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Table of Contents
Preface; Biography; Contents; 1 Introduction; 1.1 Is this War?; 1.2 Scenario Planning: Why, What, Where, How, When ... ; 1.3 Objectives and Typology; 1.4 Scenario Pre-requirements; 1.5 Scenarios, a Living Organism; 1.6 Risk Culture; References; 2 Environment; 2.1 The Risk Framework; 2.2 The Risk Taxonomy: A Base for Story Lines; 2.3 Risk Interactions and Contagion; 2.4 The Regulatory Framework; References; 3 The Information Set: Feeding the Scenarios; 3.1 Characterising Numeric Data; 3.1.1 Moments; 3.1.2 Quantiles; 3.1.3 Dependencies; 3.2 Data Sciences; 3.2.1 Data Mining.
3.2.2 Machine Learning and Artificial Intelligence3.2.3 Common Methodologies; References; 4 The Consensus Approach; 4.1 The Process; 4.2 In Practice; 4.2.1 Pre-workshop; 4.2.2 The Workshops; 4.3 For the Manager; 4.3.1 Sponsorship; 4.3.2 Buy-In; 4.3.3 Validation; 4.3.4 Sign-Offs; 4.4 Alternatives and Comparison; References; 5 Tilting Strategy: Using Probability Distribution Properties; 5.1 Theoretical Basis; 5.1.1 Distributions; 5.1.2 Risk Measures; 5.1.3 Fitting; 5.1.4 Goodness-of-Fit Tests; 5.2 Application; 5.3 For the Manager: Pros and Cons; 5.3.1 Implementation.
5.3.2 Distribution Selection5.3.3 Risk Measures; References; 6 Leveraging Extreme Value Theory; 6.1 Introduction; 6.2 The Extreme Value Framework; 6.2.1 Fisher-Tippett Theorem; 6.2.2 The GEV; 6.2.3 Building the Data Set; 6.2.4 How to Apply It?; 6.3 Summary of Results Obtained; 6.4 Conclusion; References; 7 Fault Trees and Variations; 7.1 Methodology; 7.2 In Practice; 7.2.1 Symbols; 7.2.2 Construction Steps; 7.2.3 Analysis; 7.2.4 For the Manager; 7.2.5 Calculations: An Example; 7.3 Alternatives; 7.3.1 Failure Mode and Effects Analysis; 7.3.2 Root Cause Analysis; 7.3.3 Why-Because Strategy.
7.3.4 Ishikawa's Fishbone Diagrams7.3.5 Fuzzy Logic; References; 8 Bayesian Networks; 8.1 Introduction; 8.2 Theory; 8.2.1 A Practical Focus on the Gaussian Case; 8.2.2 Moving Towards an Integrated System: Learning; 8.3 For the Managers; References; 9 Artificial Neural Network to Serve Scenario Analysis Purposes; 9.1 Origins; 9.2 In Theory; 9.3 Learning Algorithms; 9.4 Application; 9.5 For the Manager: Pros and Cons; References; 10 Forward-Looking Underlying Information: Working with Time Series; 10.1 Introduction; 10.2 Methodology; 10.2.1 Theoretical Aspects; 10.2.1.1 Stationary Process.
10.2.1.2 Autocorrelation10.2.1.3 White Noise; 10.2.1.4 Estimation; 10.2.1.5 Seasonality; 10.2.1.6 Trends; 10.2.2 The Models; 10.3 Application; References; 11 Dependencies and Relationships Between Variables; 11.1 Dependencies, Correlations and Copulas; 11.1.1 Correlations Measures; 11.1.2 Regression; 11.1.3 Copula; 11.2 For the Manager; References; Index.
3.2.2 Machine Learning and Artificial Intelligence3.2.3 Common Methodologies; References; 4 The Consensus Approach; 4.1 The Process; 4.2 In Practice; 4.2.1 Pre-workshop; 4.2.2 The Workshops; 4.3 For the Manager; 4.3.1 Sponsorship; 4.3.2 Buy-In; 4.3.3 Validation; 4.3.4 Sign-Offs; 4.4 Alternatives and Comparison; References; 5 Tilting Strategy: Using Probability Distribution Properties; 5.1 Theoretical Basis; 5.1.1 Distributions; 5.1.2 Risk Measures; 5.1.3 Fitting; 5.1.4 Goodness-of-Fit Tests; 5.2 Application; 5.3 For the Manager: Pros and Cons; 5.3.1 Implementation.
5.3.2 Distribution Selection5.3.3 Risk Measures; References; 6 Leveraging Extreme Value Theory; 6.1 Introduction; 6.2 The Extreme Value Framework; 6.2.1 Fisher-Tippett Theorem; 6.2.2 The GEV; 6.2.3 Building the Data Set; 6.2.4 How to Apply It?; 6.3 Summary of Results Obtained; 6.4 Conclusion; References; 7 Fault Trees and Variations; 7.1 Methodology; 7.2 In Practice; 7.2.1 Symbols; 7.2.2 Construction Steps; 7.2.3 Analysis; 7.2.4 For the Manager; 7.2.5 Calculations: An Example; 7.3 Alternatives; 7.3.1 Failure Mode and Effects Analysis; 7.3.2 Root Cause Analysis; 7.3.3 Why-Because Strategy.
7.3.4 Ishikawa's Fishbone Diagrams7.3.5 Fuzzy Logic; References; 8 Bayesian Networks; 8.1 Introduction; 8.2 Theory; 8.2.1 A Practical Focus on the Gaussian Case; 8.2.2 Moving Towards an Integrated System: Learning; 8.3 For the Managers; References; 9 Artificial Neural Network to Serve Scenario Analysis Purposes; 9.1 Origins; 9.2 In Theory; 9.3 Learning Algorithms; 9.4 Application; 9.5 For the Manager: Pros and Cons; References; 10 Forward-Looking Underlying Information: Working with Time Series; 10.1 Introduction; 10.2 Methodology; 10.2.1 Theoretical Aspects; 10.2.1.1 Stationary Process.
10.2.1.2 Autocorrelation10.2.1.3 White Noise; 10.2.1.4 Estimation; 10.2.1.5 Seasonality; 10.2.1.6 Trends; 10.2.2 The Models; 10.3 Application; References; 11 Dependencies and Relationships Between Variables; 11.1 Dependencies, Correlations and Copulas; 11.1.1 Correlations Measures; 11.1.2 Regression; 11.1.3 Copula; 11.2 For the Manager; References; Index.