermophilus in canned green beans manufactured by a French company. The model accounts for initial microbial contaminations of fresh unprocessed green beans with G. stearothermophilus, cross-contaminations in the processing chain, inactivation processes and probability of survival and growth. The sterilization process is modeled by an equivalent heating time depending on sterilization value F₀ and on G. stearothermophilus resistance parameter z(T). Following the recommendations of international organizations, second order Monte-Carlo simulations are used, separately propagating uncertainty and variability on parameters. As a result of the model, the mean predicted non-stability rate is of 0.5%, with a 95% uncertainty interval of [0.1%; 1.2%], which is highly similar to data communicated by the French industry. A sensitivity analysis based on Sobol indices and some scenario tests underline the importance of cross-contamination at the blanching step, in addition to inactivation due to the sterilization process.
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
Beelen P van; Doelman P; ECO
1996-01-01
Micro-organisms are vital for soil fertility and for the degradation of organic matter and pollutants in soils and sediments. Due to their function and ubiquitous presence the microflora can act as an environmentally very relevant indicator of pollution. Microbial tests should be used discriminatory
Institute of Scientific and Technical Information of China (English)
胡思涛; 朱艳茹; 项乔君
2014-01-01
农村公路急弯、陡坡、路侧险要等典型路段往往是事故易发多发路段。在现阶段农村公路安保工程资金有限的情况下，需对既有典型路段的事故风险进行定量评价和排序，为隐患路段的排查和安保工程分期实施提供依据。在现有研究成果的基础上，构建典型路段事故风险评价指标体系，提出一种基于贝叶斯网络的农村公路典型路段事故风险评价方法。该方法能有效集结专家知识和经验定量评价典型路段（包括单一路段和组合路段）的事故风险，并可以随着数据的更新不断完善模型。在农村公路事故资料相对缺乏的情况下，该方法具有较强的实用性。%Typical sections such as sharp turns, steep slopes and dangerous roadsides of the rural roads tend to be crash prone sections.In the case of limited budget for highway safety enhancement project,in order to identi-fy potential high-risk sections and provide the basis for implement in phases of safety enhancement project, it is necessary to evaluate quantitatively and sort the crash risk of typical sections.On the basis of related resear-ches, a crash risk assessment index system for typical sections has been constructed.And a crash risk assess-ment method for typical sections of rural roads using Bayesian networks was proposed.It is effective to evaluate the crash risk of typical sections ( including single sections and combination sections ) by combining expert knowledge and experience with the proposed method.It was also noted that the proposed model could be con-stantly improved as related data updated.The proposed method showed promise to be used for crash risk assess-ment especially when lack of accident relative information.
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...
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel N
2012-01-01
Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...
Drilling Site Risk Assessment Based on Bayesian Network%基于贝叶斯网络的钻井作业现场风险评估
Institute of Scientific and Technical Information of China (English)
王兵; 杨小莹; 赵春兰; 肖斌
2015-01-01
In view of the high investment and risk and uncertainties in drilling operation,the safety evaluation about the drilling operation is carried out in the paper. The method of evaluating risk and seeking risk resource during drilling operation has been developed by using Bayes network. The 32 risk factors during the drilling operation could be classified into man-made risk factors and natural risk factors by analyzing the history data and identifying the dangerous factors with the help of expertise. The Bayes network topological structure and conditional probability table(CPT)was developed for drilling operation risk;the probability was predicted forward and diagnosed backward;the safety probability of drilling operation was evaluated quantitative and the most dangerous factor was found out. After applying the Bayes network model to Well L gas drilling operation,we got the risk probability of man-made risk and natural risk at 0.108 and 0.165,respectively,the risk probability of Well L gas drilling operation at 0.137. The many dangerous factors are defects in monitor during the drilling process,lack of security protection facilities,hidden trouble induced by drilling operation,defect in well-control equipment and management in production. This will provide precise diagnostic data for operators and decision-making for safe production.%针对高投入、高风险和不确定性的钻井作业现场，展开了安全评价研究。提出了一种基于贝叶斯网络定量评价钻井作业现场风险、寻找风险源的方法。通过分析历史数据与借助专家经验识别不安全因素，将影响钻井作业现场安全性的32个因素分为“人的不安全行为”和“物的不安全状态”，同时构建了钻井作业现场安全性的贝叶斯网络拓扑结构，并进行了概率推理向前预测和向后诊断，定量评估了钻井作业现场安全性，找出了影响最突出的不安全因素。将其应用于龙岗气田L井钻井作业
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...... in a Matlab toolbox, is demonstrated for non-negative decompositions and compared with non-negative matrix factorization....
Wetering, van de G.; Steuten, L.M.G.; Birgelen, von C.; Adang, E.M.; IJzerman, M.J.
2012-01-01
Objectives: Innovative point-of-care (POC) diagnostics are likely to have a strong impact on 18 health care. The aim of this study is to conduct an early assessment of a point-of-care chip 19 for the detection of a pathological deviation of the potassium levels in patients at increased 20 risk, spec
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...
Risk Analysis of Shield Construction by Polymorphic Bayesian Networks Model%盾构法施工风险的多态贝叶斯网络模型分析
Institute of Scientific and Technical Information of China (English)
周健; 王红卫; 吴邵海
2013-01-01
Subway tunnel is characterzed by a large-scale investment, many risk factors, large social impact, which determines the need of risk management during the construction. In view of the fuzziness of risks, it is difficult to use an accurate probability or a probability interval to describe risks. Therefore, shield excavation risks are proposed to be analyzed by multiple probability intervals. Based on the five probability intervals which divide risk probabilities into five ranks in the subway and underground engineering, a risk factor questionnaire is designed, and the probability distributions of probability ranks about shield construction risk factors can be figured out through expert survey method. According to the questionnaire, Bayesian Networks are applied to establishing a multi-state system to analyze the probability distributions. The multi-state system is used in the risk assessment of a subway tunnel, the probability rank of the project is figured out and at the corresponding probability rank, such risk factors as poor seal in the portals, axis deviation which causes segment failure and the damage, destruction of sealing material are considered as the key control objects.%地铁隧道具有投资规模大、风险因素多、社会影响大等特点,这决定了在施工过程中进行风险管理的必要性.鉴于风险的模糊性,很难用一个具体的概率值或概率区间来描述其发生概率,由此提出了用多个概率区间来分析盾构法施工过程中的风险.以划分地铁及地下工程风险概率等级的5个概率区间为度量标准,设计出风险因素概率等级调查表,通过专家调查法可统计出盾构法施工风险因素概率等级的概率分布；基于调查表,采用贝叶斯网络建立了分析风险事故概率等级概率分布的多态系统.利用该系统对某地铁隧道工程进行风险评估,得到了该工程的风险概率等级,并分析出相应风险概率等级下进出洞洞口密封效
Liao, Hehuan; Krometis, Leigh-Anne H; Kline, Karen
2016-05-01
Within the United States, elevated levels of fecal indicator bacteria (FIB) remain the leading cause of surface water-quality impairments requiring formal remediation plans under the federal Clean Water Act's Total Maximum Daily Load (TMDL) program. The sufficiency of compliance with numerical FIB criteria as the targeted endpoint of TMDL remediation plans may be questionable given poor correlations between FIB and pathogenic microorganisms and varying degrees of risk associated with exposure to different fecal pollution sources (e.g. human vs animal). The present study linked a watershed-scale FIB fate and transport model with a dose-response model to continuously predict human health risks via quantitative microbial risk assessment (QMRA), for comparison to regulatory benchmarks. This process permitted comparison of risks associated with different fecal pollution sources in an impaired urban watershed in order to identify remediation priorities. Results indicate that total human illness risks were consistently higher than the regulatory benchmark of 36 illnesses/1000 people for the study watershed, even when the predicted FIB levels were in compliance with the Escherichia coli geometric mean standard of 126CFU/100mL. Sanitary sewer overflows were associated with the greatest risk of illness. This is of particular concern, given increasing indications that sewer leakage is ubiquitous in urban areas, yet not typically fully accounted for during TMDL development. Uncertainty analysis suggested the accuracy of risk estimates would be improved by more detailed knowledge of site-specific pathogen presence and densities. While previous applications of the QMRA process to impaired waterways have mostly focused on single storm events or hypothetical situations, the continuous modeling framework presented in this study could be integrated into long-term water quality management planning, especially the United States' TMDL program, providing greater clarity to watershed
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...... 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...
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
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.
Wadhia, Kirit; Dando, Terry; Thompson, K Clive
2007-09-01
The Microbial Assay for Risk Assessment (MARA) is an innovative system based on an array of 11 different microbial species freeze-dried in a 96-well micro-titre plate format. Developed for testing the toxicity of chemicals, mixtures and environmental samples, the assay employs species of a taxonomically diverse range. In addition to ten prokaryotic species, a eukaryote (yeast) is included in the range. The MARA's innate scope of a multi-dimensional test allows determination of toxicity based on a unique assay fingerprint or index, numerically expressed as the mean Microbial Toxic Concentration (MTC). The most significant potential of the test is in the additional inference that can be conveyed to the toxicity evaluation because of the presence of each of the constituent species. In view of the fact that conventional aquatic bioassays, like fish or cladoceran tests, are expensive and impractical, the MARA could provide a cost-effective solution for routine ecotoxicological testing. The performance of the MARA was evaluated to ascertain its capability and potential scope. Sensitivity to toxicants and different environmental samples was assessed. Evaluation included comparison with other tests: namely Microtox, invertebrate (Daphnia magna and Thamnocephalus platyurus) microbiotests, and respiration-inhibition and nitrification-inhibition tests. The most sensitive invertebrate test was found to be the T. platyurus microbiotest for three of the four metals tested. The LC(50) values for this test for Cd(ii), Cr(vi) and As(iii) were 0.2, 0.018 and 0.3 mg l(-1), respectively; and the corresponding most sensitive MARA species MTC values were 4.4, 2.8 and 17 mg l(-1), respectively.
Classification using Bayesian neural nets
J.C. Bioch (Cor); O. van der Meer; R. Potharst (Rob)
1995-01-01
textabstractRecently, Bayesian methods have been proposed for neural networks to solve regression and classification problems. These methods claim to overcome some difficulties encountered in the standard approach such as overfitting. However, an implementation of the full Bayesian approach to neura
Bayesian Intersubjectivity and Quantum Theory
Pérez-Suárez, Marcos; Santos, David J.
2005-02-01
Two of the major approaches to probability, namely, frequentism and (subjectivistic) Bayesian theory, are discussed, together with the replacement of frequentist objectivity for Bayesian intersubjectivity. This discussion is then expanded to Quantum Theory, as quantum states and operations can be seen as structural elements of a subjective nature.
Bayesian Approach for Inconsistent Information.
Stein, M; Beer, M; Kreinovich, V
2013-10-01
In engineering situations, we usually have a large amount of prior knowledge that needs to be taken into account when processing data. Traditionally, the Bayesian approach is used to process data in the presence of prior knowledge. Sometimes, when we apply the traditional Bayesian techniques to engineering data, we get inconsistencies between the data and prior knowledge. These inconsistencies are usually caused by the fact that in the traditional approach, we assume that we know the exact sample values, that the prior distribution is exactly known, etc. In reality, the data is imprecise due to measurement errors, the prior knowledge is only approximately known, etc. So, a natural way to deal with the seemingly inconsistent information is to take this imprecision into account in the Bayesian approach - e.g., by using fuzzy techniques. In this paper, we describe several possible scenarios for fuzzifying the Bayesian approach. Particular attention is paid to the interaction between the estimated imprecise parameters. In this paper, to implement the corresponding fuzzy versions of the Bayesian formulas, we use straightforward computations of the related expression - which makes our computations reasonably time-consuming. Computations in the traditional (non-fuzzy) Bayesian approach are much faster - because they use algorithmically efficient reformulations of the Bayesian formulas. We expect that similar reformulations of the fuzzy Bayesian formulas will also drastically decrease the computation time and thus, enhance the practical use of the proposed methods.
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....
Procter, T D; Pearl, D L; Finley, R L; Leonard, E K; Janecko, N; Reid-Smith, R J; Weese, J S; Peregrine, A S; Sargeant, J M
2014-06-01
Anti-microbial resistance can threaten health by limiting treatment options and increasing the risk of hospitalization and severity of infection. Companion animals can shed anti-microbial-resistant bacteria that may result in the exposure of other dogs and humans to anti-microbial-resistant genes. The prevalence of anti-microbial-resistant generic Escherichia coli in the faeces of dogs that visited dog parks in south-western Ontario was examined and risk factors for shedding anti-microbial-resistant generic E. coli identified. From May to August 2009, canine faecal samples were collected at ten dog parks in three cities in south-western Ontario, Canada. Owners completed a questionnaire related to pet characteristics and management factors including recent treatment with antibiotics. Faecal samples were collected from 251 dogs, and 189 surveys were completed. Generic E. coli was isolated from 237 of the faecal samples, and up to three isolates per sample were tested for anti-microbial susceptibility. Eighty-nine percent of isolates were pan-susceptible; 82.3% of dogs shed isolates that were pan-susceptible. Multiclass resistance was detected in 7.2% of the isolates from 10.1% of the dogs. Based on multilevel multivariable logistic regression, a risk factor for the shedding of generic E. coli resistant to ampicillin was attending dog day care. Risk factors for the shedding of E. coli resistant to at least one anti-microbial included attending dog day care and being a large mixed breed dog, whereas consumption of commercial dry and home cooked diets was protective factor. In a multilevel multivariable model for the shedding of multiclass-resistant E. coli, exposure to compost and being a large mixed breed dog were risk factors, while consumption of a commercial dry diet was a sparing factor. Pet dogs are a potential reservoir of anti-microbial-resistant generic E. coli; some dog characteristics and management factors are associated with the prevalence of anti-microbial
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.
Approximate Bayesian computation.
Directory of Open Access Journals (Sweden)
Mikael Sunnåker
Full Text Available Approximate Bayesian computation (ABC constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology.
Pérez-Rodríguez, Fernando; Zwietering, Marcel H
2012-02-15
The Central Limit Theorem (CLT) is proposed as a means of understanding microbial risk in foods from a Public Health perspective. One variant of the CLT states that as the number of random variables, each with a finite mean and variance, increases (→∞), the distribution of the sum (or mean) of those variables approximates a normal distribution. On the basis of the CLT, the hypothesis introduced by this paper states that the Coefficient of Variation (CV) of the annual number of food-borne illness cases decreases as a result of a larger number of exposures (or servings) (n). Second-order Monte-Carlo analysis and classical statistics were used to support the hypothesis, based on existing risk models on Listeria monocytogenes in deli meat products focused on elderly people in the United States. Likewise, the hypothesis was tested on epidemiological data of annual incidence of salmonellosis and listeriosis in different countries (i.e. different n). Although different sources of error affected the accuracy of the results, both the Monte-Carlo analysis (in silico) and epidemiological data (in vivo), especially for salmonellosis, demonstrated that the CV of the annual number of cases decreased as n increased as stated by the CLT. Furthermore, results from this work showed that classical statistical methods can be helpful to provide reliable risk estimates based on simple and well-established statistical principles.
Borsboom, D.; Haig, B.D.
2013-01-01
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science primar
Implementing Bayesian Vector Autoregressions Implementing Bayesian Vector Autoregressions
Directory of Open Access Journals (Sweden)
Richard M. Todd
1988-03-01
Full Text Available Implementing Bayesian Vector Autoregressions This paper discusses how the Bayesian approach can be used to construct a type of multivariate forecasting model known as a Bayesian vector autoregression (BVAR. In doing so, we mainly explain Doan, Littermann, and Sims (1984 propositions on how to estimate a BVAR based on a certain family of prior probability distributions. indexed by a fairly small set of hyperparameters. There is also a discussion on how to specify a BVAR and set up a BVAR database. A 4-variable model is used to iliustrate the BVAR approach.
Tromp, S.O.; Rijgersberg, H.; Franz, E.
2010-01-01
Quantitative microbial risk assessments do not usually account for the planning and ordering mechanisms (logistics) of a food supply chain. These mechanisms and consumer demand determine the storage and delay times of products. The aim of this study was to quantitatively assess the difference betwee
Energy Technology Data Exchange (ETDEWEB)
Liao, Hehuan, E-mail: hehuan86@vt.edu [Department of Biological Systems Engineering, Virginia Tech, 155 Ag Quad Lane, Blacksburg, VA 24061 (United States); Krometis, Leigh-Anne H. [Department of Biological Systems Engineering, Virginia Tech, 155 Ag Quad Lane, Blacksburg, VA 24061 (United States); Kline, Karen [Department of Biological Systems Engineering, Virginia Tech, 155 Ag Quad Lane, Blacksburg, VA 24061 (United States); Center for Watershed Studies, Virginia Tech, 155 Ag Quad Lane, Blacksburg, VA 24061 (United States)
2016-05-01
Within the United States, elevated levels of fecal indicator bacteria (FIB) remain the leading cause of surface water-quality impairments requiring formal remediation plans under the federal Clean Water Act's Total Maximum Daily Load (TMDL) program. The sufficiency of compliance with numerical FIB criteria as the targeted endpoint of TMDL remediation plans may be questionable given poor correlations between FIB and pathogenic microorganisms and varying degrees of risk associated with exposure to different fecal pollution sources (e.g. human vs animal). The present study linked a watershed-scale FIB fate and transport model with a dose-response model to continuously predict human health risks via quantitative microbial risk assessment (QMRA), for comparison to regulatory benchmarks. This process permitted comparison of risks associated with different fecal pollution sources in an impaired urban watershed in order to identify remediation priorities. Results indicate that total human illness risks were consistently higher than the regulatory benchmark of 36 illnesses/1000 people for the study watershed, even when the predicted FIB levels were in compliance with the Escherichia coli geometric mean standard of 126 CFU/100 mL. Sanitary sewer overflows were associated with the greatest risk of illness. This is of particular concern, given increasing indications that sewer leakage is ubiquitous in urban areas, yet not typically fully accounted for during TMDL development. Uncertainty analysis suggested the accuracy of risk estimates would be improved by more detailed knowledge of site-specific pathogen presence and densities. While previous applications of the QMRA process to impaired waterways have mostly focused on single storm events or hypothetical situations, the continuous modeling framework presented in this study could be integrated into long-term water quality management planning, especially the United States' TMDL program, providing greater clarity to
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)
Ortega, Pedro A
2011-01-01
Discovering causal relationships is a hard task, often hindered by the need for intervention, and often requiring large amounts of data to resolve statistical uncertainty. However, humans quickly arrive at useful causal relationships. One possible reason is that humans use strong prior knowledge; and rather than encoding hard causal relationships, they encode beliefs over causal structures, allowing for sound generalization from the observations they obtain from directly acting in the world. In this work we propose a Bayesian approach to causal induction which allows modeling beliefs over multiple causal hypotheses and predicting the behavior of the world under causal interventions. We then illustrate how this method extracts causal information from data containing interventions and observations.
Blundell, Charles; Heller, Katherine A
2012-01-01
Hierarchical structure is ubiquitous in data across many domains. There are many hier- archical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these meth- ods limit discoverable hierarchies to those with binary branching structure. This lim- itation, while computationally convenient, is often undesirable. In this paper we ex- plore a Bayesian hierarchical clustering algo- rithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy ag- glomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.
Bayesian inference in geomagnetism
Backus, George E.
1988-01-01
The inverse problem in empirical geomagnetic modeling is investigated, with critical examination of recently published studies. Particular attention is given to the use of Bayesian inference (BI) to select the damping parameter lambda in the uniqueness portion of the inverse problem. The mathematical bases of BI and stochastic inversion are explored, with consideration of bound-softening problems and resolution in linear Gaussian BI. The problem of estimating the radial magnetic field B(r) at the earth core-mantle boundary from surface and satellite measurements is then analyzed in detail, with specific attention to the selection of lambda in the studies of Gubbins (1983) and Gubbins and Bloxham (1985). It is argued that the selection method is inappropriate and leads to lambda values much larger than those that would result if a reasonable bound on the heat flow at the CMB were assumed.
Franz, E; Tromp, S O; Rijgersberg, H; van der Fels-Klerx, H J
2010-02-01
Fresh vegetables are increasingly recognized as a source of foodborne outbreaks in many parts of the world. The purpose of this study was to conduct a quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes infection from consumption of leafy green vegetables in salad from salad bars in The Netherlands. Pathogen growth was modeled in Aladin (Agro Logistics Analysis and Design Instrument) using time-temperature profiles in the chilled supply chain and one particular restaurant with a salad bar. A second-order Monte Carlo risk assessment model was constructed (using @Risk) to estimate the public health effects. The temperature in the studied cold chain was well controlled below 5 degrees C. Growth of E. coli O157:H7 and Salmonella was minimal (17 and 15%, respectively). Growth of L. monocytogenes was considerably greater (194%). Based on first-order Monte Carlo simulations, the average number of cases per year in The Netherlands associated the consumption leafy greens in salads from salad bars was 166, 187, and 0.3 for E. coli O157:H7, Salmonella, and L. monocytogenes, respectively. The ranges of the average number of annual cases as estimated by second-order Monte Carlo simulation (with prevalence and number of visitors as uncertain variables) were 42 to 551 for E. coli O157:H7, 81 to 281 for Salmonella, and 0.1 to 0.9 for L. monocytogenes. This study included an integration of modeling pathogen growth in the supply chain of fresh leafy vegetables destined for restaurant salad bars using software designed to model and design logistics and modeling the public health effects using probabilistic risk assessment software.
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
Irregular-Time Bayesian Networks
Ramati, Michael
2012-01-01
In many fields observations are performed irregularly along time, due to either measurement limitations or lack of a constant immanent rate. While discrete-time Markov models (as Dynamic Bayesian Networks) introduce either inefficient computation or an information loss to reasoning about such processes, continuous-time Markov models assume either a discrete state space (as Continuous-Time Bayesian Networks), or a flat continuous state space (as stochastic dif- ferential equations). To address these problems, we present a new modeling class called Irregular-Time Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, allowing substantially more compact representations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learning temporal systems, provided that they are fully observed at the same irregularly spaced time-points, and a semiparametric subclass of ITBNs is introduced to allow further adaptation to the irregular nature of t...
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.
Pielaat, Annemarie; Boer, M.P.; Wijnands, Lucas M.; Hoek, van A.H.A.M.; Bouw, El; Barker, G.C.; Teunis, P.F.M.; Aarts, Henk J.M.; Franz, Eelco
2015-01-01
The potential for using whole genome sequencing (WGS) data in microbiological risk assessment (MRA) has been discussed on several occasions since the beginning of this century. Still, the proposed heuristic approaches have never been applied in a practical framework. This is due to the non-trivia
Bayesian Inference: with ecological applications
Link, William A.; Barker, Richard J.
2010-01-01
This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.
Bayesian Methods for Statistical Analysis
Puza, Borek
2015-01-01
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete c...
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...
Modeling microbial growth and dynamics.
Esser, Daniel S; Leveau, Johan H J; Meyer, Katrin M
2015-11-01
Modeling has become an important tool for widening our understanding of microbial growth in the context of applied microbiology and related to such processes as safe food production, wastewater treatment, bioremediation, or microbe-mediated mining. Various modeling techniques, such as primary, secondary and tertiary mathematical models, phenomenological models, mechanistic or kinetic models, reactive transport models, Bayesian network models, artificial neural networks, as well as agent-, individual-, and particle-based models have been applied to model microbial growth and activity in many applied fields. In this mini-review, we summarize the basic concepts of these models using examples and applications from food safety and wastewater treatment systems. We further review recent developments in other applied fields focusing on models that explicitly include spatial relationships. Using these examples, we point out the conceptual similarities across fields of application and encourage the combined use of different modeling techniques in hybrid models as well as their cross-disciplinary exchange. For instance, pattern-oriented modeling has its origin in ecology but may be employed to parameterize microbial growth models when experimental data are scarce. Models could also be used as virtual laboratories to optimize experimental design analogous to the virtual ecologist approach. Future microbial growth models will likely become more complex to benefit from the rich toolbox that is now available to microbial growth modelers.
Prediction of road accidents: A Bayesian hierarchical approach
DEFF Research Database (Denmark)
Deublein, Markus; Schubert, Matthias; Adey, Bryan T.;
2013-01-01
In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson......-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...
R.W. Strachan (Rodney); H.K. van Dijk (Herman)
2007-01-01
textabstractA Bayesian model averaging procedure is presented within the class of vector autoregressive (VAR) processes and applied to two empirical issues. First, stability of the "Great Ratios" in U.S. macro-economic time series is investigated, together with the presence and e¤ects of permanent s
A bayesian approach to classification criteria for spectacled eiders
Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.
1996-01-01
To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.
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.
Ruengsomwong, Supatjaree; La-Ongkham, Orawan; Jiang, Jiahui; Wannissorn, Bhusita; Nakayama, Jiro; Nitisinprasert, Sunee
2016-10-28
Pyrosequencing analysis of intestinal microflora from healthy Thai vegetarians and non-vegetarians exhibited 893 OTUs covering 189 species. The strong species indicators of vegetarians and non-vegetarians were Prevotella copri and Bacteroides vulgatus as well as bacteria close to Escherichia hermanii with % relative abundance of 16.9 and 4.5-4.7, respectively. Core gut microbiota of the vegetarian and non-vegetarian groups consisted of 11 and 20 different bacterial species, respectively, belonging to Actinobacteria, Firmicutes, and Proteobacteria commonly found in both groups. Two species, Faecalibacterium prausnitzii and Gemmiger formicilis, had a prevalence of 100% in both groups. Three species, Clostridium nexile, Eubacterium eligens, and P. copri, showed up in most vegetarians, whereas more diversity of Collinsella aerofaciens, Ruminococcus torques, various species of Bacteroides, Parabacteroides, Escherichia, and different species of Clostridium and Eubacterium were found in most non-vegetarians. Considering the correlation of personal characters, consumption behavior, and microbial groups, the age of non-vegetarians showed a strong positive correlation coefficient of 0.54 (p = 0.001) to Bacteroides uniformis but exhibited a moderate one to Alistipes finegoldii and B. vulgatus. Only a positive moderate correlation of body mass index and Parabacteroides distasonis appeared. Based on the significant abundance of potential pathogens, the microbiota of the non-vegetarian group showed an abundance of potential pathogen varieties of Bilophila wadsworthia, Escherichia coli, and E. hermannii, whereas that of the vegetarian group served for only Klebsiella pneumoniae. These results implied that the microbiota of vegetarians with high abundance of P. copri and low potential pathogen variety would be a way to maintain good health in Thais.
Macnab, Ying C
2009-04-30
This paper presents Bayesian multivariate disease mapping and ecological regression models that take into account errors in covariates. Bayesian hierarchical formulations of multivariate disease models and covariate measurement models, with related methods of estimation and inference, are developed as an integral part of a Bayesian disability adjusted life years (DALYs) methodology for the analysis of multivariate disease or injury data and associated ecological risk factors and for small area DALYs estimation, inference, and mapping. The methodology facilitates the estimation of multivariate small area disease and injury rates and associated risk effects, evaluation of DALYs and 'preventable' DALYs, and identification of regions to which disease or injury prevention resources may be directed to reduce DALYs. The methodology interfaces and intersects the Bayesian disease mapping methodology and the global burden of disease framework such that the impact of disease, injury, and risk factors on population health may be evaluated to inform community health, health needs, and priority considerations for disease and injury prevention. A burden of injury study on road traffic accidents in local health areas in British Columbia, Canada, is presented as an illustrative example.
Dynamic Batch Bayesian Optimization
Azimi, Javad; Fern, Xiaoli
2011-01-01
Bayesian optimization (BO) algorithms try to optimize an unknown function that is expensive to evaluate using minimum number of evaluations/experiments. Most of the proposed algorithms in BO are sequential, where only one experiment is selected at each iteration. This method can be time inefficient when each experiment takes a long time and more than one experiment can be ran concurrently. On the other hand, requesting a fix-sized batch of experiments at each iteration causes performance inefficiency in BO compared to the sequential policies. In this paper, we present an algorithm that asks a batch of experiments at each time step t where the batch size p_t is dynamically determined in each step. Our algorithm is based on the observation that the sequence of experiments selected by the sequential policy can sometimes be almost independent from each other. Our algorithm identifies such scenarios and request those experiments at the same time without degrading the performance. We evaluate our proposed method us...
Wilkes, G; Brassard, J; Edge, T A; Gannon, V; Jokinen, C C; Jones, T H; Neumann, N; Pintar, K D M; Ruecker, N; Schmidt, P J; Sunohara, M; Topp, E; Lapen, D R
2013-10-15
Over 3500 individual water samples, for 131 sampling times, targeting waterborne pathogens/fecal indicator bacteria were collected during a 7-year period from 4 sites along an intermittent stream running through a small livestock pasture system with and without cattle access-to-stream restriction measures. The study assessed the impact of cattle pasturing/riparian zone protection on: pathogen (bacterial, viral, parasite) occurrence, concentrations of fecal indicators, and quantitative microbial risk assessments (QMRA) of the risk of Cryptosporidium, Giardia and Escherichia coli O157:H7 infection in humans. Methodologies were developed to compute QMRA mean risks on the basis of water samples exhibiting potentially human infectious Cryptosporidium and E. coli based on genotyping Crytosporidium, and E. coli O157:H7 presence/absence information paired with enumerated E. coli. All Giardia spp. were considered infectious. No significant pasturing treatment effects were observed among pathogens, with the exception of Campylobacter spp. and E. coli O157:H7. Campylobacter spp. prevalence significantly decreased downstream through pasture treatments and E. coli O157:H7 was observed in a few instances in the middle of the unrestricted pasture. Densities of total coliform, fecal coliform, and E. coli reduced significantly downstream in the restricted pasture system, but not in the unrestricted system. Seasonal and flow conditions were associated with greater indicator bacteria densities, especially in the summer. Norovirus GII was detected at rates of 7-22% of samples for all monitoring sites, and rotavirus in 0-7% of samples for all monitoring sites; pasture treatment trends were not evident, however. Seasonal and stream flow variables (and their interactions) were relatively more important than pasture treatments for initially stratifying pathogen occurrence and higher fecal indicator bacteria densities. Significant positive associations among fecal indicator bacteria and
A Review of the Bayesian Occupancy Filter
Saval-Calvo, Marcelo; Medina-Valdés, Luis; Castillo-Secilla, José María; Cuenca-Asensi, Sergio; Martínez-Álvarez, Antonio; Villagrá, Jorge
2017-01-01
Autonomous vehicle systems are currently the object of intense research within scientific and industrial communities; however, many problems remain to be solved. One of the most critical aspects addressed in both autonomous driving and robotics is environment perception, since it consists of the ability to understand the surroundings of the vehicle to estimate risks and make decisions on future movements. In recent years, the Bayesian Occupancy Filter (BOF) method has been developed to evaluate occupancy by tessellation of the environment. A review of the BOF and its variants is presented in this paper. Moreover, we propose a detailed taxonomy where the BOF is decomposed into five progressive layers, from the level closest to the sensor to the highest abstract level of risk assessment. In addition, we present a study of implemented use cases to provide a practical understanding on the main uses of the BOF and its taxonomy. PMID:28208638
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 microsaccade detection
Mihali, Andra; van Opheusden, Bas; Ma, Wei Ji
2017-01-01
Microsaccades are high-velocity fixational eye movements, with special roles in perception and cognition. The default microsaccade detection method is to determine when the smoothed eye velocity exceeds a threshold. We have developed a new method, Bayesian microsaccade detection (BMD), which performs inference based on a simple statistical model of eye positions. In this model, a hidden state variable changes between drift and microsaccade states at random times. The eye position is a biased random walk with different velocity distributions for each state. BMD generates samples from the posterior probability distribution over the eye state time series given the eye position time series. Applied to simulated data, BMD recovers the “true” microsaccades with fewer errors than alternative algorithms, especially at high noise. Applied to EyeLink eye tracker data, BMD detects almost all the microsaccades detected by the default method, but also apparent microsaccades embedded in high noise—although these can also be interpreted as false positives. Next we apply the algorithms to data collected with a Dual Purkinje Image eye tracker, whose higher precision justifies defining the inferred microsaccades as ground truth. When we add artificial measurement noise, the inferences of all algorithms degrade; however, at noise levels comparable to EyeLink data, BMD recovers the “true” microsaccades with 54% fewer errors than the default algorithm. Though unsuitable for online detection, BMD has other advantages: It returns probabilities rather than binary judgments, and it can be straightforwardly adapted as the generative model is refined. We make our algorithm available as a software package. PMID:28114483
Maximum margin Bayesian network classifiers.
Pernkopf, Franz; Wohlmayr, Michael; Tschiatschek, Sebastian
2012-03-01
We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.
Xiao, Shumin; An, Wei; Chen, Zhimin; Zhang, Dongqing; Yu, Jianwei; Yang, Min
2012-09-01
A comprehensive quantitative microbial risk assessment (QMRA) of Cryptosporidium infection, considering pathogen removal efficiency, different exposure pathways and different susceptible subpopulations, was performed based on the result of a survey of source water from 66 waterworks in 33 major cities across China. The Cryptosporidium concentrations in source water were 0-6 oocysts/10 L, with a mean value of 0.7 oocysts/10 L. The annual diarrhea morbidity caused by Cryptosporidium in drinking water was estimated to be 2701 (95% confidence interval (CI): 138-9381) cases per 100,000 immunodeficient persons and 148 (95% CI: 1-603) cases per 100,000 immunocompetent persons, giving an overall rate of 149.0 (95% CI: 1.3-606.4) cases per 100,000 population. The cryptosporidiosis burden associated with drinking water treated with the conventional process was calculated to be 8.31 × 10(-6) (95% CI: 0.34-30.93 × 10(-6)) disability-adjusted life years (DALYs) per person per year, which was higher than the reference risk level suggested by the World Health Organization (WHO), but lower than that suggested by the United States Environmental Protection Agency (USEPA). Sixty-six percent of the total health burden due to cryptosporidiosis that occurred in the immunodeficient subpopulation, and 90% of the total DALYs was attributed to adults aged 15-59 years. The sensitivity analysis highlighted the great importance of stability of the treatment process and the importance of watershed protection. The results of this study will be useful in better evaluating and reducing the burden of Cryptosporidium infection.
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 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 ...
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...
Attention in a bayesian framework
DEFF Research Database (Denmark)
Whiteley, Louise Emma; Sahani, Maneesh
2012-01-01
The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models...... of perception, and use this observation to frame a new computational account of the need for, and action of, attention - unifying diverse attentional phenomena in a way that goes beyond previous inferential, probabilistic and Bayesian models. Attentional effects are most evident in cluttered environments......, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental...
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.
Energy Technology Data Exchange (ETDEWEB)
Fuhrimann, Samuel, E-mail: samuel.fuhrimann@unibas.ch [Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel (Switzerland); University of Basel, Basel (Switzerland); Pham-Duc, Phuc [Center for Public Health and Ecosystem Research, Hanoi School of Public Health, Hanoi (Viet Nam); Cissé, Guéladio [Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel (Switzerland); University of Basel, Basel (Switzerland); Tram, Nguyen Thuy; Thu Ha, Hoang [Department of Microbiology, National Institute of Hygiene and Epidemiology, Hanoi (Viet Nam); Dung, Do Trung [Department of Parasitology, National Institute of Malaria, Parasitology, and Entomology, Hanoi (Viet Nam); Ngoc, Pham [Department of Animal Hygiene, National Institute for Veterinary Research, Hanoi (Viet Nam); Nguyen-Viet, Hung [Center for Public Health and Ecosystem Research, Hanoi School of Public Health, Hanoi (Viet Nam); International Livestock Research Institute, Hanoi (Viet Nam); Anh Vuong, Tuan [Department of Microbiology, National Institute of Hygiene and Epidemiology, Hanoi (Viet Nam); Utzinger, Jürg; Schindler, Christian; Winkler, Mirko S. [Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel (Switzerland); University of Basel, Basel (Switzerland)
2016-10-01
The use of wastewater in agriculture and aquaculture has a long tradition throughout Asia. For example, in Hanoi, it creates important livelihood opportunities for > 500,000 farmers in peri-urban communities. Discharge of domestic effluents pollute the water streams with potential pathogenic organisms posing a public health threat to farmers and consumers of wastewater-fed foodstuff. We determined the effectiveness of Hanoi's wastewater conveyance system, placing particular emphasis on the quality of wastewater used in agriculture and aquaculture. Between April and June 2014, a total of 216 water samples were obtained from 24 sampling points and the concentrations of total coliforms (TC), Escherichia coli, Salmonella spp. and helminth eggs determined. Despite applied wastewater treatment, agricultural field irrigation water was heavily contaminated with TC (1.3 × 10{sup 7} colony forming unit (CFU)/100 mL), E. coli (1.1 × 10{sup 6} CFU/100 mL) and Salmonella spp. (108 most probable number (MPN)/100 mL). These values are 110-fold above Vietnamese discharge limits for restricted agriculture and 260-fold above the World Health Organization (WHO)'s tolerable safety limits for unrestricted agriculture. Mean helminth egg concentrations were below WHO tolerable levels in all study systems (< 1 egg/L). Hence, elevated levels of bacterial contamination, but not helminth infections, pose a major health risk for farmers and consumers of wastewater fed-products. We propose a set of control measures that might protect the health of exposed population groups without compromising current urban farming activities. This study presents an important example for sanitation safety planning in a rapidly expanding Asian city and can guide public and private entities working towards Sustainable Development Goal target 6.3, that is to improve water quality by reducing pollution, halving the proportion of untreated wastewater and increasing recycling and safe reuse globally
Piñar, Guadalupe; Piombino-Mascali, Dario; Maixner, Frank; Zink, Albert; Sterflinger, Katja
2013-11-01
The Capuchin Catacombs of Palermo contain over 1800 preserved bodies dating from the 16th to 20th centuries AD and showing evidence of biodeterioration. An extensive microbiological and molecular investigation was recently performed. Samples were taken from skin, muscle, hair, bone, stuffing materials, clothes, and surrounding walls as well as from the indoor air. In this study, we witnessed that the different degradation phenomena observed on the variety of materials located at the Capuchin Catacombs of Palermo are biological in origin. Molecular techniques showed the dominance of halophilic species of the domains Bacteria and Archaea on the walls and - as a result of salt emanating from the walls - on the mummies themselves. Nevertheless, specialized microorganisms belonging to taxa well-known for their cellulolytic and proteolytic activities were detected on clothes and stuffing material, and on skin, muscle, hair, and bone, respectively. This specialized microbiota is threatening the conservation of the mummies themselves. Additionally, sequences related to the human skin microbiome and to some pathogenic Bacteria (order Clostridiales) and fungi (genus Phialosimplex) were identified on samples derived from the mummies. Furthermore, a phosphate-reducing fungus, Penicillium radicum, was detected on bone. Finally, the high concentration of airborne fungal spores is not conducive to the conservation of the human remains and is posing a potential health risk for visitors.
Bayesian Missile System Reliability from Point Estimates
2014-10-28
OCT 2014 2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Bayesian Missile System Reliability from Point Estimates 5a. CONTRACT...Principle (MEP) to convert point estimates to probability distributions to be used as priors for Bayesian reliability analysis of missile data, and...illustrate this approach by applying the priors to a Bayesian reliability model of a missile system. 15. SUBJECT TERMS priors, Bayesian , missile
Perception, illusions and Bayesian inference.
Nour, Matthew M; Nour, Joseph M
2015-01-01
Descriptive psychopathology makes a distinction between veridical perception and illusory perception. In both cases a perception is tied to a sensory stimulus, but in illusions the perception is of a false object. This article re-examines this distinction in light of new work in theoretical and computational neurobiology, which views all perception as a form of Bayesian statistical inference that combines sensory signals with prior expectations. Bayesian perceptual inference can solve the 'inverse optics' problem of veridical perception and provides a biologically plausible account of a number of illusory phenomena, suggesting that veridical and illusory perceptions are generated by precisely the same inferential mechanisms.
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....
A Bayesian Nonparametric Approach to Test Equating
Karabatsos, George; Walker, Stephen G.
2009-01-01
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
Bayesian Model Averaging for Propensity Score Analysis
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
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 b...
Bayesian stable isotope mixing models
In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching Bayesian statistical framework which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixtur...
Naive Bayesian for Email Filtering
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The paper presents a method of email filter based on Naive Bayesian theory that can effectively filter junk mail and illegal mail. Furthermore, the keys of implementation are discussed in detail. The filtering model is obtained from training set of email. The filtering can be done without the users specification of filtering rules.
Bayesian analysis of binary sequences
Torney, David C.
2005-03-01
This manuscript details Bayesian methodology for "learning by example", with binary n-sequences encoding the objects under consideration. Priors prove influential; conformable priors are described. Laplace approximation of Bayes integrals yields posterior likelihoods for all n-sequences. This involves the optimization of a definite function over a convex domain--efficiently effectuated by the sequential application of the quadratic program.
Bayesian NL interpretation and learning
Zeevat, H.
2011-01-01
Everyday natural language communication is normally successful, even though contemporary computational linguistics has shown that NL is characterised by very high degree of ambiguity and the results of stochastic methods are not good enough to explain the high success rate. Bayesian natural language
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 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...
Bayesian Classification of Image Structures
DEFF Research Database (Denmark)
Goswami, Dibyendu; Kalkan, Sinan; Krüger, Norbert
2009-01-01
In this paper, we describe work on Bayesian classi ers for distinguishing between homogeneous structures, textures, edges and junctions. We build semi-local classiers from hand-labeled images to distinguish between these four different kinds of structures based on the concept of intrinsic dimensi...
3-D contextual Bayesian classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
In this paper we will consider extensions of a series of Bayesian 2-D contextual classification pocedures proposed by Owen (1984) Hjort & Mohn (1984) and Welch & Salter (1971) and Haslett (1985) to 3 spatial dimensions. It is evident that compared to classical pixelwise classification further...
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...
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...
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 Evidence and Model Selection
Knuth, Kevin H; Malakar, Nabin K; Mubeen, Asim M; Placek, Ben
2014-01-01
In this paper we review the concept of the Bayesian evidence and its application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Application to several practical examples within the context of signal processing are discussed.
Differentiated Bayesian Conjoint Choice Designs
Z. Sándor (Zsolt); M. Wedel (Michel)
2003-01-01
textabstractPrevious conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about
Bayesian Alternation During Tactile Augmentation
Directory of Open Access Journals (Sweden)
Caspar Mathias Goeke
2016-10-01
Full Text Available A large number of studies suggest that the integration of multisensory signals by humans is well described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition, rotation only (native condition, and both augmented and native information (bimodal condition. Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants’ responses with a probit model and calculated the just notable difference (JND. Then we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χred2 = 1.67 than the Bayesian integration model (χred2= 4.34. Slightly higher accuracy showed a non-Bayesian winner takes all model (χred2= 1.64, which either used only native or only augmented values per subject for prediction. However the performance of the Bayesian alternation model could be substantially improved (χred2= 1.09 utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in
A Review of the Bayesian Occupancy Filter.
Saval-Calvo, Marcelo; Medina-Valdés, Luis; Castillo-Secilla, José María; Cuenca-Asensi, Sergio; Martínezlvarez, Antonio; Villagrá, Jorge
2017-02-10
Autonomous vehicle systems are currently the object of intense research within scientiﬁc and industrial communities; however, many problems remain to be solved. One of the most critical aspects addressed in both autonomous driving and robotics is environment perception, since it consists of the ability to understand the surroundings of the vehicle to estimate risks and make decisions on future movements. In recent years, the Bayesian Occupancy Filter (BOF) method has been developed to evaluate occupancy by tessellation of the environment. A review of the BOF and its variants is presented in this paper. Moreover, we propose a detailed taxonomy where the BOF is decomposed into ﬁve progressive layers, from the level closest to the sensor to the highest abstractlevelofriskassessment. Inaddition,wepresentastudyofimplementedusecasestoprovide a practical understanding on the main uses of the BOF and its taxonomy.
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 Review of the Bayesian Occupancy Filter
Directory of Open Access Journals (Sweden)
Marcelo Saval-Calvo
2017-02-01
Full Text Available Autonomous vehicle systems are currently the object of intense research within scientiﬁc and industrial communities; however, many problems remain to be solved. One of the most critical aspects addressed in both autonomous driving and robotics is environment perception, since it consists of the ability to understand the surroundings of the vehicle to estimate risks and make decisions on future movements. In recent years, the Bayesian Occupancy Filter (BOF method has been developed to evaluate occupancy by tessellation of the environment. A review of the BOF and its variants is presented in this paper. Moreover, we propose a detailed taxonomy where the BOF is decomposed into ﬁve progressive layers, from the level closest to the sensor to the highest abstractlevelofriskassessment. Inaddition,wepresentastudyofimplementedusecasestoprovide a practical understanding on the main uses of the BOF and its taxonomy.
Tromp, S O; Rijgersberg, H; Franz, E
2010-10-01
Quantitative microbial risk assessments do not usually account for the planning and ordering mechanisms (logistics) of a food supply chain. These mechanisms and consumer demand determine the storage and delay times of products. The aim of this study was to quantitatively assess the difference between simulating supply chain logistics (MOD) and assuming fixed storage times (FIX) in microbial risk estimation for the supply chain of fresh-cut leafy green vegetables destined for working-canteen salad bars. The results of the FIX model were previously published (E. Franz, S. O. Tromp, H. Rijgersberg, and H. J. van der Fels-Klerx, J. Food Prot. 73:274-285, 2010). Pathogen growth was modeled using stochastic discrete-event simulation of the applied logistics concept. The public health effects were assessed by conducting an exposure assessment and risk characterization. The relative growths of Escherichia coli O157 (17%) and Salmonella enterica (15%) were identical in the MOD and FIX models. In contrast, the relative growth of Listeria monocytogenes was considerably higher in the MOD model (1,156%) than in the FIX model (194%). The probability of L. monocytogenes infection in The Netherlands was higher in the MOD model (5.18×10(-8)) than in the FIX model (1.23×10(-8)). The risk of listeriosis-induced fetal mortality in the perinatal population increased from 1.24×10(-4) (FIX) to 1.66×10(-4) (MOD). Modeling the probabilistic nature of supply chain logistics is of additional value for microbial risk assessments regarding psychrotrophic pathogens in food products for which time and temperature are the postharvest preventive measures in guaranteeing food safety.
STATISTICAL BAYESIAN ANALYSIS OF EXPERIMENTAL DATA.
Directory of Open Access Journals (Sweden)
AHLAM LABDAOUI
2012-12-01
Full Text Available The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computational tools used in modern Bayesian econometrics. Some of the most important methods of posterior simulation are Monte Carlo integration, importance sampling, Gibbs sampling and the Metropolis- Hastings algorithm. The Bayesian should also be able to put the theory and computational tools together in the context of substantive empirical problems. We focus primarily on recent developments in Bayesian computation. Then we focus on particular models. Inevitably, we combine theory and computation in the context of particular models. Although we have tried to be reasonably complete in terms of covering the basic ideas of Bayesian theory and the computational tools most commonly used by the Bayesian, there is no way we can cover all the classes of models used in econometrics. We propose to the user of analysis of variance and linear regression model.
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...
Bayesian geostatistical modeling of leishmaniasis incidence in Brazil.
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Dimitrios-Alexios Karagiannis-Voules
Full Text Available BACKGROUND: Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries. METHODOLOGY: We applied Bayesian geostatistical negative binomial models to analyze reported incidence data of cutaneous and visceral leishmaniasis in Brazil covering a 10-year period (2001-2010. Particular emphasis was placed on spatial and temporal patterns. The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference. Bayesian variable selection was employed to determine the most important climatic, environmental, and socioeconomic predictors of cutaneous and visceral leishmaniasis. PRINCIPAL FINDINGS: For both types of leishmaniasis, precipitation and socioeconomic proxies were identified as important risk factors. The predicted number of cases in 2010 were 30,189 (standard deviation [SD]: 7,676 for cutaneous leishmaniasis and 4,889 (SD: 288 for visceral leishmaniasis. Our risk maps predicted the highest numbers of infected people in the states of Minas Gerais and Pará for visceral and cutaneous leishmaniasis, respectively. CONCLUSIONS/SIGNIFICANCE: Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence.
Application of a predictive Bayesian model to environmental accounting.
Anex, R P; Englehardt, J D
2001-03-30
Environmental accounting techniques are intended to capture important environmental costs and benefits that are often overlooked in standard accounting practices. Environmental accounting methods themselves often ignore or inadequately represent large but highly uncertain environmental costs and costs conditioned by specific prior events. Use of a predictive Bayesian model is demonstrated for the assessment of such highly uncertain environmental and contingent costs. The predictive Bayesian approach presented generates probability distributions for the quantity of interest (rather than parameters thereof). A spreadsheet implementation of a previously proposed predictive Bayesian model, extended to represent contingent costs, is described and used to evaluate whether a firm should undertake an accelerated phase-out of its PCB containing transformers. Variability and uncertainty (due to lack of information) in transformer accident frequency and severity are assessed simultaneously using a combination of historical accident data, engineering model-based cost estimates, and subjective judgement. Model results are compared using several different risk measures. Use of the model for incorporation of environmental risk management into a company's overall risk management strategy is discussed.
Li, Shi; Mukherjee, Bhramar; Batterman, Stuart; Ghosh, Malay
2013-12-01
Case-crossover designs are widely used to study short-term exposure effects on the risk of acute adverse health events. While the frequentist literature on this topic is vast, there is no Bayesian work in this general area. The contribution of this paper is twofold. First, the paper establishes Bayesian equivalence results that require characterization of the set of priors under which the posterior distributions of the risk ratio parameters based on a case-crossover and time-series analysis are identical. Second, the paper studies inferential issues under case-crossover designs in a Bayesian framework. Traditionally, a conditional logistic regression is used for inference on risk-ratio parameters in case-crossover studies. We consider instead a more general full likelihood-based approach which makes less restrictive assumptions on the risk functions. Formulation of a full likelihood leads to growth in the number of parameters proportional to the sample size. We propose a semi-parametric Bayesian approach using a Dirichlet process prior to handle the random nuisance parameters that appear in a full likelihood formulation. We carry out a simulation study to compare the Bayesian methods based on full and conditional likelihood with the standard frequentist approaches for case-crossover and time-series analysis. The proposed methods are illustrated through the Detroit Asthma Morbidity, Air Quality and Traffic study, which examines the association between acute asthma risk and ambient air pollutant concentrations.
Bayesian versus 'plain-vanilla Bayesian' multitarget statistics
Mahler, Ronald P. S.
2004-08-01
Finite-set statistics (FISST) is a direct generalization of single-sensor, single-target Bayes statistics to the multisensor-multitarget realm, based on random set theory. Various aspects of FISST are being investigated by several research teams around the world. In recent years, however, a few partisans have claimed that a "plain-vanilla Bayesian approach" suffices as down-to-earth, "straightforward," and general "first principles" for multitarget problems. Therefore, FISST is mere mathematical "obfuscation." In this and a companion paper I demonstrate the speciousness of these claims. In this paper I summarize general Bayes statistics, what is required to use it in multisensor-multitarget problems, and why FISST is necessary to make it practical. Then I demonstrate that the "plain-vanilla Bayesian approach" is so heedlessly formulated that it is erroneous, not even Bayesian denigrates FISST concepts while unwittingly assuming them, and has resulted in a succession of algorithms afflicted by inherent -- but less than candidly acknowledged -- computational "logjams."
Bayesian Analysis for Linearized Multi-Stage Models in Quantal Bioassay.
Kuo, Lynn; Cohen, Michael P.
Bayesian methods for estimating dose response curves in quantal bioassay are studied. A linearized multi-stage model is assumed for the shape of the curves. A Gibbs sampling approach with data augmentation is employed to compute the Bayes estimates. In addition, estimation of the "relative additional risk" and the "risk specific…
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 ...
Deep Learning and Bayesian Methods
Directory of Open Access Journals (Sweden)
Prosper Harrison B.
2017-01-01
Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Deep Learning and Bayesian Methods
Prosper, Harrison B.
2017-03-01
A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Bayesian Source Separation and Localization
Knuth, K H
1998-01-01
The problem of mixed signals occurs in many different contexts; one of the most familiar being acoustics. The forward problem in acoustics consists of finding the sound pressure levels at various detectors resulting from sound signals emanating from the active acoustic sources. The inverse problem consists of using the sound recorded by the detectors to separate the signals and recover the original source waveforms. In general, the inverse problem is unsolvable without additional information. This general problem is called source separation, and several techniques have been developed that utilize maximum entropy, minimum mutual information, and maximum likelihood. In previous work, it has been demonstrated that these techniques can be recast in a Bayesian framework. This paper demonstrates the power of the Bayesian approach, which provides a natural means for incorporating prior information into a source model. An algorithm is developed that utilizes information regarding both the statistics of the amplitudes...
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.
Directory of Open Access Journals (Sweden)
Gabriel Hideki Vatanabe Brunello
Full Text Available 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.
Bayesian analysis for kaon photoproduction
Energy Technology Data Exchange (ETDEWEB)
Marsainy, T., E-mail: tmart@fisika.ui.ac.id; Mart, T., E-mail: tmart@fisika.ui.ac.id [Department Fisika, FMIPA, Universitas Indonesia, Depok 16424 (Indonesia)
2014-09-25
We have investigated contribution of the nucleon resonances in the kaon photoproduction process by using an established statistical decision making method, i.e. the Bayesian method. This method does not only evaluate the model over its entire parameter space, but also takes the prior information and experimental data into account. The result indicates that certain resonances have larger probabilities to contribute to the process.
Bayesian priors and nuisance parameters
Gupta, Sourendu
2016-01-01
Bayesian techniques are widely used to obtain spectral functions from correlators. We suggest a technique to rid the results of nuisance parameters, ie, parameters which are needed for the regularization but cannot be determined from data. We give examples where the method works, including a pion mass extraction with two flavours of staggered quarks at a lattice spacing of about 0.07 fm. We also give an example where the method does not work.
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
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.
Bayesian Sampling using Condition Indicators
DEFF Research Database (Denmark)
Faber, Michael H.; Sørensen, John Dalsgaard
2002-01-01
. This 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....... It is shown how half-cell potential measurements may be utilized to update the probability of excessive repair after 50 years....
Pielaat, Annemarie; Boer, Martin P; Wijnands, Lucas M; van Hoek, Angela H A M; Bouw, El; Barker, Gary C; Teunis, Peter F M; Aarts, Henk J M; Franz, Eelco
2015-11-20
The potential for using whole genome sequencing (WGS) data in microbiological risk assessment (MRA) has been discussed on several occasions since the beginning of this century. Still, the proposed heuristic approaches have never been applied in a practical framework. This is due to the non-trivial problem of mapping microbial information consisting of thousands of loci onto a probabilistic scale for risks. The paradigm change for MRA involves translation of multidimensional microbial genotypic information to much reduced (integrated) phenotypic information and onwards to a single measure of human risk (i.e. probability of illness). In this paper a first approach in methodology development is described for the application of WGS data in MRA; this is supported by a practical example. That is, combining genetic data (single nucleotide polymorphisms; SNPs) for Shiga toxin-producing Escherichia coli (STEC) O157 with phenotypic data (in vitro adherence to epithelial cells as a proxy for virulence) leads to hazard identification in a Genome Wide Association Study (GWAS). This application revealed practical implications when using SNP data for MRA. These can be summarized by considering the following main issues: optimum sample size for valid inference on population level, correction for population structure, quantification and calibration of results, reproducibility of the analysis, links with epidemiological data, anchoring and integration of results into a systems biology approach for the translation of molecular studies to human health risk. Future developments in genetic data analysis for MRA should aim at resolving the mapping problem of processing genetic sequences to come to a quantitative description of risk. The development of a clustering scheme focusing on biologically relevant information of the microbe involved would be a useful approach in molecular data reduction for risk assessment.
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 Inversion of Seabed Scattering Data
2014-09-30
Bayesian Inversion of Seabed Scattering Data (Special Research Award in Ocean Acoustics) Gavin A.M.W. Steininger School of Earth & Ocean...project are to carry out joint Bayesian inversion of scattering and reflection data to estimate the in-situ seabed scattering and geoacoustic parameters...valid OMB control number. 1. REPORT DATE 30 SEP 2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Bayesian
Anomaly Detection and Attribution Using Bayesian Networks
2014-06-01
UNCLASSIFIED Anomaly Detection and Attribution Using Bayesian Networks Andrew Kirk, Jonathan Legg and Edwin El-Mahassni National Security and...detection in Bayesian networks , en- abling both the detection and explanation of anomalous cases in a dataset. By exploiting the structure of a... Bayesian network , our algorithm is able to efficiently search for local maxima of data conflict between closely related vari- ables. Benchmark tests using
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 differentiating these circuits in time linear in their size. We report on experimental results showing the successful compilation, and efficient inference, on relational Bayesian networks whose {\\primula}--generated propositional instances have thousands of variables, and whose jointrees have clusters...
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.
SYNTHESIZED EXPECTED BAYESIAN METHOD OF PARAMETRIC ESTIMATE
Institute of Scientific and Technical Information of China (English)
Ming HAN; Yuanyao DING
2004-01-01
This paper develops a new method of parametric estimate, which is named as "synthesized expected Bayesian method". When samples of products are tested and no failure events occur, thedefinition of expected Bayesian estimate is introduced and the estimates of failure probability and failure rate are provided. After some failure information is introduced by making an extra-test, a synthesized expected Bayesian method is defined and used to estimate failure probability, failure rateand some other parameters in exponential distribution and Weibull distribution of populations. Finally,calculations are performed according to practical problems, which show that the synthesized expected Bayesian method is feasible and easy to operate.
Learning dynamic Bayesian networks with mixed variables
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned....... An automated procedure for specifying prior distributions for the parameters in a dynamic Bayesian network is presented. It is a simple extension of the procedure for the ordinary Bayesian networks. Finally the W¨olfer?s sunspot numbers are analyzed....
Variational bayesian method of estimating variance components.
Arakawa, Aisaku; Taniguchi, Masaaki; Hayashi, Takeshi; Mikawa, Satoshi
2016-07-01
We developed a Bayesian analysis approach by using a variational inference method, a so-called variational Bayesian method, to determine the posterior distributions of variance components. This variational Bayesian method and an alternative Bayesian method using Gibbs sampling were compared in estimating genetic and residual variance components from both simulated data and publically available real pig data. In the simulated data set, we observed strong bias toward overestimation of genetic variance for the variational Bayesian method in the case of low heritability and low population size, and less bias was detected with larger population sizes in both methods examined. The differences in the estimates of variance components between the variational Bayesian and the Gibbs sampling were not found in the real pig data. However, the posterior distributions of the variance components obtained with the variational Bayesian method had shorter tails than those obtained with the Gibbs sampling. Consequently, the posterior standard deviations of the genetic and residual variances of the variational Bayesian method were lower than those of the method using Gibbs sampling. The computing time required was much shorter with the variational Bayesian method than with the method using Gibbs sampling.
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 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 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
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....
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 homeopathy: talking normal again.
Rutten, A L B
2007-04-01
Homeopathy has a communication problem: important homeopathic concepts are not understood by conventional colleagues. Homeopathic terminology seems to be comprehensible only after practical experience of homeopathy. The main problem lies in different handling of diagnosis. In conventional medicine diagnosis is the starting point for randomised controlled trials to determine the effect of treatment. In homeopathy diagnosis is combined with other symptoms and personal traits of the patient to guide treatment and predict response. Broadening our scope to include diagnostic as well as treatment research opens the possibility of multi factorial reasoning. Adopting Bayesian methodology opens the possibility of investigating homeopathy in everyday practice and of describing some aspects of homeopathy in conventional terms.
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.
Advances in Bayesian Modeling in Educational Research
Levy, Roy
2016-01-01
In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…
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....
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
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…
The Bayesian Revolution Approaches Psychological Development
Shultz, Thomas R.
2007-01-01
This commentary reviews five articles that apply Bayesian ideas to psychological development, some with psychology experiments, some with computational modeling, and some with both experiments and modeling. The reviewed work extends the current Bayesian revolution into tasks often studied in children, such as causal learning and word learning, and…
Bayesian analysis of exoplanet and binary orbits
Schulze-Hartung, Tim; Launhardt, Ralf; 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.
Bayesian Network for multiple hypthesis tracking
W.P. Zajdel; B.J.A. Kröse
2002-01-01
For a flexible camera-to-camera tracking of multiple objects we model the objects behavior with a Bayesian network and combine it with the multiple hypohesis framework that associates observations with objects. Bayesian networks offer a possibility to factor complex, joint distributions into a produ
Bayesian Network Assessment Method for Civil Aviation Safety Based on Flight Delays
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 ...
Bayesian Option Pricing Framework with Stochastic Volatility for FX Data
Directory of Open Access Journals (Sweden)
Ying Wang
2016-12-01
Full Text Available The application of stochastic volatility (SV models in the option pricing literature usually assumes that the market has sufficient option data to calibrate the model’s risk-neutral parameters. When option data are insufficient or unavailable, market practitioners must estimate the model from the historical returns of the underlying asset and then transform the resulting model into its risk-neutral equivalent. However, the likelihood function of an SV model can only be expressed in a high-dimensional integration, which makes the estimation a highly challenging task. The Bayesian approach has been the classical way to estimate SV models under the data-generating (physical probability measure, but the transformation from the estimated physical dynamic into its risk-neutral counterpart has not been addressed. Inspired by the generalized autoregressive conditional heteroskedasticity (GARCH option pricing approach by Duan in 1995, we propose an SV model that enables us to simultaneously and conveniently perform Bayesian inference and transformation into risk-neutral dynamics. Our model relaxes the normality assumption on innovations of both return and volatility processes, and our empirical study shows that the estimated option prices generate realistic implied volatility smile shapes. In addition, the volatility premium is almost flat across strike prices, so adding a few option data to the historical time series of the underlying asset can greatly improve the estimation of option prices.
Barshi, Immanuel
2016-01-01
Speaking up, i.e. expressing ones concerns, is a critical piece of effective communication. Yet, we see many situations in which crew members have concerns and still remain silent. Why would that be the case? And how can we assess the risks of speaking up vs. the risks of keeping silent? And once we do make up our minds to speak up, how should we go about it? Our workshop aims to answer these questions, and to provide us all with practical tools for effective risk assessment and effective speaking-up strategies..
Hepatitis disease detection using Bayesian theory
Maseleno, Andino; Hidayati, Rohmah Zahroh
2017-02-01
This paper presents hepatitis disease diagnosis using a Bayesian theory for better understanding of the theory. In this research, we used a Bayesian theory for detecting hepatitis disease and displaying the result of diagnosis process. Bayesian algorithm theory is rediscovered and perfected by Laplace, the basic idea is using of the known prior probability and conditional probability density parameter, based on Bayes theorem to calculate the corresponding posterior probability, and then obtained the posterior probability to infer and make decisions. Bayesian methods combine existing knowledge, prior probabilities, with additional knowledge derived from new data, the likelihood function. The initial symptoms of hepatitis which include malaise, fever and headache. The probability of hepatitis given the presence of malaise, fever, and headache. The result revealed that a Bayesian theory has successfully identified the existence of hepatitis disease.
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.
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.
Kwon, Deukwoo; Hoffman, F Owen; Moroz, Brian E; Simon, Steven L
2016-02-10
Most conventional risk analysis methods rely on a single best estimate of exposure per person, which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2376 subjects who were exposed to fallout from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulations and comparisons against conventional regression risk analysis methods. When the estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple a priori plausible draws of dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods and better coverage, that is, a markedly increased capability to include the true risk coefficient within the 95% credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure.
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
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....
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.
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.
On Bayesian System Reliability Analysis
Energy Technology Data Exchange (ETDEWEB)
Soerensen Ringi, M.
1995-05-01
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.
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...
Bayesian Inference with Optimal Maps
Moselhy, Tarek A El
2011-01-01
We present a new approach to Bayesian inference that entirely avoids Markov chain simulation, by constructing a map that pushes forward the prior measure to the posterior measure. Existence and uniqueness of a suitable measure-preserving map is established by formulating the problem in the context of optimal transport theory. We discuss various means of explicitly parameterizing the map and computing it efficiently through solution of an optimization problem, exploiting gradient information from the forward model when possible. The resulting algorithm overcomes many of the computational bottlenecks associated with Markov chain Monte Carlo. Advantages of a map-based representation of the posterior include analytical expressions for posterior moments and the ability to generate arbitrary numbers of independent posterior samples without additional likelihood evaluations or forward solves. The optimization approach also provides clear convergence criteria for posterior approximation and facilitates model selectio...
Multiview Bayesian Correlated Component Analysis
DEFF Research Database (Denmark)
Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai
2015-01-01
Correlated component analysis as proposed by Dmochowski, Sajda, Dias, and Parra (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks are iden......Correlated component analysis as proposed by Dmochowski, Sajda, Dias, and Parra (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....
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 ...
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...
Bayesian Inference in Queueing Networks
Sutton, Charles
2010-01-01
Modern Web services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are modeled by networks of queues, where one queue models each of the individual computers in the system. A key challenge is that the data is incomplete, because recording detailed information about every request to a heavily used system can require unacceptable overhead. In this paper we develop a Bayesian perspective on queueing models in which the arrival and departure times that are not observed are treated as latent variables. Underlying this viewpoint is the observation that a queueing model defines a deterministic transformation between the data and a set of independent variables called the service times. With this viewpoint in hand, we sample from the posterior distribution over missing data and model...
A Bayesian Reflection on Surfaces
Directory of Open Access Journals (Sweden)
David R. Wolf
1999-10-01
Full Text Available Abstract: The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a continuous object and not representable in a finite manner; the tradeoff between accuracy of representation in terms of information learned, and memory or storage capacity in bits; the approximation of probability distributions so that a maximal amount of information about the object being inferred is preserved; an information theoretic justification for multigrid methodology. The maximally informative field inference framework is described in full generality and denoted the Generalized Kalman Filter. The Generalized Kalman Filter allows the update of field knowledge from previous knowledge at any scale, and new data, to new knowledge at any other scale. An application example instance, the inference of continuous surfaces from measurements (for example, camera image data, is presented.
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
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark
2006-01-01
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 evaluating and differentiating these circuits in time linear in their size. We report on experimental results showing successful compilation and efficient inference on relational Bayesian networks, whose PRIMULA--generated propositional instances have thousands of variables, and whose jointrees have clusters...
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.
Institute of Scientific and Technical Information of China (English)
朱磊; 梁晓龙; 张佳强
2016-01-01
In order to fully understand the causes of the civil aviation and military flight conflict and near-miss potential accident,and its causes are focused prevention and probability of accurate prediction of events.first,a fault tree model has been set up to analyze the likely causes that may lead to the civil aviation and military flight conflict and near-miss potential accident,summarized the basic events which lead to the top events.The minimum cut sets and structure importance of basic events have been worked out.Then,the basic events probability assignment reasonably.It is concluded that the structure of the basic events importance,based on this,the probability of such basic events affect the occurrence probability of the top event hace been pointed out.In the end,the Bayesian Network diagram is also mapped out,the probability and the probability of the top middle events are calanlated,as well compared with monitoring results,it has proved the feasibility of the method.%为充分了解当前军民航飞行冲突、危险接近事故征候的成因，并对其发生原因进行有重点的预防和对事件发生概率的精确预测，首先建立事故树模型，对其原因进行分析；总结导致其发生的基本事件，计算事故树模型的最小割集和各个基本事件的结构重要度；其次对军民航飞行冲突、危险接近事故征候的发生概率进行合理的取值，得出基本事件的概率重要度，指出基本事件发生概率对顶事件发生概率的影响；最后构建军民航飞行冲突、危险接近事故征候贝叶斯网络模型，计算中间事件的条件概率和顶事件发生概率，并与实际结果进行对比，验证方法可行性。
Ouyang, Bichun; Sinha, Debajyoti; Slate, Elizabeth H; Van Bakel, Adrian B
2013-07-10
For a heart transplant patient, the risk of graft rejection and risk of death are likely to be associated. Two fully specified Bayesian models for recurrent events with dependent termination are applied to investigate the potential relationships between these two types of risk as well as association with risk factors. We particularly focus on the choice of priors, selection of the appropriate prediction model, and prediction methods for these two types of risk for an individual patient. Our prediction tools can be easily implemented and helpful to physicians for setting heart transplant patients' biopsy schedule.
Statistical Considerations in Environmental Microbial Forensics.
McBride, Graham; Gilpin, Brent
2016-08-01
In environmental microbial forensics, as in other pursuits, statistical calculations are sometimes inappropriately applied, giving rise to the appearance of support for a particular conclusion or failing to support an innately obvious conclusion. This is a reflection of issues related to dealing with sample sizes, the methodologies involved, and the difficulty of communicating uncertainties. In this brief review, we attempt to illustrate ways to minimize such problems. In doing so, we consider one of the most common applications of environmental microbial forensics-the use of genotyping in food and water and disease investigations. We explore three important questions. (i) Do hypothesis tests' P values serve as adequate metrics of evidence? (ii) How can we quantify the value of the evidence? (iii) Can we turn a value-of-evidence metric into attribution probabilities? Our general conclusions are as follows. (i) P values have the unfortunate property of regularly detecting trivial effects when sample sizes are large. (ii) Likelihood ratios, rather than any kind of probability, are the better strength-of-evidence metric, addressing the question "what do these data say?" (iii) Attribution probabilities, addressing the question "what should I believe?," can be calculated using Bayesian methods, relying in part on likelihood ratios but also invoking prior beliefs which therefore can be quite subjective. In legal settings a Bayesian analysis may be required, but the choice and sensitivity of prior assumptions should be made clear.
An object-oriented Bayesian network modeling the causes of leg disorders in finisher herds
DEFF Research Database (Denmark)
Jensen, Tina Birk; Kristensen, Anders Ringgaard; Toft, Nils
2009-01-01
The implementation of an effective control strategy against disease in a finisher herd requires knowledge regarding the disease level in the herd. A Bayesian network was constructed that can estimate risk indexes for three cause-categories of leg disorders in a finisher herd. The cause...... pigs (e.g. results from diagnostic tests) were used to estimate the most likely cause of leg disorders at herd level. As information to the model originated from two different levels, we used an object-oriented structure in order to ease the specification of the Bayesian network. Hence, a Herd class...
Heard, Nicholas A.; Holmes, Christopher C.; Stephens, David A.; Hand, David J.; Dimopoulos, George
2005-01-01
We present a method for Bayesian model-based hierarchical coclustering of gene expression data and use it to study the temporal transcription responses of an Anopheles gambiae cell line upon challenge with multiple microbial elicitors. The method fits statistical regression models to the gene expression time series for each experiment and performs coclustering on the genes by optimizing a joint probability model, characterizing gene coregulation between multiple experiments. We compute the mo...
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.
GPstuff: Bayesian Modeling with Gaussian Processes
Vanhatalo, J.; Riihimaki, J.; Hartikainen, J.; Jylänki, P.P.; Tolvanen, V.; Vehtari, A.
2013-01-01
The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.
Bayesian Uncertainty Analyses Via Deterministic Model
Krzysztofowicz, R.
2001-05-01
Rational decision-making requires that the total uncertainty about a variate of interest (a predictand) be quantified in terms of a probability distribution, conditional on all available information and knowledge. Suppose the state-of-knowledge is embodied in a deterministic model, which is imperfect and outputs only an estimate of the predictand. Fundamentals are presented of three Bayesian approaches to producing a probability distribution of the predictand via any deterministic model. The Bayesian Processor of Output (BPO) quantifies the total uncertainty in terms of a posterior distribution, conditional on model output. The Bayesian Processor of Ensemble (BPE) quantifies the total uncertainty in terms of a posterior distribution, conditional on an ensemble of model output. The Bayesian Forecasting System (BFS) decomposes the total uncertainty into input uncertainty and model uncertainty, which are characterized independently and then integrated into a predictive distribution.
Picturing classical and quantum Bayesian inference
Coecke, Bob
2011-01-01
We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum Bayesian inference wherein one considers density operators rather than probability distributions as representative of degrees of belief. The diagrammatic framework is stated in the graphical language of symmetric monoidal categories and of compact structures and Frobenius structures therein, in which Bayesian inversion boils down to transposition with respect to an appropriate compact structure. We characterize classical Bayesian inference in terms of a graphical property and demonstrate that our approach eliminates some purely conventional elements that appear in common representations thereof, such as whether degrees of belief are represented by probabilities or entropic quantities. We also introduce a quantum-like calculus wherein the Frobenius structure is noncommutative and show that it can accommodate Leifer's calculus of `cond...
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.
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++.
ProFit: Bayesian galaxy fitting tool
Robotham, A. S. G.; Taranu, D.; Tobar, R.
2016-12-01
ProFit is a Bayesian galaxy fitting tool that uses the fast C++ image generation library libprofit (ascl:1612.003) and a flexible R interface to a large number of likelihood samplers. It offers a fully featured Bayesian interface to galaxy model fitting (also called profiling), using mostly the same standard inputs as other popular codes (e.g. GALFIT ascl:1104.010), but it is also able to use complex priors and a number of likelihoods.
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.
Variational Bayesian Approximation methods for inverse problems
Mohammad-Djafari, Ali
2012-09-01
Variational Bayesian Approximation (VBA) methods are recent tools for effective Bayesian computations. In this paper, these tools are used for inverse problems where the prior models include hidden variables and where where the estimation of the hyper parameters has also to be addressed. In particular two specific prior models (Student-t and mixture of Gaussian models) are considered and details of the algorithms are given.
Bayesian Modeling of a Human MMORPG Player
Synnaeve, Gabriel
2010-01-01
This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes are present. We explain the model in Bayesian programming and show how we could learn the conditional probabilities from data gathered during human-played sessions.
Bayesian Modeling of a Human MMORPG Player
Synnaeve, Gabriel; Bessière, Pierre
2011-03-01
This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes are present. We explain the model in Bayesian programming and show how we could learn the conditional probabilities from data gathered during human-played sessions.
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.
Philosophy and the practice of Bayesian statistics.
Gelman, Andrew; Shalizi, Cosma Rohilla
2013-02-01
A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework.
Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis
Narasimhan, Sriram; Mengshoel, Ole
2008-01-01
Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.
Dynamic safety assessment of natural gas stations using Bayesian network.
Zarei, Esmaeil; Azadeh, Ali; Khakzad, Nima; Aliabadi, Mostafa Mirzaei; Mohammadfam, Iraj
2017-01-05
Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks.
Analysing uncertainties: Towards comparing Bayesian and interval probabilities'
Blockley, David
2013-05-01
Two assumptions, commonly made in risk and reliability studies, have a long history. The first is that uncertainty is either aleatoric or epistemic. The second is that standard probability theory is sufficient to express uncertainty. The purposes of this paper are to provide a conceptual analysis of uncertainty and to compare Bayesian approaches with interval approaches with an example relevant to research on climate change. The analysis reveals that the categorisation of uncertainty as either aleatoric or epistemic is unsatisfactory for practical decision making. It is argued that uncertainty emerges from three conceptually distinctive and orthogonal attributes FIR i.e., fuzziness, incompleteness (epistemic) and randomness (aleatory). Characterisations of uncertainty, such as ambiguity, dubiety and conflict, are complex mixes of interactions in an FIR space. To manage future risks in complex systems it will be important to recognise the extent to which we 'don't know' about possible unintended and unwanted consequences or unknown-unknowns. In this way we may be more alert to unexpected hazards. The Bayesian approach is compared with an interval probability approach to show one way in which conflict due to incomplete information can be managed.
Clearing Unexploded Ordnance: Bayesian Methodology for Assessing Success
Energy Technology Data Exchange (ETDEWEB)
Anderson, K K.
2005-10-30
The Department of Defense has many Formerly Used Defense Sites (FUDS) that are slated for transfer for public use. Some sites have unexploded ordnance (UXO) that must be cleared prior to any land transfers. Sites are characterized using geophysical sensing devices and locations are identified where possible UXO may be located. In practice, based on the analysis of the geophysical surveys, a dig list of N suspect locations is created for a site that is possibly contaminated with UXO. The suspect locations on the dig list are often assigned into K bins ranging from ``most likely to contain UXO" to ``least likely to be UXO" based on signal discrimination techniques and expert judgment. Usually all dig list locations are sampled to determine if UXO is present before the site is determined to be free of UXO. While this method is 100% certain to insure no UXO remains in the locations identified by the signal discrimination and expert judgment, it is very costly. This paper proposes a statistical Bayesian methodology that may result in digging less than 100% of the suspect locations to reach a pre-defined tolerable risk, where risk is defined in terms of a low probability that any UXO remains in the unsampled dig list locations. Two important features of a Bayesian approach are that it can account for uncertainties in model parameters and that it can handle data that becomes available in stages. The results from each stage of data can be used to direct the subsequent digs.
Bayesian Approach for Reliability Assessment of Sunshield Deployment on JWST
Kaminskiy, Mark P.; Evans, John W.; Gallo, Luis D.
2013-01-01
Deployable subsystems are essential to mission success of most spacecraft. These subsystems enable critical functions including power, communications and thermal control. The loss of any of these functions will generally result in loss of the mission. These subsystems and their components often consist of unique designs and applications, for which various standardized data sources are not applicable for estimating reliability and for assessing risks. In this study, a Bayesian approach for reliability estimation of spacecraft deployment was developed for this purpose. This approach was then applied to the James Webb Space Telescope (JWST) Sunshield subsystem, a unique design intended for thermal control of the observatory's telescope and science instruments. In order to collect the prior information on deployable systems, detailed studies of "heritage information", were conducted extending over 45 years of spacecraft launches. The NASA Goddard Space Flight Center (GSFC) Spacecraft Operational Anomaly and Reporting System (SOARS) data were then used to estimate the parameters of the conjugative beta prior distribution for anomaly and failure occurrence, as the most consistent set of available data and that could be matched to launch histories. This allows for an emperical Bayesian prediction for the risk of an anomaly occurrence of the complex Sunshield deployment, with credibility limits, using prior deployment data and test information.
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.
Bayesian inference for OPC modeling
Burbine, Andrew; Sturtevant, John; Fryer, David; Smith, Bruce W.
2016-03-01
The use of optical proximity correction (OPC) demands increasingly accurate models of the photolithographic process. Model building and inference techniques in the data science community have seen great strides in the past two decades which make better use of available information. This paper aims to demonstrate the predictive power of Bayesian inference as a method for parameter selection in lithographic models by quantifying the uncertainty associated with model inputs and wafer data. Specifically, the method combines the model builder's prior information about each modelling assumption with the maximization of each observation's likelihood as a Student's t-distributed random variable. Through the use of a Markov chain Monte Carlo (MCMC) algorithm, a model's parameter space is explored to find the most credible parameter values. During parameter exploration, the parameters' posterior distributions are generated by applying Bayes' rule, using a likelihood function and the a priori knowledge supplied. The MCMC algorithm used, an affine invariant ensemble sampler (AIES), is implemented by initializing many walkers which semiindependently explore the space. The convergence of these walkers to global maxima of the likelihood volume determine the parameter values' highest density intervals (HDI) to reveal champion models. We show that this method of parameter selection provides insights into the data that traditional methods do not and outline continued experiments to vet the method.
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 ...
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.
Enhancing debris flow modeling parameters integrating Bayesian networks
Graf, C.; Stoffel, M.; Grêt-Regamey, A.
2009-04-01
Applied debris-flow modeling requires suitably constraint input parameter sets. Depending on the used model, there is a series of parameters to define before running the model. Normally, the data base describing the event, the initiation conditions, the flow behavior, the deposition process and mainly the potential range of possible debris flow events in a certain torrent is limited. There are only some scarce places in the world, where we fortunately can find valuable data sets describing event history of debris flow channels delivering information on spatial and temporal distribution of former flow paths and deposition zones. Tree-ring records in combination with detailed geomorphic mapping for instance provide such data sets over a long time span. Considering the significant loss potential associated with debris-flow disasters, it is crucial that decisions made in regard to hazard mitigation are based on a consistent assessment of the risks. This in turn necessitates a proper assessment of the uncertainties involved in the modeling of the debris-flow frequencies and intensities, the possible run out extent, as well as the estimations of the damage potential. In this study, we link a Bayesian network to a Geographic Information System in order to assess debris-flow risk. We identify the major sources of uncertainty and show the potential of Bayesian inference techniques to improve the debris-flow model. We model the flow paths and deposition zones of a highly active debris-flow channel in the Swiss Alps using the numerical 2-D model RAMMS. Because uncertainties in run-out areas cause large changes in risk estimations, we use the data of flow path and deposition zone information of reconstructed debris-flow events derived from dendrogeomorphological analysis covering more than 400 years to update the input parameters of the RAMMS model. The probabilistic model, which consistently incorporates this available information, can serve as a basis for spatial risk
Origin of microbial biomineralization and magnetotaxis during the Archean
Paterson, Greig A.; Wang, Yinzhao; Kopylova, Evguenia; Li, Ying; Knight, Rob; Bazylinski, Dennis A.; Zhu, Rixiang; Kirschvink, Joseph L.; Pan, Yongxin
2017-01-01
Microbes that synthesize minerals, a process known as microbial biomineralization, contributed substantially to the evolution of current planetary environments through numerous important geochemical processes. Despite its geological significance, the origin and evolution of microbial biomineralization remain poorly understood. Through combined metagenomic and phylogenetic analyses of deep-branching magnetotactic bacteria from the Nitrospirae phylum, and using a Bayesian molecular clock-dating method, we show here that the gene cluster responsible for biomineralization of magnetosomes, and the arrangement of magnetosome chain(s) within cells, both originated before or near the Archean divergence between the Nitrospirae and Proteobacteria. This phylogenetic divergence occurred well before the Great Oxygenation Event. Magnetotaxis likely evolved due to environmental pressures conferring an evolutionary advantage to navigation via the geomagnetic field. Earth’s dynamo must therefore have been sufficiently strong to sustain microbial magnetotaxis in the Archean, suggesting that magnetotaxis coevolved with the geodynamo over geological time. PMID:28193877
Origin of microbial biomineralization and magnetotaxis during the Archean.
Lin, Wei; Paterson, Greig A; Zhu, Qiyun; Wang, Yinzhao; Kopylova, Evguenia; Li, Ying; Knight, Rob; Bazylinski, Dennis A; Zhu, Rixiang; Kirschvink, Joseph L; Pan, Yongxin
2017-02-28
Microbes that synthesize minerals, a process known as microbial biomineralization, contributed substantially to the evolution of current planetary environments through numerous important geochemical processes. Despite its geological significance, the origin and evolution of microbial biomineralization remain poorly understood. Through combined metagenomic and phylogenetic analyses of deep-branching magnetotactic bacteria from the Nitrospirae phylum, and using a Bayesian molecular clock-dating method, we show here that the gene cluster responsible for biomineralization of magnetosomes, and the arrangement of magnetosome chain(s) within cells, both originated before or near the Archean divergence between the Nitrospirae and Proteobacteria This phylogenetic divergence occurred well before the Great Oxygenation Event. Magnetotaxis likely evolved due to environmental pressures conferring an evolutionary advantage to navigation via the geomagnetic field. Earth's dynamo must therefore have been sufficiently strong to sustain microbial magnetotaxis in the Archean, suggesting that magnetotaxis coevolved with the geodynamo over geological time.
Bayesian Calibration of Microsimulation Models.
Rutter, Carolyn M; Miglioretti, Diana L; Savarino, James E
2009-12-01
Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are characterized by simulation of individual event histories for an idealized population of interest. Microsimulation models are complex and invariably include parameters that are not well informed by existing data. Therefore, a key component of model development is the choice of parameter values. Microsimulation model parameter values are selected to reproduce expected or known results though the process of model calibration. Calibration may be done by perturbing model parameters one at a time or by using a search algorithm. As an alternative, we propose a Bayesian method to calibrate microsimulation models that uses Markov chain Monte Carlo. We show that this approach converges to the target distribution and use a simulation study to demonstrate its finite-sample performance. Although computationally intensive, this approach has several advantages over previously proposed methods, including the use of statistical criteria to select parameter values, simultaneous calibration of multiple parameters to multiple data sources, incorporation of information via prior distributions, description of parameter identifiability, and the ability to obtain interval estimates of model parameters. We develop a microsimulation model for colorectal cancer and use our proposed method to calibrate model parameters. The microsimulation model provides a good fit to the calibration data. We find evidence that some parameters are identified primarily through prior distributions. Our results underscore the need to incorporate multiple sources of variability (i.e., due to calibration data, unknown parameters, and estimated parameters and predicted values) when calibrating and applying microsimulation models.
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.
Bayesian modeling of flexible cognitive control.
Jiang, Jiefeng; Heller, Katherine; Egner, Tobias
2014-10-01
"Cognitive control" describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation.
Multi-Fraction Bayesian Sediment Transport Model
Directory of Open Access Journals (Sweden)
Mark L. Schmelter
2015-09-01
Full Text Available A Bayesian approach to sediment transport modeling can provide a strong basis for evaluating and propagating model uncertainty, which can be useful in transport applications. Previous work in developing and applying Bayesian sediment transport models used a single grain size fraction or characterized the transport of mixed-size sediment with a single characteristic grain size. Although this approach is common in sediment transport modeling, it precludes the possibility of capturing processes that cause mixed-size sediments to sort and, thereby, alter the grain size available for transport and the transport rates themselves. This paper extends development of a Bayesian transport model from one to k fractional dimensions. The model uses an existing transport function as its deterministic core and is applied to the dataset used to originally develop the function. The Bayesian multi-fraction model is able to infer the posterior distributions for essential model parameters and replicates predictive distributions of both bulk and fractional transport. Further, the inferred posterior distributions are used to evaluate parametric and other sources of variability in relations representing mixed-size interactions in the original model. Successful OPEN ACCESS J. Mar. Sci. Eng. 2015, 3 1067 development of the model demonstrates that Bayesian methods can be used to provide a robust and rigorous basis for quantifying uncertainty in mixed-size sediment transport. Such a method has heretofore been unavailable and allows for the propagation of uncertainty in sediment transport applications.
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.
Bayesian modeling of flexible cognitive control
Jiang, Jiefeng; Heller, Katherine; Egner, Tobias
2014-01-01
“Cognitive control” describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation. PMID:24929218
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.
On Bayesian shared component disease mapping and ecological regression with errors in covariates.
MacNab, Ying C
2010-05-20
Recent literature on Bayesian disease mapping presents shared component models (SCMs) for joint spatial modeling of two or more diseases with common risk factors. In this study, Bayesian hierarchical formulations of shared component disease mapping and ecological models are explored and developed in the context of ecological regression, taking into consideration errors in covariates. A review of multivariate disease mapping models (MultiVMs) such as the multivariate conditional autoregressive models that are also part of the more recent Bayesian disease mapping literature is presented. Some insights into the connections and distinctions between the SCM and MultiVM procedures are communicated. Important issues surrounding (appropriate) formulation of shared- and disease-specific components, consideration/choice of spatial or non-spatial random effects priors, and identification of model parameters in SCMs are explored and discussed in the context of spatial and ecological analysis of small area multivariate disease or health outcome rates and associated ecological risk factors. The methods are illustrated through an in-depth analysis of four-variate road traffic accident injury (RTAI) data: gender-specific fatal and non-fatal RTAI rates in 84 local health areas in British Columbia (Canada). Fully Bayesian inference via Markov chain Monte Carlo simulations is presented.
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
Microbial conversions of terpenoids
Parshikov, Igor A
2015-01-01
The monograph describes examples of the application of microbial technology for obtaining of derivatives of terpenoids. Obtaining new derivatives of terpenoids, including artemisinin derivatives with increased antimalarial activity, is an important goal of research in microbial biotechnology and medicinal chemistry.
Bayesian Inference in Polling Technique: 1992 Presidential Polls.
Satake, Eiki
1994-01-01
Explores the potential utility of Bayesian statistical methods in determining the predictability of multiple polls. Compares Bayesian techniques to the classical statistical method employed by pollsters. Considers these questions in the context of the 1992 presidential elections. (HB)
Bayesian modeling of unknown diseases for biosurveillance.
Shen, Yanna; Cooper, Gregory F
2009-11-14
This paper investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection. We introduce a Bayesian approach that models and detects both (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A key contribution of this paper is that it introduces a Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has broad applicability in medical informatics, where the space of known causes of outcomes of interest is seldom complete.
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...
Event generator tuning using Bayesian optimization
Ilten, Philip; Yang, Yunjie
2016-01-01
Monte Carlo event generators contain a large number of parameters that must be determined by comparing the output of the generator with experimental data. Generating enough events with a fixed set of parameter values to enable making such a comparison is extremely CPU intensive, which prohibits performing a simple brute-force grid-based tuning of the parameters. Bayesian optimization is a powerful method designed for such black-box tuning applications. In this article, we show that Monte Carlo event generator parameters can be accurately obtained using Bayesian optimization and minimal expert-level physics knowledge. A tune of the PYTHIA 8 event generator using $e^+e^-$ events, where 20 parameters are optimized, can be run on a modern laptop in just two days. Combining the Bayesian optimization approach with expert knowledge should enable producing better tunes in the future, by making it faster and easier to study discrepancies between Monte Carlo and experimental data.
Bayesian Inference Methods for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand
2013-01-01
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...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...
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...
Distributed Bayesian Networks for User Modeling
DEFF Research Database (Denmark)
Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang
2006-01-01
by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...... efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms....
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.
Variational Bayesian Inference of Line Spectra
DEFF Research Database (Denmark)
Badiu, Mihai Alin; Hansen, Thomas Lundgaard; Fleury, Bernard Henri
2016-01-01
In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coeffici......In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid......; and the coefficients are governed by a Bernoulli-Gaussian prior model turning model order selection into binary sequence detection. Unlike earlier works which retain only point estimates of the frequencies, we undertake a more complete Bayesian treatment by estimating the posterior probability density functions (pdfs...
Hessian PDF reweighting meets the Bayesian methods
Paukkunen, Hannu
2014-01-01
We discuss the Hessian PDF reweighting - a technique intended to estimate the effects that new measurements have on a set of PDFs. The method stems straightforwardly from considering new data in a usual $\\chi^2$-fit and it naturally incorporates also non-zero values for the tolerance, $\\Delta\\chi^2>1$. In comparison to the contemporary Bayesian reweighting techniques, there is no need to generate large ensembles of PDF Monte-Carlo replicas, and the observables need to be evaluated only with the central and the error sets of the original PDFs. In spite of the apparently rather different methodologies, we find that the Hessian and the Bayesian techniques are actually equivalent if the $\\Delta\\chi^2$ criterion is properly included to the Bayesian likelihood function that is a simple exponential.
Bayesian analysis of MEG visual evoked responses
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Schmidt, D.M.; George, J.S.; Wood, C.C.
1999-04-01
The authors developed a method for analyzing neural electromagnetic data that allows probabilistic inferences to be drawn about regions of activation. The method involves the generation of a large number of possible solutions which both fir the data and prior expectations about the nature of probable solutions made explicit by a Bayesian formalism. In addition, they have introduced a model for the current distributions that produce MEG and (EEG) data that allows extended regions of activity, and can easily incorporate prior information such as anatomical constraints from MRI. To evaluate the feasibility and utility of the Bayesian approach with actual data, they analyzed MEG data from a visual evoked response experiment. They compared Bayesian analyses of MEG responses to visual stimuli in the left and right visual fields, in order to examine the sensitivity of the method to detect known features of human visual cortex organization. They also examined the changing pattern of cortical activation as a function of time.
Bayesian Analysis of Perceived Eye Level
Orendorff, Elaine E.; Kalesinskas, Laurynas; Palumbo, Robert T.; Albert, Mark V.
2016-01-01
To accurately perceive the world, people must efficiently combine internal beliefs and external sensory cues. We introduce a Bayesian framework that explains the role of internal balance cues and visual stimuli on perceived eye level (PEL)—a self-reported measure of elevation angle. This framework provides a single, coherent model explaining a set of experimentally observed PEL over a range of experimental conditions. Further, it provides a parsimonious explanation for the additive effect of low fidelity cues as well as the averaging effect of high fidelity cues, as also found in other Bayesian cue combination psychophysical studies. Our model accurately estimates the PEL and explains the form of previous equations used in describing PEL behavior. Most importantly, the proposed Bayesian framework for PEL is more powerful than previous behavioral modeling; it permits behavioral estimation in a wider range of cue combination and perceptual studies than models previously reported. PMID:28018204
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-based Project Monitoring: Framework Development and Model Testing
Directory of Open Access Journals (Sweden)
Budi Hartono
2015-12-01
Full Text Available During project implementation, risk becomes an integral part of project monitoring. Therefore. a tool that could dynamically include elements of risk in project progress monitoring is needed. This objective of this study is to develop a general framework that addresses such a concern. The developed framework consists of three interrelated major building blocks, namely: Risk Register (RR, Bayesian Network (BN, and Project Time Networks (PTN for dynamic project monitoring. RR is used to list and to categorize identified project risks. PTN is utilized for modeling the relationship between project activities. BN is used to reflect the interdependence among risk factors and to bridge RR and PTN. A residential development project is chosen as a working example and the result shows that the proposed framework has been successfully applied. The specific model of the development project is also successfully developed and is used to monitor the project progress. It is shown in this study that the proposed BN-based model provides superior performance in terms of forecast accuracy compared to the extant models.
Mining data from hemodynamic simulations via Bayesian emulation
Directory of Open Access Journals (Sweden)
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.
A Bayesian Framework for Reliability Analysis of Spacecraft Deployments
Evans, John W.; Gallo, Luis; Kaminsky, Mark
2012-01-01
Deployable subsystems are essential to mission success of most spacecraft. These subsystems enable critical functions including power, communications and thermal control. The loss of any of these functions will generally result in loss of the mission. These subsystems and their components often consist of unique designs and applications for which various standardized data sources are not applicable for estimating reliability and for assessing risks. In this study, a two stage sequential Bayesian framework for reliability estimation of spacecraft deployment was developed for this purpose. This process was then applied to the James Webb Space Telescope (JWST) Sunshield subsystem, a unique design intended for thermal control of the Optical Telescope Element. Initially, detailed studies of NASA deployment history, "heritage information", were conducted, extending over 45 years of spacecraft launches. This information was then coupled to a non-informative prior and a binomial likelihood function to create a posterior distribution for deployments of various subsystems uSing Monte Carlo Markov Chain sampling. Select distributions were then coupled to a subsequent analysis, using test data and anomaly occurrences on successive ground test deployments of scale model test articles of JWST hardware, to update the NASA heritage data. This allowed for a realistic prediction for the reliability of the complex Sunshield deployment, with credibility limits, within this two stage Bayesian framework.
Structure learning for Bayesian networks as models of biological networks.
Larjo, Antti; Shmulevich, Ilya; Lähdesmäki, Harri
2013-01-01
Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.
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.
Microfluidics and microbial engineering.
Kou, Songzi; Cheng, Danhui; Sun, Fei; Hsing, I-Ming
2016-02-01
The combination of microbial engineering and microfluidics is synergistic in nature. For example, microfluidics is benefiting from the outcome of microbial engineering and many reported point-of-care microfluidic devices employ engineered microbes as functional parts for the microsystems. In addition, microbial engineering is facilitated by various microfluidic techniques, due to their inherent strength in high-throughput screening and miniaturization. In this review article, we firstly examine the applications of engineered microbes for toxicity detection, biosensing, and motion generation in microfluidic platforms. Secondly, we look into how microfluidic technologies facilitate the upstream and downstream processes of microbial engineering, including DNA recombination, transformation, target microbe selection, mutant characterization, and microbial function analysis. Thirdly, we highlight an emerging concept in microbial engineering, namely, microbial consortium engineering, where the behavior of a multicultural microbial community rather than that of a single cell/species is delineated. Integrating the disciplines of microfluidics and microbial engineering opens up many new opportunities, for example in diagnostics, engineering of microbial motors, development of portable devices for genetics, high throughput characterization of genetic mutants, isolation and identification of rare/unculturable microbial species, single-cell analysis with high spatio-temporal resolution, and exploration of natural microbial communities.
A Bayesian Analysis of Spectral ARMA Model
Directory of Open Access Journals (Sweden)
Manoel I. Silvestre Bezerra
2012-01-01
Full Text Available Bezerra et al. (2008 proposed a new method, based on Yule-Walker equations, to estimate the ARMA spectral model. In this paper, a Bayesian approach is developed for this model by using the noninformative prior proposed by Jeffreys (1967. The Bayesian computations, simulation via Markov Monte Carlo (MCMC is carried out and characteristics of marginal posterior distributions such as Bayes estimator and confidence interval for the parameters of the ARMA model are derived. Both methods are also compared with the traditional least squares and maximum likelihood approaches and a numerical illustration with two examples of the ARMA model is presented to evaluate the performance of the procedures.
Length Scales in Bayesian Automatic Adaptive Quadrature
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Adam Gh.
2016-01-01
Full Text Available Two conceptual developments in the Bayesian automatic adaptive quadrature approach to the numerical solution of one-dimensional Riemann integrals [Gh. Adam, S. Adam, Springer LNCS 7125, 1–16 (2012] are reported. First, it is shown that the numerical quadrature which avoids the overcomputing and minimizes the hidden floating point loss of precision asks for the consideration of three classes of integration domain lengths endowed with specific quadrature sums: microscopic (trapezoidal rule, mesoscopic (Simpson rule, and macroscopic (quadrature sums of high algebraic degrees of precision. Second, sensitive diagnostic tools for the Bayesian inference on macroscopic ranges, coming from the use of Clenshaw-Curtis quadrature, are derived.
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
Bayesian long branch attraction bias and corrections.
Susko, Edward
2015-03-01
Previous work on the star-tree paradox has shown that Bayesian methods suffer from a long branch attraction bias. That work is extended to settings involving more taxa and partially resolved trees. The long branch attraction bias is confirmed to arise more broadly and an additional source of bias is found. A by-product of the analysis is methods that correct for biases toward particular topologies. The corrections can be easily calculated using existing Bayesian software. Posterior support for a set of two or more trees can thus be supplemented with corrected versions to cross-check or replace results. Simulations show the corrections to be highly effective.
From retrodiction to Bayesian quantum imaging
Speirits, Fiona C.; Sonnleitner, Matthias; Barnett, Stephen M.
2017-04-01
We employ quantum retrodiction to develop a robust Bayesian algorithm for reconstructing the intensity values of an image from sparse photocount data, while also accounting for detector noise in the form of dark counts. This method yields not only a reconstructed image but also provides the full probability distribution function for the intensity at each pixel. We use simulated as well as real data to illustrate both the applications of the algorithm and the analysis options that are only available when the full probability distribution functions are known. These include calculating Bayesian credible regions for each pixel intensity, allowing an objective assessment of the reliability of the reconstructed image intensity values.
A Bayesian Concept Learning Approach to Crowdsourcing
DEFF Research Database (Denmark)
Viappiani, Paolo Renato; Zilles, Sandra; Hamilton, Howard J.;
2011-01-01
techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing......We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation...... that our Bayesian strategies are effective even in large concept spaces with many uninformative experts....
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 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 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...
Prior approval: the growth of Bayesian methods in psychology.
Andrews, Mark; Baguley, Thom
2013-02-01
Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.
A default Bayesian hypothesis test for ANOVA designs
Wetzels, R.; Grasman, R.P.P.P.; Wagenmakers, E.J.
2012-01-01
This article presents a Bayesian hypothesis test for analysis of variance (ANOVA) designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA desig
From arguments to constraints on a Bayesian network
Bex, F.J.; Renooij, S.
2016-01-01
In this paper, we propose a way to derive constraints for a Bayesian Network from structured arguments. Argumentation and Bayesian networks can both be considered decision support techniques, but are typically used by experts with different backgrounds. Bayesian network experts have the mathematical
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…
Guillier, Laurent; Danan, Corinne; Bergis, Hélène; Delignette-Muller, Marie-Laure; Granier, Sophie; Rudelle, Sylvie; Beaufort, Annie; Brisabois, Anne
2013-09-16
A major community outbreak of salmonellosis occurred in France in October 2010. Classical epidemiological investigations led to the identification of beef burgers as the cause of the outbreak and the presence of the emerging monophasic Salmonella Typhimurium 4,5,12:i:-. The objective of this study was to understand the events that led to this large outbreak, that is to say, what are the contributing factors associated with consumer exposure to Salmonella. To this end, intensive microbiological investigations on several beef burgers were conducted and a risk assessment model was built. The microbiological results confirm the presence of Salmonella in all analysed frozen burgers at high levels of contamination above 1000 MPN/g. These results in frozen burgers combined with a model of thermal destruction were used to estimate the dose ingested by the exposed persons. Most people that consumed cooked beef burgers were exposed from 1.6 to 3.1 log₁₀ (MPN). The number of sick people predicted with a dose-response relationship for Salmonella is consistent with the observed number of salmonellosis cases. The very high initial contamination level in frozen beef burgers is the primary cause of this large outbreak rather than bad cooking practices. Intensive investigations, modelling of the initial contamination and quantitative exposure and risk assessments are complementary to epidemiological investigation. They can be valuable elements for the assessment of missing information or the identification of the primary causes of outbreaks.
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
Bayesian Vector Autoregressions with Stochastic Volatility
Uhlig, H.F.H.V.S.
1996-01-01
This paper proposes a Bayesian approach to a vector autoregression with stochastic volatility, where the multiplicative evolution of the precision matrix is driven by a multivariate beta variate.Exact updating formulas are given to the nonlinear filtering of the precision matrix.Estimation of the au
Bayesian Estimation Supersedes the "t" Test
Kruschke, John K.
2013-01-01
Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional "t" tests) when certainty in the estimate is…
Bayesian Meta-Analysis of Coefficient Alpha
Brannick, Michael T.; Zhang, Nanhua
2013-01-01
The current paper describes and illustrates a Bayesian approach to the meta-analysis of coefficient alpha. Alpha is the most commonly used estimate of the reliability or consistency (freedom from measurement error) for educational and psychological measures. The conventional approach to meta-analysis uses inverse variance weights to combine…
Comprehension and computation in Bayesian problem solving
Directory of Open Access Journals (Sweden)
Eric D. Johnson
2015-07-01
Full Text Available Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian reasoning relative to normalized formats (e.g. probabilities, percentages, both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on transparent Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e. transparent problem structures at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct versus incorrect reasoners depart, and how individual difference might influence this time point.
Bayesian Networks: Aspects of Approximate Inference
Bolt, J.H.
2008-01-01
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a given problem domain. Such a network consists of an acyclic directed graph in which the nodes represent stochastic variables, supplemented with probabilities indicating the strength of the influences betw
Bayesian Benefits for the Pragmatic Researcher
Wagenmakers, E.-J.; Morey, R.D.; Lee, M.D.
2016-01-01
The practical advantages of Bayesian inference are demonstrated here through two concrete examples. In the first example, we wish to learn about a criminal’s IQ: a problem of parameter estimation. In the second example, we wish to quantify and track support in favor of the null hypothesis that Adam
Communication cost in Distributed Bayesian Belief Networks
Gosliga, S.P. van; Maris, M.G.
2005-01-01
In this paper, two different methods for information fusionare compared with respect to communication cost. These are the lambda-pi and the junction tree approach as the probability computing methods in Bayesian networks. The analysis is done within the scope of large distributed networks of computi