Risk Assessment and Decision Analysis with Bayesian Networks

Name: Risk Assessment and Decision Analysis with Bayesian Networks
Price: 15.31 USD
Availability: InStock
ISBN: 9781439809105

ISBN-10: 1439809100

ISBN-13: 9781439809105

Edition: 2013

Authors: Norman Fenton, Martin Neil

List price: $90.00

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Book details

List price: $90.00
Copyright year: 2013
Publisher: Taylor & Francis Group
Publication date: 12/12/2012
Binding: Hardcover
Pages: 524
Size: 7.25" wide x 10.00" long x 1.00" tall
Weight: 2.332
Language: English

Queen Mary University of London, UK



Foreword


Preface


Acknowledgments


Authors



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



What Type of Average?



When Averages Alone Will Never Be Sufficient for Decision Making



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?



When Ideology and Causation Collide



The Limitations of Common Approaches to Risk Assessment



Measuring Armageddon and Other Risks



Risks and Opportunities



Risk Registers and Heat Maps



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



Multiple Experiments



Joint Experiments



Joint Events and Marginalization



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



Probability Distributions with Infinite Outcomes



Joint Probability Distributions and Probability of Marginalized Events



Dealing with More than Two Variables



Independent Events and Conditional Probability



Binomial Distribution



Using Simple Probability Theory to Solve Earlier Problems and Explain Widespread Misunderstandings



The Birthday Problem



The Monty Hall Problem



When Incredible Events Are Really Mundane



When Mundane Events Really Are Quite Incredible



Summary


Further Reading



Bayes' Theorem and Conditional Probability



Introduction



All Probabilities Are Conditional



Bayes' Theorem



Using Bayes' Theorem to Debunk Some Probability Fallacies



Traditional Statistical Hypothesis Testing



The Prosecutor Fallacy Revisited



The Defendant's Fallacy



Odds Form of Bayes and the Likelihood Ratio



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



Serial Connection: Causal and Evidential Trials



Diverging Connection: Common Cause



Converging Connection: Common Effect



Determining Whether Any Two Nodes in a BN Are Dependent



Propagation in Bayesian Networks



Using BNs to Explain Apparent Paradoxes



Revisiting the Monty Hall Problem



Simple Solution



Complex Solution



Revisiting Simpson's Paradox



Steps in Building and Running a BN Model



Building a BN Model



Running a BN Model



Inconsistent Evidence



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 Cause-Consequence Idiom



Measurement Idiom



Definitional/Synthesis Idiom



Case 1: Definitional Relationship between Variables



Case 2: Hierarchical Definitions



Case 3: Combining Different Nodes Together to Reduce Effects of Combinatorial Explosion ("Divorcing")



Induction Idiom



The Problems of Asymmetry and How to Tackle Them



Impossible Paths



Mutually Exclusive Paths



Distinct Causal Pathways



Taxonomic Classification



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



The Asia Model



The OR Function for Boolean Nodes



The AND Function for Boolean Nodes



M from N Operator



NoisyOR Function for Boolean Nodes



Weighted Averages



Ranked Nodes



Background



Solution: Ranked Nodes with the TNormal Distribution



Alternative Weighted Functions for Ranked Nodes



Hints and Tips When Working with Ranked Nodes and NPTs



Tip 1: Use the Weighted Functions as Far as Possible



Tip 2: Make Use of the Fact That a Ranked Node Parent Has an Underlying Numerical Scale



Tip 3: Do Not Forget the Importance of the Variance in the TNormal Distribution



Tip 4: Change the Granularity of a Ranked Scale without Having to Make Any Other Changes



Tip 5: Do Not Create Large, Deep, Hierarchies Consisting of Rank Nodes



Elicitation



Elicitation Protocols and Cognitive Biases



Scoring Rules and Validation



Sensitivity Analysis



Summary


Further Reading



Numeric Variables and Continuous Distribution Functions



Introduction



Some Theory on Functions and Continuous Distributions



Static Discretization



Dynamic Discretization



Using Dynamic Discretization



Prediction Using Dynamic Discretization



Conditioning on Discrete Evidence



Parameter Learning (Induction) Using Dynamic Discretization



Classical versus Bayesian Modeling



Bayesian Hierarchical Model Using Beta-Binomial



Avoiding Common Problems When Using Numeric Nodes



Unintentional Negative Values in a Node's State Range



Potential Division by Zero



Using Unbounded Distributions on a Bounded Range



Observations with Very Low Probability



Summary


Further Reading



Hypothesis Testing and Confidence Intervals



Introduction



Hypothesis Testing



Bayes Factors



Testing for Hypothetical Differences



Comparing Bayesian and Classical Hypothesis Testing



Model Comparison: Choosing the Best Predictive Model



Accommodating Expert Judgments about Hypotheses



Distribution Fitting as Hypothesis Testing



Bayesian Model Comparison and Complex Causal Hypotheses



Confidence Intervals



The Fallacy of Frequentist Confident Intervals



The Bayesian Alternative to 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



Modeling the Operational Loss Generation Process



Scenarios and Stress Testing



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



Explaining away Idiom



Putting it All Together: Vole Example



Using BNs to Expose Further Fallacies of Legal Reasoning



The Jury Observation Fallacy



The "Crimewatch UK" Fallacy



Summary


Further Reading



The Basics of Counting



The Algebra of Node Probability Tables



Junction Tree Algorithm



Dynamic Discretization



Statistical Distributions


Index