Librería Portfolio

Summary
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

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, forensics, cybersecurity and more
Introduces all necessary mathematics, probability, and statistics as needed
Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications
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.



Table of Contents
There Is More to Assessing Risk Than Statistics.

The Need for Causal, Explanatory Models in Risk Assessment.

Measuring Uncertainty: The Inevitability of Subjectivity.

The Basics of Probability.

Bayes' Theorem and Conditional Probability.

From Bayes' Theorem to Bayesian Networks.

Defining the Structure of Bayesian Networks.

Building and Eliciting Node Probability Tables.

Numeric Variables and Continuous Distribution Functions.

Hypothesis Testing and Confidence Intervals.

Modeling Operational Risk.

Systems Reliability Modeling.

Bayes and the Law.

Learning Bayesian Networks.

Decision making, Influence Diagrams and Value of information.

Bayesian networks in forensics.

Using Bayesian networks to debunk bad statistics.

Bayesian networks for football prediction.

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.