Use Of Bayesian Network Models Research Articles

Increased Use of Bayesian Network Models Has Improved Environmental Risk Assessments.

Environmental and ecological risk assessments are defined as the process for evaluating the likelihood that the environment may be impacted as a result of exposure to stressors. Although this definition implies the calculation of probabilities, risk assessments traditionally rely on nonprobabilistic methods such as calculation of a risk quotient. Bayesian network (BN) models are a tool for probabilistic and causal modeling, increasingly used in many fields of environmental science. Bayesian networks are defined as directed acyclic graphs where the causal relationships and the associated uncertainty are quantified in conditional probability tables. Bayesian networks inherently incorporate uncertainty and can integrate a variety of information types, including expert elicitation. During the last 2 decades, there has been a steady increase in reports on BN applications in environmental risk assessment and management. At recent annual meetings of the Society of Environmental Toxicology and Chemistry (SETAC) North America and SETAC Europe, a number of applications of BN models were presented along with new theoretical developments. Likewise, recent meetings of the European Geosciences Union (EGU) have dedicated sessions to Bayesian modeling in relation to water quality. This special series contains 10 articles based on presentations in these sessions, reflecting a range of BN applications to systems, ranging from cells and populations to watersheds and national scale. The articles report on recent progress in many topics, including climate and management scenarios, ecological and socioeconomic endpoints, machine learning, diagnostic inference, and model evaluation. They demonstrate that BNs can be adapted to established conceptual frameworks used to support environmental risk assessments, such as adverse outcome pathways and the relative risk model. The contributions from EGU demonstrate recent advancements in areas such as spatial (geographic information system [GIS]–based) and temporal (dynamic) BN modeling. In conclusion, this special series supports the prediction that increased use of Bayesian network models will improve environmental risk assessments.

The use of Bayesian network models to identify factors related to frailty phenotype and health outcomes in middle-aged and older persons

BackgroundStudies on frailty frequently only include older persons. The mapping of factors related to this syndrome and negative outcomes associated with it, also in middle age, may assist in health strategies to each age group. ObjectivesTo investigate social and health factors related to the frailty phenotype and to analyze the probabilistic relationships between frailty, falls and hospitalization in middle-aged and older persons. MethodsThis is a cross-sectional study using data for 4442 middle-aged (50–59 years) and older participants (60 years or older) from the Brazilian Longitudinal Study of Aging (ELSI-Brazil) 2015 and 2016. Bayesian network models were estimated with the score-based hill-climbing algorithm to identify factors associated with frailty, falls and hospitalization. ResultsMean age was 63.7 years, and prevalence of frailty was 8.5 % and 11.9 % among middle-aged and older participants, respectively. In the former, the probability of frailty increased when “poor” self-rated memory was considered in the model; and in the latter, the probability of frailty was greatest among individuals who did not participate socially and had the lowest level of education. In both age groups, frailty was an important factor that influenced the probability of negative health outcomes such as falls and hospitalization. However, this result depended on combinations of health factors in each sample. ConclusionsThis study has identified potential vulnerabilities that should be considered when undertaking a comprehensive assessment of middle-aged and older persons and developing suitable health strategies for each of these phases of life.

A Bayesian network model for prediction and analysis of possible forest fire causes

Possible causes of a forest fire ignition could be human-caused (arson, smoking, hunting, picnic fire, shepherd fire, stubble burning) or natural-caused (lightning strikes, power lines). Temperature, relative humidity, tree species, distance from road, wind speed, distance from agricultural land, amount of burnt area, month and distance from settlement are the risk factors that may affect the occurrence of forest fires. This study introduces the use of Bayesian network model to predict the possible forest fire causes, as well as to perform an analysis of the multilateral interactive relations among them. The study was conducted in Mugla Regional Directorate of Forestry area located in the southwest of Turkey. The fire data, which were recorded between 2008 and 2018 in the area, were provided by General Directorate of Forestry. In this study, after applying some different structural learning algorithms, a Bayesian network, which is built on the nodes relative humidity, temperature, wind speed, month, distance from settlement, amount of burnt area, distance from agricultural land, distance from road and tree species, was estimated. The model showed that month is the first and temperature is the second most effective factor on the forest fire ignitions. The Bayesian network model approach adopted in this study could also be used with data obtained from different areas having different sizes.

Quantitive Assessment of Corrosion Probability—A Bayesian Network Approach

Corrosion processes mainly affect the probability of failure, which then leads to consequences, such as, fire, explosion, or environmental damage. This paper focuses on the use of Bayesian network models for assessing the probability of corrosion. The Bayesian network approach incorporates cause- effect relationships of complex systems in the form of conditional probabilities. This method considers both knowledge uncertainties (i.e., modeling uncertainties) and data uncertainties to make more informed decisions. The Bayes theorem allows the model to predict the probability of events from their causes, and, if a particular event is known to have occurred, predict probable causes of that event. Two case studies, the first one involving internal corrosion and the second involving external corrosion of oil and gas pipeline, are presented, along with validation using field measurements. The extension of the approach to predicting stress corrosion cracking of pipelines is discussed.

Research on Rural Service Network Establishment of Express Company based on Bayesian Network Model

Express industry development focuses on adapting to expanding domestic need. Because rural market potential is tremendous, developing rural market is full of temptation. How to develop rural market is the key to the development of economy and express industry in China. In this paper, in the light of the express delivery industry's professional characteristics the index system has established considering that some indexes will influence the network’ layout. Making use of Bayesian network model, the express company's selection scheme is determined in rural service network. After that, based on the analysis, the method of establishing a network of express is proposed. Finally an example is given to verify express company in rural network scheme, which proves the feasibility of the research method.

Modeling Diagnostic Assessments with Bayesian Networks

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 are reviewed, as they affect applications to diagnostic assessment. The paper discusses how Bayesian network models are set up with expert information, improved and calibrated from data, and deployed as evidence‐based inference engines. Aimed at a general educational measurement audience, the paper illustrates the flexibility and capabilities of Bayesian networks through a series of concrete examples, and without extensive technical detail. Examples are provided of proficiency spaces with direct dependencies among proficiency nodes, and of customized evidence models for complex tasks. This paper is intended to motivate educational measurement practitioners to learn more about Bayesian networks from the research literature, to acquire readily available Bayesian network software, to perform studies with real and simulated data sets, and to look for opportunities in educational settings that may benefit from diagnostic assessment fueled by Bayesian network modeling.