Risk Assessment and Decision Analysis with Bayesian Networks

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.

Preface

Chapter 1 : There Is More to Assessing Risk Than Statistics
Chapter 2 : The Need for Causal, Explanatory Models in Risk Assessment
Chapter 3 : Measuring Uncertainty : The Inevitability of Subjectivity
Chapter 4 : The Basics of Probability
Chapter 5 : Bayes’ Theorem and Conditional Probability
Chapter 6 : From Bayes’ Theorem to Bayesian Networks
Chapter 7 : Defining the Structure of Bayesian Networks
Chapter 8 : Building and Eliciting Node Probability Tables
Chapter 9 : Numeric Variables and Continuous Distribution Functions
Chapter 10 : Hypothesis Testing and Confidence Intervals
Chapter 11 : Modeling Operational Risk
Chapter 12 : Systems Reliability Modeling
Chapter 13 : Bayes and the Law

Appendix A : The Basics of Counting
Appendix B : The Algebra of Node Probability Tables
Appendix C : Junction Tree Algorithm
Appendix D : Dynamic Discretization
Appendix E : Statistical Distributions
Index