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.Another reason why people overestimate the probability of winning if they stick is that when Monty Hall reveals a door that ... We shall return yet again to this problem in Chapter 6 by showing a solution using a Bayesian network. ... to open door 3 Monty Hall must choose to open door 2 Case (c): Prize is behind door 3 Figure 4.9 All three cases once you have chosen a particular door (assumed to be 1).
|Title||:||Risk Assessment and Decision Analysis with Bayesian Networks|
|Author||:||Norman Fenton, Martin Neil|
|Publisher||:||CRC Press - 2013-06-19|