Risk Assessment and Decision Analysis with Bayesian Networks

by Norman Fenton, Martin Neil

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. 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.

Genres: Mathematics, Reference

524 pages, Hardcover

First published August 3, 2012

Reviews

[Luis Detlefsen]

I started reading it because it explains very well the Monty Hall problem.

It explains very well the Bayesian networks, all the theory and a bunch of detailed examples. This book actually made me like Bayesian networks and really understand the applications of it.

[Joe Hightower]

Very informative, but too much like a manual for the author's software. Aside from that it is a very useful description of how to construct complex Bayesian Networks