There Is More to Assessing Risk Than
Statistics
Introduction
Predicting Economic Growth: The Normal Distribution and Its
Limitations
Patterns and Randomness: From School League Tables to Siegfried and
Roy
Dubious Relationships: Why You Should Be Very Wary of Correlations
and
Their Significance Values
Spurious Correlations: How You Can Always Find a Silly `Cause’ of
Exam
Success
The Danger of Regression: Looking Back When You Need to Look
Forward
The Danger of Averages
When Simpson’s Paradox Becomes More Worrisome
Uncertain Information and Incomplete Information: Do Not Assume
They Are
Different
Do Not Trust Anybody (Even Experts) to Properly Reason about
Probabilities
Chapter Summary
Further Reading
The Need for Causal, Explanatory Models in Risk
Assessment
Introduction
Are You More Likely to Die in an Automobile Crash When the Weather
Is
Good Compared to Bad?
The Limitations of Common Approaches to Risk Assessment
Thinking about Risk Using Causal Analysis
Applying the Causal Framework to Armageddon
Summary
Further Reading
Measuring Uncertainty: The Inevitability of
Subjectivity
Introduction
Experiments, Outcomes, and Events
Frequentist versus Subjective View of Uncertainty
Summary
Further Reading
The Basics of Probability
Introduction
Some Observations Leading to Axioms and Theorems of Probability
Probability Distributions
Independent Events and Conditional Probability
Binomial Distribution
Using Simple Probability Theory to Solve Earlier Problems and
Explain
Widespread Misunderstandings
Summary
Further Reading
Bayes’ Theorem and Conditional
Probability
Introduction
All Probabilities Are Conditional
Bayes’ Theorem
Using Bayes’ Theorem to Debunk Some Probability Fallacies
Second-Order Probability
Summary
Further Reading
From Bayes’ Theorem to Bayesian
Networks
Introduction
A Very Simple Risk Assessment Problem
Accounting for Multiple Causes (and Effects)
Using Propagation to Make Special Types of Reasoning Possible
The Crucial Independence Assumptions
Structural Properties of BNs
Propagation in Bayesian Networks
Using BNs to Explain Apparent Paradoxes
Steps in Building and Running a BN Model
Summary
Further Reading
Theoretical Underpinnings
BN Applications
Nature and Theory of Causality
Uncertain Evidence (Soft and Virtual)
Defining the Structure of Bayesian
Networks
Introduction
Causal Inference and Choosing the Correct Edge Direction
The Idioms
The Problems of Asymmetry and How to Tackle Them
Multiobject Bayesian Network Models
The Missing Variable Fallacy
Conclusions
Further Reading
Building and Eliciting Node Probability
Tables
Introduction
Factorial Growth in the Size of Probability Tables
Labeled Nodes and Comparative Expressions
Boolean Nodes and Functions
Ranked Nodes
Elicitation
Summary
Further Reading
Numeric Variables and Continuous Distribution
Functions
Introduction
Some Theory on Functions and Continuous Distributions
Static Discretization
Dynamic Discretization
Using Dynamic Discretization
Avoiding Common Problems When Using Numeric Nodes
Summary
Further Reading
Hypothesis Testing and Confidence
Intervals
Introduction
Hypothesis Testing
Confidence Intervals
Summary
Further Reading
Modeling Operational Risk
Introduction
The Swiss Cheese Model for Rare Catastrophic Events
Bow Ties and Hazards
Fault Tree Analysis (FTA)
Event Tree Analysis (ETA)
Soft Systems, Causal Models, and Risk Arguments
KUUUB Factors
Operational Risk in Finance
Summary
Further Reading
Systems Reliability Modeling
Introduction
Probability of Failure on Demand for Discrete Use Systems
Time to Failure for Continuous Use Systems
System Failure Diagnosis and Dynamic Bayesian Networks
Dynamic Fault Trees (DFTs)
Software Defect Prediction
Summary
Further Reading
Bayes and the Law
Introduction
The Case for Bayesian Reasoning about Legal Evidence
Building Legal Arguments Using Idioms
The Evidence Idiom
The Evidence Accuracy Idiom
Idioms to Deal with the Key Notions of “Motive” and
“Opportunity”
Idiom for Modeling Dependency between Different Pieces of
Evidence
Alibi Evidence Idiom
Putting it All Together: Vole Example
Using BNs to Expose Further Fallacies of Legal Reasoning
Summary
Further Reading
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
"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 mathematical or statistical background. I
highly recommend this book to all researchers, students and
practitioners who would like to go beyond traditional statistics or
automated data mining techniques and incorporate expert knowledge
in their models."
—Dr. Łukasz Radliński, Szczecin University "It is the first book
that takes the art and science of developing Bayesian network
models for actual problems as seriously as the underlying
mathematics. The reader will obtain a good understanding of the
methods as they are introduced through well-motivated and intuitive
examples and attractive case studies. The authors do this in such a
way that readers with little previous exposure to probability
theory and statistics will be able to grasp and appreciate the
power of Bayesian networks. While this in itself is already a major
achievement, the authors go far beyond this by providing very close
and pragmatic links between model building and the required
techniques. It, thus, shares insights that are mostly missing from
other textbooks, making this book also of interest to advanced
readers, lecturers and researchers in the area."
—Prof.dr. Peter Lucas, Institute for Computing and Information
Sciences, Radboud University Nijmegen, and Leiden Institute of
Advanced Computer Science, Leiden University "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 "Given the massive uncertainties managers now need to
operate within, this book is both vital and timely. Fenton and
Neil’s explanation of how to create practical models that simulate
real-life strategic scenarios gives hard-pressed managers a new
tool that they can use to understand potential impacts and
opportunities. This book should be required reading for anyone
involved in strategy, business planning, or significant
decision-making."
—Rob Wirszycz, Celaton Limited
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