… 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
This book gives a thorough account of Bayesian networks, one of the
most widely used frameworks for reasoning with uncertainty, and their
application in domains as diverse as system reliability modelling and
legal reasoning. The book's central premise is that ‘essentially,
all models are wrong, but some are useful’ (G.E.P. Box), and the
book distinguishes itself by focusing on the art of building useful
models for risk assessment and decision analysis rather than on delving
into mathematical detail of the models that are built. The authors are
renowned for their ability to put Bayesian network technology into
practical use, and it is therefore no surprise that the book is filled
to the brim with motivating and relevant examples. With the
accompanying evaluation copy of the excellent AgenaRisk software,
readers can easily play around with the examples and gain valuable
insights of how the models behave ‘at work.’ I believe this
book should be of interest to practitioners working with risk
assessment and decision making and also as a valuable textbook in
undergraduate courses on probability and risk.
—Helge Langseth, Norwegian University of Science
and Technology
Bayesian networks are revolutionizing the way experts assess and
manage uncertainty. This is the first book to explain this powerful new
tool to a non-specialist audience. It takes us on a compelling journey
from the basics of probability to sophisticated networks of system
design, finance and crime. This trip is greatly supported by free
software, allowing readers to explore and develop Bayesian networks for
themselves. The style is accessible and entertaining, without
sacrificing conceptual or mathematical rigor. This book is a must-read
for anyone wanting to learn about Bayesian networks; it provides the
know-how and software so that we can all share this adventure into risk
and uncertainty.
—David Lagnado, Senior Lecturer in Cognitive and
Decision Sciences, University College London
This is the book I have wanted to see for many years. Whilst we are
entitled to see appropriate duty of care in any risk management
scenario, ill-informed practice is in fact prevalent in industry and
society. There is little real excuse for this as classical decision
theory has a long established history, and it can now be
operationalized in complex scenarios using the Bayesian network
technology that is a core theme of this book. The problem has been that
most books on Bayesian networks and decision theory focus in depth on
the technical foundations, and provide little in the way of practical
guidance on how to use the technology to support real-world risk
assessment and decision making.
In contrast, Norman Fenton and Martin Neil have provided a clearly
written and highly readable book that is packed with informative and
insightful examples. I had fun reading it, but there is also sufficient
technical detail so that one can obtain a deep understanding of the
subject from studying the book. It is a joy, and one that I keep
dipping back into.
—Paul Krause, Professor of Software Engineering,
University of Surrey