## Book description

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.

## Table of contents

- Front Cover (1/2)
- Front Cover (2/2)
- Contents (1/2)
- Contents (2/2)
- Foreword
- Preface
- Acknowledgments
- Authors
- Chapter 1 - There Is More to Assessing Risk Than Statistics (1/6)
- Chapter 1 - There Is More to Assessing Risk Than Statistics (2/6)
- Chapter 1 - There Is More to Assessing Risk Than Statistics (3/6)
- Chapter 1 - There Is More to Assessing Risk Than Statistics (4/6)
- Chapter 1 - There Is More to Assessing Risk Than Statistics (5/6)
- Chapter 1 - There Is More to Assessing Risk Than Statistics (6/6)
- Chapter 2 - The Need for Causal, Explanatory Models in Risk Assessment (1/4)
- Chapter 2 - The Need for Causal, Explanatory Models in Risk Assessment (2/4)
- Chapter 2 - The Need for Causal, Explanatory Models in Risk Assessment (3/4)
- Chapter 2 - The Need for Causal, Explanatory Models in Risk Assessment (4/4)
- Chapter 3 - Measuring Uncertainty: The Inevitability of Subjectivity (1/4)
- Chapter 3 - Measuring Uncertainty: The Inevitability of Subjectivity (2/4)
- Chapter 3 - Measuring Uncertainty: The Inevitability of Subjectivity (3/4)
- Chapter 3 - Measuring Uncertainty: The Inevitability of Subjectivity (4/4)
- Chapter 4 - The Basics of Probability (1/9)
- Chapter 4 - The Basics of Probability (2/9)
- Chapter 4 - The Basics of Probability (3/9)
- Chapter 4 - The Basics of Probability (4/9)
- Chapter 4 - The Basics of Probability (5/9)
- Chapter 4 - The Basics of Probability (6/9)
- Chapter 4 - The Basics of Probability (7/9)
- Chapter 4 - The Basics of Probability (8/9)
- Chapter 4 - The Basics of Probability (9/9)
- Chapter 5 - Bayes’ Theorem and Conditional Probability (1/4)
- Chapter 5 - Bayes’ Theorem and Conditional Probability (2/4)
- Chapter 5 - Bayes’ Theorem and Conditional Probability (3/4)
- Chapter 5 - Bayes’ Theorem and Conditional Probability (4/4)
- Chapter 6 - From Bayes’ Theorem to Bayesian Networks (1/8)
- Chapter 6 - From Bayes’ Theorem to Bayesian Networks (2/8)
- Chapter 6 - From Bayes’ Theorem to Bayesian Networks (3/8)
- Chapter 6 - From Bayes’ Theorem to Bayesian Networks (4/8)
- Chapter 6 - From Bayes’ Theorem to Bayesian Networks (5/8)
- Chapter 6 - From Bayes’ Theorem to Bayesian Networks (6/8)
- Chapter 6 - From Bayes’ Theorem to Bayesian Networks (7/8)
- Chapter 6 - From Bayes’ Theorem to Bayesian Networks (8/8)
- Chapter 7 - Defining the Structure of Bayesian Networks (1/9)
- Chapter 7 - Defining the Structure of Bayesian Networks (2/9)
- Chapter 7 - Defining the Structure of Bayesian Networks (3/9)
- Chapter 7 - Defining the Structure of Bayesian Networks (4/9)
- Chapter 7 - Defining the Structure of Bayesian Networks (5/9)
- Chapter 7 - Defining the Structure of Bayesian Networks (6/9)
- Chapter 7 - Defining the Structure of Bayesian Networks (7/9)
- Chapter 7 - Defining the Structure of Bayesian Networks (8/9)
- Chapter 7 - Defining the Structure of Bayesian Networks (9/9)
- Chapter 8 - Building and Eliciting Node Probability Tables (1/11)
- Chapter 8 - Building and Eliciting Node Probability Tables (2/11)
- Chapter 8 - Building and Eliciting Node Probability Tables (3/11)
- Chapter 8 - Building and Eliciting Node Probability Tables (4/11)
- Chapter 8 - Building and Eliciting Node Probability Tables (5/11)
- Chapter 8 - Building and Eliciting Node Probability Tables (6/11)
- Chapter 8 - Building and Eliciting Node Probability Tables (7/11)
- Chapter 8 - Building and Eliciting Node Probability Tables (8/11)
- Chapter 8 - Building and Eliciting Node Probability Tables (9/11)
- Chapter 8 - Building and Eliciting Node Probability Tables (10/11)
- Chapter 8 - Building and Eliciting Node Probability Tables (11/11)
- Chapter 9 - Numeric Variables and Continuous Distribution Functions (1/8)
- Chapter 9 - Numeric Variables and Continuous Distribution Functions (2/8)
- Chapter 9 - Numeric Variables and Continuous Distribution Functions (3/8)
- Chapter 9 - Numeric Variables and Continuous Distribution Functions (4/8)
- Chapter 9 - Numeric Variables and Continuous Distribution Functions (5/8)
- Chapter 9 - Numeric Variables and Continuous Distribution Functions (6/8)
- Chapter 9 - Numeric Variables and Continuous Distribution Functions (7/8)
- Chapter 9 - Numeric Variables and Continuous Distribution Functions (8/8)
- Chapter 10 - Hypothesis Testing and Confidence Intervals (1/8)
- Chapter 10 - Hypothesis Testing and Confidence Intervals (2/8)
- Chapter 10 - Hypothesis Testing and Confidence Intervals (3/8)
- Chapter 10 - Hypothesis Testing and Confidence Intervals (4/8)
- Chapter 10 - Hypothesis Testing and Confidence Intervals (5/8)
- Chapter 10 - Hypothesis Testing and Confidence Intervals (6/8)
- Chapter 10 - Hypothesis Testing and Confidence Intervals (7/8)
- Chapter 10 - Hypothesis Testing and Confidence Intervals (8/8)
- Chapter 11 - Modeling Operational Risk (1/7)
- Chapter 11 - Modeling Operational Risk (2/7)
- Chapter 11 - Modeling Operational Risk (3/7)
- Chapter 11 - Modeling Operational Risk (4/7)
- Chapter 11 - Modeling Operational Risk (5/7)
- Chapter 11 - Modeling Operational Risk (6/7)
- Chapter 11 - Modeling Operational Risk (7/7)
- Chapter 12 - Systems Reliability Modeling (1/6)
- Chapter 12 - Systems Reliability Modeling (2/6)
- Chapter 12 - Systems Reliability Modeling (3/6)
- Chapter 12 - Systems Reliability Modeling (4/6)
- Chapter 12 - Systems Reliability Modeling (5/6)
- Chapter 12 - Systems Reliability Modeling (6/6)
- Chapter 13 - Bayes and the Law (1/7)
- Chapter 13 - Bayes and the Law (2/7)
- Chapter 13 - Bayes and the Law (3/7)
- Chapter 13 - Bayes and the Law (4/7)
- Chapter 13 - Bayes and the Law (5/7)
- Chapter 13 - Bayes and the Law (6/7)
- Chapter 13 - Bayes and the Law (7/7)
- Appendix A: The Basics of Counting (1/2)
- Appendix A: The Basics of Counting (2/2)
- Appendix B: The Algebra of Node Probability Tables (1/2)
- Appendix B: The Algebra of Node Probability Tables (2/2)
- Appendix C: Junction Tree Algorithm (1/2)
- Appendix C: Junction Tree Algorithm (2/2)
- Appendix D: Dynamic Discretization (1/4)
- Appendix D: Dynamic Discretization (2/4)
- Appendix D: Dynamic Discretization (3/4)
- Appendix D: Dynamic Discretization (4/4)
- Appendix E: Statistical Distributions (1/3)
- Appendix E: Statistical Distributions (2/3)
- Appendix E: Statistical Distributions (3/3)
- Back Cover

## Product information

- Title: Risk Assessment and Decision Analysis with Bayesian Networks
- Author(s):
- Release date: June 2013
- Publisher(s): CRC Press
- ISBN: 9781439809112

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