Psychology

Health, Fitness & Dieting

Neil, Martin

Psychology & Counseling

Allyn&Bacon

Risk Assessment and Decision Analysis with Bayesian Networks

Business & Money

Fenton, Norman

Management & Leadership

Chapman and Hall/CRC

Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions. Features <ul>
<li>Provides all tools necessary to build and run realistic Bayesian network models</li> <li>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, forensics, cybersecurity and more</li> <li>Introduces all necessary mathematics, probability, and statistics as needed</li> <li>Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications</li> </ul>
A dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. The AgenaRisk software contains a model library with executable versions of all of the models in the book. Lecture slides are freely available to accredited academic teachers adopting the book on their course.

Psychology: WITH " Introduction to Research Methods in Psychology " AND " Short Guide to Writing About Psychology "

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. <ul>
<li>Provides all tools necessary to build and run realistic Bayesian network models</li> <li>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</li> <li>Introduces all necessary mathematics, probability, and statistics as needed</li> </ul> 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.

CRC-Press

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