Risk Assessment and Decision Analysis with Bayesian Networks

ISBN-10: 1439809100
ISBN-13: 9781439809105
Edition: 2013
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Book details

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: 2.002
Language: English

Queen Mary University of London, UK

There Is More to Assessing Risk Than Statistics
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
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
Further Reading
Measuring Uncertainty: The Inevitability of Subjectivity
Experiments, Outcomes, and Events
Multiple Experiments
Joint Experiments
Joint Events and Marginalization
Frequentist versus Subjective View of Uncertainty
Further Reading
The Basics of Probability
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
Further Reading
Bayes' Theorem and Conditional Probability
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
Further Reading
From Bayes' Theorem to Bayesian Networks
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
Further Reading
Theoretical Underpinnings
BN Applications
Nature and Theory of Causality
Uncertain Evidence (Soft and Virtual)
Defining the Structure of Bayesian Networks
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
Further Reading
Building and Eliciting Node Probability Tables
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
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 Protocols and Cognitive Biases
Scoring Rules and Validation
Sensitivity Analysis
Further Reading
Numeric Variables and Continuous Distribution Functions
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
Further Reading
Hypothesis Testing and Confidence Intervals
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
Further Reading
Modeling Operational Risk
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
Further Reading
Systems Reliability Modeling
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
Further Reading
Bayes and the Law
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
Further Reading
The Basics of Counting
The Algebra of Node Probability Tables
Junction Tree Algorithm
Dynamic Discretization
Statistical Distributions

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