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Risk Assessment and Decision Analysis with Bayesian Networks - Norman Fenton

Risk Assessment and Decision Analysis with Bayesian Networks

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Published: 4th December 2012
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Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide powerful insights and better decision making.

  • 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, and more
  • Introduces all necessary mathematics, probability, and statistics as needed

The book first establishes the basics of probability, risk, and building and using BN models, then goes into the detailed applications. The underlying BN algorithms appear in appendices rather than the main text since there is no need to understand them to build and use BN models. Keeping the body of the text free of intimidating mathematics, the book provides pragmatic advice about model building to ensure models are built efficiently.

A dedicated website, www.BayesianRisk.com, contains executable versions of all of the models described, exercises and worked solutions for all chapters, PowerPoint slides, numerous other resources, and a free downloadable copy of the AgenaRisk software.

"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. Lukasz Radlinski, 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

Forewordp. xi
Prefacep. xiii
Acknowledgmentsp. xvii
Authorsp. xix
There Is More to Assessing Risk Than Statisticsp. 1
Introductionp. 1
Predicting Economic Growth: The Normal Distribution and Its Limitationsp. 3
Patterns and Randomness: From School League Tables to Siegfried and Royp. 7
Dubious Relationships: Why You Should Be Very Wary of Correlations and Their Significance Valuesp. 10
Spurious Correlations: How You Can Always Find a Silly 'Cause' of Exam Successp. 14
The Danger of Regression: Looking Back When You Need to Look Forwardp. 16
The Danger of Averagesp. 18
What Type of Average?p. 19
When Averages Alone Will Never Be Sufficient for Decision Makingp. 20
When Simpson's Paradox Becomes More Worrisomep. 21
Uncertain Information and Incomplete Information: Do Not Assume They Are Differentp. 23
Do Not Trust Anybody (Even Experts) to Properly Reason about Probabilitiesp. 26
Chapter Summaryp. 29
Further Readingp. 29
The Need for Causal, Explanatory Models in Risk Assessmentp. 31
Introductionp. 31
Are You More Likely to Die in an Automobile Crash When the Weather Is Good Compared to Bad?p. 31
When Ideology and Causation Collidep. 35
The Limitations of Common Approaches to Risk Assessmentp. 37
Measuring Armageddon and Other Risksp. 37
Risks and Opportunitiesp. 39
Risk Registers and Heat Mapsp. 40
Thinking about Risk Using Causal Analysisp. 42
Applying the Causal Framework to Armageddonp. 46
Summaryp. 49
Further Readingp. 49
Measuring Uncertainty: The Inevitability of Subjectivityp. 51
Introductionp. 51
Experiments, Outcomes, and Eventsp. 52
Multiple Experimentsp. 56
Joint Experimentsp. 57
Joint Events and Marginalizationp. 58
Frequentist versus Subjective View of Uncertaintyp. 60
Summaryp. 67
Further Readingp. 68
The Basics of Probabilityp. 69
Introductionp. 69
Some Observations Leading to Axioms and Theorems of Probabilityp. 69
Probability Distributionsp. 81
Probability Distributions with Infinite Outcomesp. 83
Joint Probability Distributions and Probability of Marginalized Eventsp. 85
Dealing with More than Two Variablesp. 88
Independent Events and Conditional Probabilityp. 89
Binomial Distributionp. 96
Using Simple Probability Theory to Solve Earlier Problems and Explain Widespread Misunderstandingsp. 101
The Birthday Problemp. 101
The Monty Hall Problemp. 103
When Incredible Events Are Really Mundanep. 105
When Mundane Events Really Are Quite Incrediblep. 109
Summaryp. 110
Further Readingp. 111
Bayes' Theorem and Conditional Probabilityp. 113
Introductionp. 113
All Probabilities Are Conditionalp. 113
Bayes' Theoremp. 116
Using Bayes' Theorem to Debunk Some Probability Fallaciesp. 121
Traditional Statistical Hypothesis Testingp. 122
The Prosecutor Fallacy Revisitedp. 124
The Defendant's Fallacyp. 124
Odds Form of Bayes and the Likelihood Ratiop. 125
Second-Order Probabilityp. 127
Summaryp. 129
Further Readingp. 129
From Bayes' Theorem to Bayesian Networksp. 131
Introductionp. 131
A Very Simple Risk Assessment Problemp. 132
Accounting for Multiple Causes (and Effects)p. 134
Using Propagation to Make Special Types of Reasoning Possiblep. 137
The Crucial Independence Assumptionsp. 139
Structural Properties of BNsp. 144
Serial Connection: Causal and Evidential Trialsp. 144
Diverging Connection: Common Causep. 147
Converging Connection: Common Effectp. 149
Determining Whether Any Two Nodes in a BN Are Dependentp. 151
Propagation in Bayesian Networksp. 153
Using BNs to Explain Apparent Paradoxesp. 156
Revisiting the Monty Hall Problemp. 156
Simple Solutionp. 156
Complex Solutionp. 157
Revisiting Simpson's Paradoxp. 161
Steps in Building and Running a BN Modelp. 162
Building a BN Modelp. 162
Running a BN Modelp. 166
Inconsistent Evidencep. 168
Summaryp. 169
Further Readingp. 169
Theoretical Underpinningsp. 169
BN Applicationsp. 169
Nature and Theory of Causalityp. 170
Uncertain Evidence (Soft and Virtual)p. 170
Defining the Structure of Bayesian Networksp. 171
Introductionp. 171
Causal Inference and Choosing the Correct Edge Directionp. 172
The Idiomsp. 174
The Cause-Consequence Idiomp. 175
Measurement Idiomp. 177
Definitional/Synthesis Idiomp. 184
Case 1: Definitional Relationship between Variablesp. 184
Case 2: Hierarchical Definitionsp. 184
Case 3: Combining Different Nodes Together to Reduce Effects of Combinatorial Explosion ("Divorcing")p. 185
Induction Idiomp. 188
The Problems of Asymmetry and How to Tackle Themp. 190
Impossible Pathsp. 190
Mutually Exclusive Pathsp. 192
Distinct Causal Pathwaysp. 194
Taxonomic Classificationp. 196
Multiobject Bayesian Network Modelsp. 202
The Missing Variable Fallacyp. 207
Conclusionsp. 212
Further Readingp. 213
Building and Eliciting Node Probability Tablesp. 215
Introductionp. 215
Factorial Growth in the Size of Probability Tablesp. 215
Labeled Nodes and Comparative Expressionsp. 217
Boolean Nodes and Functionsp. 221
The Asia Modelp. 222
The OR Function for Boolean Nodesp. 227
The AND Function for Boolean Nodesp. 234
M from N Operatorp. 235
NoisyOR Function for Boolean Nodesp. 236
Weighted Averagesp. 241
Ranked Nodesp. 244
Backgroundp. 244
Solution: Ranked Nodes with the TNormal Distributionp. 246
Alternative Weighted Functions for Ranked Nodesp. 252
Hints and Tips When Working with Ranked Nodes and NPTsp. 255
Tip 1: Use the Weighted Functions as Far as Possiblep. 255
Tip 2: Make Use of the Fact That a Ranked Node Parent Has an Underlying Numerical Scalep. 255
Tip 3: Do Not Forget the Importance of the Variance in the TNormal Distributionp. 256
Tip 4: Change the Granularity of a Ranked Scale without Having to Make Any Other Changesp. 259
Tip 5: Do Not Create Large, Deep, Hierarchies Consisting of Rank Nodesp. 260
Elicitationp. 260
Elicitation Protocols and Cognitive Biasesp. 260
Scoring Rules and Validationp. 263
Sensitivity Analysisp. 264
Summaryp. 265
Further Readingp. 265
Numeric Variables and Continuous Distribution Functionsp. 267
Introductionp. 267
Some Theory on Functions and Continuous Distributionsp. 268
Static Discretizationp. 273
Dynamic Discretizationp. 280
Using Dynamic Discretizationp. 283
Prediction Using Dynamic Discretizationp. 283
Conditioning on Discrete Evidencep. 287
Parameter Learning (Induction) Using Dynamic Discretizationp. 289
Classical versus Bayesian Modelingp. 289
Bayesian Hierarchical Model Using Beta-Binomialp. 294
Avoiding Common Problems When Using Numeric Nodesp. 300
Unintentional Negative Values in a Node's State Rangep. 300
Potential Division by Zerop. 301
Using Unbounded Distributions on a Bounded Rangep. 301
Observations with Very Low Probabilityp. 302
Summaryp. 303
Further Readingp. 303
Hypothesis Testing and Confidence Intervalsp. 305
Introductionp. 305
Hypothesis Testingp. 305
Bayes Factorsp. 306
Testing for Hypothetical Differencesp. 308
Comparing Bayesian and Classical Hypothesis Testingp. 311
Model Comparison: Choosing the Best Predictive Modelp. 315
Accommodating Expert Judgments about Hypothesesp. 322
Distribution Fitting as Hypothesis Testingp. 325
Bayesian Model Comparison and Complex Causal Hypothesesp. 326
Confidence Intervalsp. 333
The Fallacy of Frequentist Confident Intervalsp. 333
The Bayesian Alternative to Confidence Intervalsp. 337
Summaryp. 340
Further Readingp. 341
Modeling Operational Riskp. 343
Introductionp. 343
The Swiss Cheese Model for Rare Catastrophic Eventsp. 344
Bow Ties and Hazardsp. 347
Fault Tree Analysis (FTA)p. 348
Event Tree Analysis (ETA)p. 354
Soft Systems, Causal Models, and Risk Argumentsp. 357
KUUUB Factorsp. 362
Operational Risk in Financep. 364
Modeling the Operational Loss Generation Processp. 364
Scenarios and Stress Testingp. 372
Summaryp. 375
Further Readingp. 376
Systems Reliability Modelingp. 377
Introductionp. 377
Probability of Failure on Demand for Discrete Use Systemsp. 378
Time to Failure for Continuous Use Systemsp. 380
System Failure Diagnosis and Dynamic Bayesian Networksp. 383
Dynamic Fault Trees (DFTs)p. 387
Software Defect Predictionp. 395
Summaryp. 404
Further Readingp. 404
Bayes and the Lawp. 407
Introductionp. 407
The Case for Bayesian Reasoning about Legal Evidencep. 408
Building Legal Arguments Using Idiomsp. 411
The Evidence Idiomp. 411
The Evidence Accuracy Idiomp. 414
Idioms to Deal with the Key Notions of "Motive" and "Opportunity"p. 417
Idiom for Modeling Dependency between Different Pieces of Evidencep. 420
Alibi Evidence Idiomp. 422
Explaining away Idiomp. 425
Putting it All Together: Vole Examplep. 428
Using BNs to Expose Further Fallacies of Legal Reasoningp. 433
The Jury Observation Fallacyp. 433
The "Crimewatch UK" Fallacyp. 435
Summaryp. 438
Further Readingp. 438
The Basics of Countingp. 441
The Algebra of Node Probability Tablesp. 449
Junction Tree Algorithmp. 455
Dynamic Discretizationp. 465
Statistical Distributionsp. 483
Indexp. 495
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781439809105
ISBN-10: 1439809100
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 524
Published: 4th December 2012
Publisher: Taylor & Francis Inc
Country of Publication: US
Dimensions (cm): 25.4 x 18.42  x 2.54
Weight (kg): 1.07
Edition Number: 1