Risk assessment and decision analysis with Bayesian networks:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
CRC Press
2013
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIX, 503 S. graph. Darst. |
| ISBN: | 9781439809105 |
Internformat
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| 245 | 1 | 0 | |a Risk assessment and decision analysis with Bayesian networks |c Norman Fenton ; Martin Neil |
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Datensatz im Suchindex
| _version_ | 1804150008606359552 |
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| adam_text | Titel: Risk assessment and decision analysis with Bayesian networks
Autor: Fenton, Norman E
Jahr: 2013
Contents
Foreword.......................................................................................................................................................xi
Preface........................................................................................................................................................xiii
Acknowledgments......................................................................................................................................xvii
Authors........................................................................................................................................................xix
Chapter 1 There Is More to Assessing Risk Than Statistics...................................................................1
1.1 Introduction..................................................................................................................1
1.2 Predicting Economic Growth: The Normal Distribution and Its Limitations.............3
1.3 Patterns and Randomness: From School League Tables to Siegfried and Roy...........7
1.4 Dubious Relationships: Why You Should Be Very Wary of Correlations and
Their Significance Values..........................................................................................10
1.5 Spurious Correlations: How You Can Always Find a Silly Cause of Exam
Success.......................................................................................................................14
1.6 The Danger of Regression: Looking Back When You Need to Look Forward.........16
1.7 The Danger of Averages.............................................................................................18
1.7.1 What Type of Average?.................................................................................19
1.7.2 When Averages Alone Will Never Be Sufficient for Decision Making........20
1.8 When Simpson s Paradox Becomes More Worrisome...............................................21
1.9 Uncertain Information and Incomplete Information: Do Not Assume They Are
Different.....................................................................................................................23
1.10 Do Not Trust Anybody (Even Experts) to Properly Reason about Probabilities.......26
1.11 Chapter Summary......................................................................................................29
Further Reading....................................................................................................................29
Chapter 2 The Need for Causal, Explanatory Models in Risk Assessment..........................................31
2.1 Introduction................................................................................................................31
2.2 Are You More Likely to Die in an Automobile Crash When the Weather Is
Good Compared to Bad?............................................................................................31
2.3 When Ideology and Causation Collide.......................................................................35
2.4 The Limitations of Common Approaches to Risk Assessment.................................37
2.4.1 Measuring Armageddon and Other Risks....................................................37
2.4.2 Risks and Opportunities................................................................................39
2.4.3 Risk Registers and Heat Maps......................................................................40
2.5 Thinking about Risk Using Causal Analysis.............................................................42
2.6 Applying the Causal Framework to Armageddon......................................................46
2.7 Summary....................................................................................................................49
Further Reading....................................................................................................................49
Chapter 3 Measuring Uncertainty: The Inevitability of Subjectivity....................................................51
3.1 Introduction................................................................................................................51
3.2 Experiments, Outcomes, and Events..........................................................................52
3.2.1 Multiple Experiments....................................................................................56
3.2.2 Joint Experiments..........................................................................................57
3.2.3 Joint Events and Marginalization..................................................................58
3.3 Frequentist versus Subjective View of Uncertainty...................................................60
3.4 Summary....................................................................................................................67
Further Reading.....................................................................................................................68
Chapter 4 The Basics of Probability......................................................................................................69
4.1 Introduction................................................................................................................69
4.2 Some Observations Leading to Axioms and Theorems of Probability.....................69
4.3 Probability Distributions............................................................................................81
4.3.1 Probability Distributions with Infinite Outcomes.........................................83
4.3.2 Joint Probability Distributions and Probability of Marginalized Events......85
4.3.3 Dealing with More than Two Variables........................................................88
4.4 Independent Events and Conditional Probability.......................................................89
4.5 Binomial Distribution.................................................................................................96
4.6 Using Simple Probability Theory to Solve Earlier Problems and Explain
Widespread Misunderstandings...............................................................................101
4.6.1 The Birthday Problem.................................................................................101
4.6.2 The Monty Hall Problem............................................................................103
4.6.3 When Incredible Events Are Really Mundane...........................................105
4.6.4 When Mundane Events Really Are Quite Incredible.................................109
4.7 Summary..................................................................................................................110
Further Reading..................................................................................................................111
Chapter 5 Bayes Theorem and Conditional Probability.....................................................................113
5.1 Introduction..............................................................................................................113
5.2 All Probabilities Are Conditional............................................................................113
5.3 Bayes Theorem........................................................................................................116
5.4 Using Bayes Theorem to Debunk Some Probability Fallacies...............................121
5.4.1 Traditional Statistical Hypothesis Testing..................................................122
5.4.2 The Prosecutor Fallacy Revisited...............................................................124
5.4.3 The Defendant s Fallacy.............................................................................124
5.4.4 Odds Form of Bayes and the Likelihood Ratio..........................................125
5.5 Second-Order Probability........................................................................................127
5.6 Summary..................................................................................................................129
Further Reading..................................................................................................................129
Chapter 6 From Bayes Theorem to Bayesian Networks.....................................................................131
6.1 Introduction..............................................................................................................131
6.2 A Very Simple Risk Assessment Problem...............................................................132
6.3 Accounting for Multiple Causes (and Effects).........................................................134
6.4 Using Propagation to Make Special Types of Reasoning Possible..........................137
6.5 The Crucial Independence Assumptions.................................................................139
6.6 Structural Properties of BNs....................................................................................144
6.6.1 Serial Connection: Causal and Evidential Trails........................................144
6.6.2 Diverging Connection: Common Cause.....................................................147
6.6.3 Converging Connection: Common Effect...................................................149
6.6.4 Determining Whether Any Two Nodes in a BN Are Dependent...............151
6.7 Propagation in Bayesian Networks...........................................................................153
6.8 Using BNs to Explain Apparent Paradoxes..............................................................156
6.8.1 Revisiting the Monty Hall Problem............................................................156
6.8.1.1 Simple Solution............................................................................156
6.8.1.2 Complex Solution........................................................................157
6.8.2 Revisiting Simpson s Paradox.....................................................................161
6.9 Steps in Building and Running a BN Model............................................................162
6.9.1 Building a BN Model..................................................................................162
6.9.2 Running a BN Model..................................................................................166
6.9.3 Inconsistent Evidence..................................................................................168
6.10 Summary..................................................................................................................169
Further Reading..................................................................................................................169
Theoretical Underpinnings.......................................................................................169
BN Applications.......................................................................................................169
Nature and Theory of Causality...............................................................................170
Uncertain Evidence (Soft and Virtual).....................................................................170
Chapter 7 Defining the Structure of Bayesian Networks....................................................................171
7.1 Introduction..............................................................................................................171
7.2 Causal Inference and Choosing the Correct Edge Direction...................................172
7.3 The Idioms................................................................................................................174
7.3.1 The Cause-Consequence Idiom.................................................................175
7.3.2 Measurement Idiom....................................................................................177
7.3.3 Definitional/Synthesis Idiom.......................................................................184
7.3.3.1 Case 1: Definitional Relationship between Variables..................184
7.3.3.2 Case 2: Hierarchical Definitions.................................................184
7.3.3.3 Case 3: Combining Different Nodes Together to Reduce
Effects of Combinatorial Explosion ( Divorcing )........................185
7.3.4 Induction Idiom...........................................................................................188
7.4 The Problems of Asymmetry and How to Tackle Them.........................................190
7.4.1 Impossible Paths..........................................................................................190
7.4.2 Mutually Exclusive Paths............................................................................192
7.4.3 Distinct Causal Pathways............................................................................194
7.4.4 Taxonomic Classification............................................................................196
7.5 Multiobject Bayesian Network Models....................................................................202
7.6 The Missing Variable Fallacy..................................................................................207
7.7 Conclusions..............................................................................................................212
Further Reading..................................................................................................................213
Chapter 8 Building and Eliciting Node Probability Tables.................................................................215
8.1 Introduction..............................................................................................................215
8.2 Factorial Growth in the Size of Probability Tables..................................................215
8.3 Labeled Nodes and Comparative Expressions.........................................................217
8.4 Boolean Nodes and Functions..................................................................................221
8.4.1 The Asia Model...........................................................................................222
8.4.2 The OR Function for Boolean Nodes..........................................................227
8.4.3 The AND Function for Boolean Nodes......................................................234
8.4.4 M from N Operator......................................................................................235
8.4.5 NoisyOR Function for Boolean Nodes.......................................................236
8.4.6 Weighted Averages......................................................................................241
8.5 Ranked Nodes..........................................................................................................244
8.5.1 Background.................................................................................................244
8.5.2 Solution: Ranked Nodes with the TNormal Distribution...........................246
8.5.3 Alternative Weighted Functions for Ranked Nodes...................................252
8.5.4 Hints and Tips When Working with Ranked Nodes and NPTs..................255
8.5.4.1 Tip 1: Use the Weighted Functions as Far as Possible................255
8.5.4.2 Tip 2: Make Use of the Fact That a Ranked Node Parent Has
an Underlying Numerical Scale...................................................255
8.5.4.3 Tip 3: Do Not Forget the Importance of the Variance in the
TNormal Distribution..................................................................256
8.5.4.4 Tip 4: Change the Granularity of a Ranked Scale without
Having to Make Any Other Changes..........................................259
8.5.4.5 Tip 5: Do Not Create Large, Deep, Hierarchies Consisting of
Rank Nodes.................................................................................260
8.6 Elicitation.................................................................................................................260
8.6.1 Elicitation Protocols and Cognitive Biases.................................................260
8.6.2 Scoring Rules and Validation......................................................................263
8.6.3 Sensitivity Analysis.....................................................................................264
8.7 Summary..................................................................................................................265
Further Reading..................................................................................................................265
Chapter 9 Numeric Variables and Continuous Distribution Functions...............................................267
9.1 Introduction..............................................................................................................267
9.2 Some Theory on Functions and Continuous Distributions......................................268
9.3 Static Discretization.................................................................................................273
9.4 Dynamic Discretization...........................................................................................280
9.5 Using Dynamic Discretization.................................................................................283
9.5.1 Prediction Using Dynamic Discretization..................................................283
9.5.2 Conditioning on Discrete Evidence............................................................287
9.5.3 Parameter Learning (Induction) Using Dynamic Discretization................289
9.5.3.1 Classical versus Bayesian Modeling............................................289
9.5.3.2 Bayesian Hierarchical Model Using Beta-Binomial...................294
9.6 Avoiding Common Problems When Using Numeric Nodes....................................300
9.6.1 Unintentional Negative Values in a Node s State Range............................300
9.6.2 Potential Division by Zero..........................................................................301
9.6.3 Using Unbounded Distributions on a Bounded Range...............................301
9.6.4 Observations with Very Low Probability...................................................302
9.7 Summary..................................................................................................................303
Further Reading..................................................................................................................303
Chapter 10 Hypothesis Testing and Confidence Intervals.....................................................................305
10.1 Introduction..............................................................................................................305
10.2 Hypothesis Testing...................................................................................................305
10.2.1 Bayes Factors...............................................................................................306
10.2.2 Testing for Hypothetical Differences..........................................................308
10.2.3 Comparing Bayesian and Classical Hypothesis Testing.............................311
10.2.4 Model Comparison: Choosing the Best Predictive Model..........................315
10.2.5 Accommodating Expert Judgments about Hypotheses..............................322
10.2.6 Distribution Fitting as Hypothesis Testing..................................................325
10.2.7 Bayesian Model Comparison and Complex Causal Hypotheses................326
10.3 Confidence Intervals.................................................................................................333
10.3.1 The Fallacy of Frequentist Confidence Intervals........................................333
10.3.2 The Bayesian Alternative to Confidence Intervals.....................................337
10.4 Summary..................................................................................................................340
Further Reading..................................................................................................................341
Chapter 11 Modeling Operational Risk.................................................................................................343
11.1 Introduction..............................................................................................................343
11.2 The Swiss Cheese Model for Rare Catastrophic Events..........................................344
11.3 Bow Ties and Hazards..............................................................................................347
11.4 Fault Tree Analysis (FTA)........................................................................................348
11.5 Event Tree Analysis (ETA).......................................................................................354
11.6 Soft Systems, Causal Models, and Risk Arguments................................................357
11.7 KUUUB Factors.......................................................................................................362
11.8 Operational Risk in Finance....................................................................................364
11.8.1 Modeling the Operational Loss Generation Process..................................364
11.8.2 Scenarios and Stress Testing.......................................................................372
11.9 Summary..................................................................................................................375
Further Reading..................................................................................................................376
Chapter 12 Systems Reliability Modeling.............................................................................................377
12.1 Introduction..............................................................................................................377
12.2 Probability of Failure on Demand for Discrete Use Systems..................................378
12.3 Time to Failure for Continuous Use Systems...........................................................380
12.4 System Failure Diagnosis and Dynamic Bayesian Networks..................................383
12.5 Dynamic Fault Trees (DFTs)....................................................................................387
12.6 Software Defect Prediction......................................................................................395
12.7 Summary..................................................................................................................404
Further Reading..................................................................................................................404
Chapter 13 Bayes and the Law..............................................................................................................407
13.1 Introduction..............................................................................................................407
13.2 The Case for Bayesian Reasoning about Legal Evidence........................................408
13.3 Building Legal Arguments Using Idioms................................................................411
13.3.1 The Evidence Idiom....................................................................................411
13.3.2 The Evidence Accuracy Idiom....................................................................414
13.3.3 Idioms to Deal with the Key Notions of Motive and Opportunity ......417
13.3.4 Idiom for Modeling Dependency between Different Pieces of Evidence... 420
13.3.5 Alibi Evidence Idiom..................................................................................422
13.3.6 Explaining away Idiom...............................................................................425
13.4 Putting it All Together: Vole Example.....................................................................428
13.5 Using BNs to Expose Further Fallacies of Legal Reasoning...................................433
13.5.1 The Jury Observation Fallacy.....................................................................433
13.5.2 The Crimewatch UK Fallacy...................................................................435
13.6 Summary..................................................................................................................438
Further Reading...................................................................................................................438
Appendix A: The Basics of Counting.....................................................................................................441
Appendix B: The Algebra of Node Probability Tables.........................................................................449
Appendix C: Junction Tree Algorithm..................................................................................................455
Appendix D: Dynamic Discretization....................................................................................................465
Appendix E: Statistical Distributions....................................................................................................483
Index..........................................................................................................................................................495
|
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| oclc_num | 826288160 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-384 DE-M347 |
| owner_facet | DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-384 DE-M347 |
| physical | XIX, 503 S. graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Fenton, Norman E. 1956- Verfasser (DE-588)172073669 aut Risk assessment and decision analysis with Bayesian networks Norman Fenton ; Martin Neil Boca Raton, Fla. [u.a.] CRC Press 2013 XIX, 503 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Bayesian statistical decision theory Decision making Risk management Risikoanalyse (DE-588)4137042-9 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Entscheidungstheorie (DE-588)4138606-1 gnd rswk-swf Bayes-Netz (DE-588)4567228-3 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 s Bayes-Netz (DE-588)4567228-3 s Entscheidungstheorie (DE-588)4138606-1 s Risikoanalyse (DE-588)4137042-9 s 1\p DE-604 DE-604 Neil, Martin 19XX- Verfasser (DE-588)1029799776 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025684988&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Fenton, Norman E. 1956- Neil, Martin 19XX- Risk assessment and decision analysis with Bayesian networks Bayesian statistical decision theory Decision making Risk management Risikoanalyse (DE-588)4137042-9 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Entscheidungstheorie (DE-588)4138606-1 gnd Bayes-Netz (DE-588)4567228-3 gnd |
| subject_GND | (DE-588)4137042-9 (DE-588)4144220-9 (DE-588)4138606-1 (DE-588)4567228-3 |
| title | Risk assessment and decision analysis with Bayesian networks |
| title_auth | Risk assessment and decision analysis with Bayesian networks |
| title_exact_search | Risk assessment and decision analysis with Bayesian networks |
| title_full | Risk assessment and decision analysis with Bayesian networks Norman Fenton ; Martin Neil |
| title_fullStr | Risk assessment and decision analysis with Bayesian networks Norman Fenton ; Martin Neil |
| title_full_unstemmed | Risk assessment and decision analysis with Bayesian networks Norman Fenton ; Martin Neil |
| title_short | Risk assessment and decision analysis with Bayesian networks |
| title_sort | risk assessment and decision analysis with bayesian networks |
| topic | Bayesian statistical decision theory Decision making Risk management Risikoanalyse (DE-588)4137042-9 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Entscheidungstheorie (DE-588)4138606-1 gnd Bayes-Netz (DE-588)4567228-3 gnd |
| topic_facet | Bayesian statistical decision theory Decision making Risk management Risikoanalyse Bayes-Entscheidungstheorie Entscheidungstheorie Bayes-Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025684988&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT fentonnormane riskassessmentanddecisionanalysiswithbayesiannetworks AT neilmartin riskassessmentanddecisionanalysiswithbayesiannetworks |