Risk assessment and decision analysis with bayesian networks / Norman Fenton and Martin Neil.

"A Chapman & Hall book."

Includes bibliographical references and index.

Cover; Half Title; Title Page; Copyright Page; Dedication; Contents; Foreword; Preface; Acknowledgments; Authors; Chapter 1: Introduction; Chapter 2: Debunking Bad Statistics; 2.1 Predicting Economic Growth: The Normal Distribution and Its Limitations; 2.2 Patterns and Randomness: From School League Tables to Siegfried and Roy; 2.3 Dubious Relationships: Why You Should Be Very Wary of Correlations and Their Significance Values; 2.4 Spurious Correlations: How You Can Always Find a Silly "Cause" of Exam Success; 2.5 The Danger of Regression: Looking Back When You Need to Look Forward 2.6 The Danger of Averages2.6.1 What Type of Average?; 2.6.2 When Averages Alone Will Never Be Sufficient for Decision Making; 2.7 When Simpson's Paradox Becomes More Worrisome; 2.8 How We Measure Risk Can Dramatically Change Our Perception of Risk; 2.9 Why Relying on Data Alone Is Insufficient for Risk Assessment; 2.10 Uncertain Information and Incomplete Information: Do Not Assume They Are Different; 2.11 Do Not Trust Anybody (Even Experts) to Properly Reason about Probabilities; 2.12 Chapter Summary; Further Reading; Chapter 3: The Need for Causal, Explanatory Models in Risk Assessment 3.1 Introduction3.2 Are You More Likely to Die in an Automobile Crash When the Weather Is Good Compared to Bad?; 3.3 When Ideology and Causation Collide; 3.4 The Limitations of Common Approaches to Risk Assessment; 3.4.1 Measuring Armageddon and Other Risks; 3.4.2 Risks and Opportunities; 3.4.3 Risk Registers and Heat Maps; 3.5 Thinking about Risk Using Causal Analysis; 3.6 Applying the Causal Framework to Armageddon; 3.7 Decisions and Utilities; 3.8 Summary; Further Reading; Chapter 4: Measuring Uncertainty: The Inevitability of Subjectivity; 4.1 Introduction 4.2 Experiments, Outcomes, and Events4.2.1 Multiple Experiments; 4.2.2 Joint Experiments; 4.2.3 Joint Events and Marginalization; 4.3 Frequentist versus Subjective View of Uncertainty; 4.4 Summary; Further Reading; Chapter 5: The Basics of Probability; 5.1 Introduction; 5.2 Some Observations Leading to Axioms and Theorems of Probability; 5.3 Probability Distributions; 5.3.1 Probability Distributions with Infinite Outcomes; 5.3.2 Joint Probability Distributions and Probability of Marginalized Events; 5.3.3 Dealing with More than Two Variables; 5.4 Independent Events and Conditional Probability 5.5 Binomial Distribution5.6 Using Simple Probability Theory to Solve Earlier Problems and Explain Widespread Misunderstandings; 5.6.1 The Birthday Problem; 5.6.2 The Monty Hall Problem; 5.6.3 When Incredible Events Are Really Mundane; 5.6.4 When Mundane Events Really Are Quite Incredible; 5.7 Summary; Further Reading; Chapter 6: Bayes' Theorem and Conditional Probability; 6.1 Introduction; 6.2 All Probabilities Are Conditional; 6.3 Bayes' Theorem; 6.4 Using Bayes' Theorem to Debunk Some Probability Fallacies; 6.4.1 Traditional Statistical Hypothesis Testing

CSE