Risk Assessment and Decision Analysis with Bayesian Networks

Front Cover
CRC Press, Sep 3, 2018 - Mathematics - 660 pages

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.

 

Selected pages

Contents

The Need for Causal Explanatory Models in Risk Assessment
2-43
The Inevitability of Subjectivity
3-22
The Basics of Probability
3-43
Bayes Theorem and Conditional Probability
3-68
Chapter 7
7-7
Chapter 15
7-15
Chapter 8
8-57
Building and Eliciting Node Probability Tables
8-63
Population Mean and Variance from Sample Mean
10-98
Modeling Operational Risk
13-1
Systems Reliability Modeling
13-24
The Role of Bayes in Forensic and Legal Evidence Presentation
13-59
Further Reading
13-90
Further Reading
13-125
Learning from Data in Bayesian Networks
13-126
The Basics of Counting
13-150

Numeric Variables and Continuous Distribution Functions
9-53
Dependent
10-7
Chapter 16
10-16
Decision Analysis Decision Trees Value of Information Analysis
10-50
Hypothesis Testing and Confidence Intervals
10-76
The Algebra of Node Probability Tables
13-157
Dynamic Discretization
13-169
Statistical Distributions
13-192
Index
13-206
Copyright

Other editions - View all

Risk Assessment and Decision Analysis with Bayesian Networks
Norman Fenton,Martin Neil
Limited preview - 2018
Risk Assessment and Decision Analysis with Bayesian Networks, Second Edition
Norman Fenton,Martin Neil
No preview available - 2018

Common terms and phrases

actually AgenaRisk algorithm analysis assume assumptions Axiom Bayes Bayes factor Bayesian networks belief biased Binomial distribution BN model Boolean node calculate cancer causal model cause Chapter classical coin compute conditional consider d-separated decision define the NPT disease Drug taken dynamic discretization elementary events equal evidence example experiment explained in Box expression factors false frequentist function given Hence independent influence diagram instantiation intervals junction tree loss marginal probability mean measurement idiom mutually exclusive mutually exclusive events NoisyOR Normal distribution Norman late null hypothesis numeric nodes O.J. Simpson murdered observation OOBN outcome of interest p-value parameters parent nodes possible posterior power4 prediction prior probability probability tables probability values probative problem range ranked node reasoning represent result risk assessment sample score shown in Figure shown in Table simply Simpson’s solution specify statistical Suppose theorem toss train strike true uncertainty variables variance winning WMIN

About the author (2018)

Norman Fenton is Professor of Risk Information Management in the School of Electronic Engineering and Computer Science at Queen Mary University of London and is also a Director of Agena, a company that specialises in risk management for critical systems. Norman is a mathematician by training who now works on quantitative risk assessment. His experience covers a wide range of application domains such as legal reasoning (he has been an expert witness in major criminal and civil cases), medical analytics, vehicle reliability, embedded software, transport systems, financial services, and football prediction. Norman has a special interest in raising public awareness of the importance of probability theory and Bayesian reasoning in everyday life. Norman has published 7 books and 250 referred articles.

Martin Neil is a Professor in Computer Science and Statistics in the School of Electronic Engineering and Computer Science at Queen Mary, University of London and is also a Director and joint founder and of Agena Ltd, who develop and distribute AgenaRisk, a software product for modeling risk and uncertainty. In addition to working on theoretical and algorithmic foundations, his research covers a wide range of application domains including medical analytics, legal reasoning, embedded software, operational risk in finance, systems and design reliability (including software), project risk, commercial risk, decision support, cost benefit analysis, AI and personalization, machine learning, legal argumentation and cyber security. At Queen Mary he teaches decision and risk analysis. Martin was a fellow at the Newton Institute for Mathematical Sciences, Cambridge University in 2016 and was invited to the Fields Institute for Research in Mathematical Sciences, University of Toronto, Canada in 2010. Martin has published over 100 refereed articles.

Bibliographic information

TitleRisk Assessment and Decision Analysis with Bayesian Networks
AuthorsNorman Fenton, Martin Neil
Edition2, illustrated
PublisherCRC Press, 2018
ISBN1351978969, 9781351978965
Length660 pages
Subjects ›  › 

Business & Economics / Management Science
Computers / Data Science / Data Analytics
Mathematics / Probability & Statistics / General
  
Export CitationBiBTeX EndNote RefMan