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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. Praise for the first edition:


"By offering many attractive examples of Bayesian networks and by making use of software that allows one to play with the networks, readers will definitely get a feel for what can be done with Bayesian networks. ... the power and also uniqueness of the book stem from the fact that it is essentially practice oriented, but with a clear aim of equipping the developer of Bayesian networks with a clear understanding of the underlying theory. Anyone involved in everyday decision making looking for a better foundation of what is now mainly based on intuition will learn something from the book."
-Peter J.F. Lucas, Journal of Statistical Theory and Practice, Vol. 8, March 2014


"... very useful to practitioners, professors, students, and anyone interested in understanding the application of Bayesian networks to risk assessment and decision analysis. Having many years of experience in the area, I highly recommend the book."
-William E. Vesely, International Journal of Performability Engineering, July 2013


"Risk Assessment and Decision Analysis with Bayesian Networks is a brilliant book. Being a non-mathematician, I've found all of the other books on BNs to be an impenetrable mass of mathematical gobble-de-gook. This, in my view, has slowed the uptake of BNs in many disciplines because people simply cannot understand why you would use them and how you can use them. This book finally makes BNs comprehensible, and I plan to develop a risk assessment course at the University of Queensland using this book as the recommended textbook."
-Carl Smith, School of Agriculture and Food Sciences, The University of Queensland


"... although there have been several excellent books dedicated to Bayesian networks and related methods, these books tend to be aimed at readers who already have a high level of mathematical sophistication ... . As such they are not accessible to readers who are not already proficient in those subjects. This book is an exciting development because it addresses this problem. ... it should be understandable by any numerate reader interested in risk assessment and decision making. The book provides sufficient motivation and examples (as well as the mathematics and probability where needed from scratch) to enable readers to understand the core principles and power of Bayesian networks. However, the focus is on ensuring that readers can build practical Bayesian network models ... readers are provided with a tool that performs the propagation, so they will be able to build their own models to solve real-world risk assessment problems."
-From the Foreword by Judea Pearl, UCLA Computer Science Department and 2011 Turing Award winner


"Let's be honest, most risk assessment methodologies are guesses, and not very good ones at that. People collect statistics about what they can see and then assume it tells them something about what they can't. The problem is that people assume the world follows nice distributions embedded in the world's fabric and that we simply need a little data to get the parameters right. Fenton and Neil take readers on an excellent journey through a more modern and appropriate way to make sense of uncertainty by leveraging prior beliefs and emerging evidence. Along the way they provide a wakeup call for the classic statistical views of risk and eloquently show the biases, fallacies and misconceptions that exist in such a view, and how dangerous they are for those making decisions.
The book is not condescending to those without a mathematical background and is not too simple for those who do. It sets a nice tone which focuses more on how readers should think about risk and uncertainty and then uses a wealth of practical examples to show them how Bayesian methods can deliver powerful insights.
After reading this book, you should be in no doubt that not only is it possible to model risk from the perspective of understanding how it behaves, but also that is necessarily the only sensible way to do so if you want to do something useful with your model and make correct decisions from it.
Anyone aspiring to work, or already working, in the field of risk is well advised to read this book and put it into practice."
-Neil Cantle, Milliman


"The lovely thing about Risk Assessment and Decision Analysis with Bayesian Networks is that it holds your hand while it guides you through this maze of statistical fallacies, p-values, randomness and subjectivity, eventually explaining how Bayesian networks work and how they can help to avoid mistakes. There are loads of vivid examples (for instance, one explaining the Monty Hall problem), and it doesn't skim over any of the technical details ..."
-Angela Saini (MIT Knight Science Journalism Fellow 2012-2013) on her blog, December 2012


"As computational chip size and product development cycle time approach zero, survival in the software industry becomes predicated on three related capabilities: prediction, diagnosis, and causality. These are the competitive advantages in 21st century software design testing. Fenton and Neil not only make a compelling case for Bayesian inference, but they also meticulously and patiently guide software engineers previously untrained in probability theory toward competence in mathematics. We have been waiting for decades for the last critical component that will make Bayesian a household word in industry: the incredible combination of an accessible software tool and an accompanying and brilliantly written textbook. Now software testers have the math, the algorithms, the tool, and the book. We no longer have any excuses for not dramatically raising our technology game to meet that challenge of continuous testing. Fenton and Neil came to our rescue, and just in the nick of time. Thanks, guys."
-Michael Corning, Microsoft Corporation


"This is an awesome book on using Bayesian networks for risk assessment and decision analysis. What makes this book so great is both its content and style. Fenton and Neil explain how the Bayesian networks work and how they can be built and applied to solve various decision-making problems in different areas. Even more importantly, the authors very clearly demonstrate motivations and advantages for using Bayesian networks over other modelling techniques. The core ideas are illustrated by lots of examples-from toy models to real-world applications. In contrast with many other books, this one is very easy to follow and does not require a strong mat

Risk Assessment and Decision Analysis with Bayesian Networks

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.