|Norman Fenton and Martin Neil|
Thursday, 15 November 2018
Tuesday, 4 September 2018
Still waiting to get our own copies of the second edition of the book, but one of our PhD students just received his copy, so it is real! The first edition (published Dec 2012) now has 437 Google scholar citations, and many dozens of 5-star reviews on Amazon.
Friday, 10 August 2018
From the back cover of the Second Edition:
"The single most important book on Bayesian methods for decision analysts" —Doug Hubbard (author in decision sciences and actuarial science)
"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." —Judea Pearl (Turing award winner)
"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.” —Angela Saini (award-winning science journalist, author & broadcaster)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.
- 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
Thursday, 9 August 2018
Wednesday, 28 March 2018
We found this review on goodreads.com
"A good stack of examples, as large as possible, is indispensable for a thorough understanding of any concept, and when I want to learn something new, I make it my first job to build one." – Paul Halmos, mathematician (by coincidence, saw this quotation twice today, but it is a big reason to read this book, as you will see).
This is my new favourite applied mathematics book, the equal of Misner, Thorne and Wheeler's Gravitation (1971).
If you want to write technical books, it is worth getting the free sample chapters of this book, even if you’re not interested in the topics, just to see how to write books on difficult technical topics, and really worth getting if you are interested in statistics and probability. Even if the subject matter is familiar, there are so many good explanations and so many examples for each topic, drawn from different disciplines, that it is worth reading just you can use with your less informed friends.
I wanted to learn Bayesian Networks. I bought 3 books. This one, Causality, by Judea Pearl and Bayesian Networks in R with Applications in Systems Biology by Rahakrishnan Nagarajan et. al. The first is also very good; review later as I'm not so far into it. The other is awful, I'll review that too. “Causality” revealed some holes in my memory of basic probability and statistics, which is why I bought “Risk Assessment...” (RA). I also got a bunch of Internet resources because when learning without an advisor, I need help in case I don’t understand something important. However, so far (half-way through), I’ve not needed anything else at all. Judea Pearl, who is a leading authority on these topics, wrote the very approving Foreword to this book and I’m now finding the more advance Pearl book much easier now I’ve read this.
RA is written in Tufte style, which I've adopted myself for my own writing. If you don’t know Edward Tufte’s writing on how to present numbers and facts, that's worth looking into, but you’ll get a really good demonstration from reading this book. The advantage is that the main text is supplemented by sidebars of illustrations, examples and further explanations so that you can either go fast where you follow the concepts or look at the sidebars for more explanation if you get bogged down. And even though the book eases you into each topic, you also get the formal mathematical notations and proofs so you can understand related papers and books. And you can use the many examples as exercises. Stop and work them yourself before reading on.
All this, plus access to free software and data to work the examples that need computer assistance. Although I’m doing working in Python and instead, the AgenaRisk software is much more user friendly. If you are not a programmer and don’t want to be, you can still use a computer to to see what is going on. If you know R or Python, you can easily The book web site (http://www.bayesianrisk.com/) links to the free chapters and to a blog and other supporting material.
I wish I’d had this 10 years ago when risk assessment was a big part of my job; I just got this because 90% of the book is setting up the probability and statistical machinery so even if not interested in risk management, but it I have read the risk parts and they are really good for anyone running a more-than-tiny project where things can go wrong. Risk management is a huge part of any project management. If you’re still at the “risk=probability×impact” stage, this book will up your game a lot.
There is a review of our book in the Journal of the ICACA (full pdf of the review is here)
Start at the beginning, and you will find it is clear that this book is written to be understandable by professional people who are interested in risk assessment and decision making; readers do not need an in-depth knowledge of statistics for the book to be enjoyable and useful.We can even forgive the reference to "Agenda Risk" rather than "AgenaRisk"
For those looking to begin working with Bayesian networks, this book serves as an excellent starting point and provides guidance for readers on how to develop and run a Bayesian network model for risk assessment and decision making.