Wednesday, 28 March 2018

Review of our book by Eric Lawton

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.

Review of our book in ICACA

There is a review of our book in the Journal of the ICACA (full pdf of the review is here)

Excerpts:

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.

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.
 We can even forgive the reference to "Agenda Risk" rather than "AgenaRisk"

Wednesday, 30 April 2014

New reviews of the book

Two journals have published excellent reviews of the book:

Journal of Statistical Theory and Practice, Vol. 8, March 2014 (full review: http://dx.doi.org/10.1080/15598608.2014.847770 
"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 Lucas
International Journal of Performability Engineering, Vol.9, No. 3, July 2013, pp 551-553 (full review: http://bayesianrisk.com/reviews/vesely.pdf):
“… this book will be found 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 experience in the area, I highly recommend the book.” --William E. Vesely (NASA),

Tuesday, 1 April 2014

Douglas Hubbard posts excellent review of our book on amazon.com

Douglas Hubbard - author of the brilliant (and top selling) books  How to Measure Anything, The Failure of Risk Management, and Pulse has written a terrific review of our book on amazon.com. This is the review verbatim:

The single most important book on Bayesian methods for decision analysts, March 19, 2014
By Douglas W. Hubbard (Glen Ellyn, IL) Amazon Verified Purchase
This review is from: Risk Assessment and Decision Analysis with Bayesian Networks (Hardcover)

Fenton and Neil have successfully made a "crossover" book that reaches broad audiences on a topic which is too often presented in a dry and esoteric manner. It is rich with illustrations, interesting examples, debunking of common fallacies, and a passionate philosophical position on Bayesian methods vs. the "frequentist" methods common in statistics.

This book is a comprehensive treatment of Bayesian methods but focuses on the particularly powerful models that can be made when conditional probabilities are presented in networks. The authors present a complete algebra of Bayesian networks using both formal expressions and simple diagrams so that almost any reader can be comfortable with the topic. This book does not assume that the reader has even basic training in probabilistic methods (it has a chapter on the basics of probability) but it also does not compromise on substantive content. The reader seeking basic explanations will not feel excluded and the reader seeking more advanced treatments will be satisfied as well.

This is exactly the sort of rigorous thinking that needs to displace the "softer" methods more common in risk assessment and decision analysis. It is presented as an entirely practical solution for managers, not an abstract, academic exercise. The "best practices" committees for PMBOK, ISO, Cobit and managers everywhere would be well advised to read this book before inventing yet another risk assessment or decision analysis method based on fluffy scores.

Doug Hubbard Author of How to Measure Anything (2007, 2010, 2014), The Failure of Risk Management (2009) and Pulse (2011)
The book now has 19 customer reviews on amazon.com (15 of which are 5-star and 4 of which are 4-star) and 10 customer reviews on amazon.co.uk (7 of which are 5-star and 3 of which are 4-star).

Last week the book was number 1 top seller on amazon in the 'risk management' category.