Risk Assessment and Decision Analysis with Bayesian Networks

By Norman E. Fenton, Martin David Neil. 2012

886 citations recorded by the source corpus.

Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide powerful insights and better decision making. Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, and more Introduces all necessary mathematics, probability, and statistics as needed The book first establishes the basics of probability, risk, and building and using BN models, then goes into the detailed applications. The underlying BN algorithms appear in appendices rather than the main text since there is no need to understand them to build and use BN models. Keeping the body of the text free of intimidating mathematics, the book provides pragmatic advice about model building to ensure models are built efficiently. A dedicated website, www.BayesianRisk.com, contains executable versions of all of the models described, exercises and worked solutions for all chapters, PowerPoint slides, numerous other resources, and a free downloadable copy of the AgenaRisk software.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7854c156af42f1e092fc74f1703e1715a9b21a32

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Citations in the corpus

Advances in Bayesian network modelling: Integration of modelling technologies (2019, methodology): “Bayesian networks (BNs) are directed acyclic graphs that link variables by conditional probabilities, where model outputs are probabilities calculated using Bayes' Theorem (Fenton and Neil, 2012; Koski and Noble, 2011).”
Solar flares, coronal mass ejections and solar energetic particle event characteristics (2016, methodology): “In addition, we calculated the p-values (p-value is the probability of obtaining a result as extreme as, or more extreme than, the result actually obtained when the null hypothesis is true) for each correlation coefficient (Fenton & Neil 2012) and all p-values are included in Table 4.”
An overview of methods to evaluate uncertainty of deterministic models in decision support (2015, positive): “) decisions has associated gains or losses which are usually dependent on several random factors and thus highly uncertain (Fenton and Neil, 2012).”
Bayesian network learning for natural hazard analyses (2014, methodology): “A detailed introduction to BNs is provided inKoller and Friedman(2009) andJensen and Nielsen(2001), whileFenton and Neil (2012) offers easy and intuitive access.”
Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review (2019, background): “Diﬀerent aspects of Bayesian network-based risk assessment and decision analysis are described by Fenton and Neil (2012, 2014).”