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Risk Assessment and Decision Analysis with Bayesian Networks 2nd New edition

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.

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.

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. Having many years of experience in the area, I highly recommend the book.

Customer Reviews

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. 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. 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 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.

Martin Neil

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.

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 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.

Bayesian networks causal models vs. machine learnt models - Professor Norman Fenton

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.

Risk Assessment and Decision Analysis with Bayesian Networks

Fenton and Neil came to our rescue, and just in the nick of time. Thanks, guys. 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.