Probabilistic risk analysis and Bayesian decision theory / / Marcel van Oijen, Mark Brewer

• Autore: van Oijen Marcel
• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (118 pages)
• Disciplina: 810
• Soggetto topico: Probabilities; Estadística bayesiana; Probabilitats; Avaluació del risc
• Soggetto genere / forma: Llibres electrònics
• Persona (resp. second.): BrewerMark
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Why This Book? -- Who Is this Book for? -- Notation -- Outline of Chapters -- Acknowledgements -- Contents -- 1 Introduction to Probabilistic Risk Analysis (PRA) -- 1.1 From Risk Matrices to PRA -- 1.2 Basic Equations for PRA -- 1.3 Decomposition of Risk: 2 or 3 Components -- 1.4 Resolution of PRA: Single-Threshold, Multi-Threshold, Categorical, Continuous -- 1.4.1 Single-Threshold PRA -- 1.4.2 Multi-Threshold PRA -- 1.4.3 Categorical PRA -- 1.4.4 Continuous PRA -- 1.5 Implementation of PRA: Distribution-Based, Sampling-Based, Model-Based -- 2 Distribution-Based Single-Threshold PRA -- 2.1 Conditional Distributions for z -- 2.1.1 Conditions for V Being Constant -- 2.2 Example of Distribution-Based PRA: Gaussian p[x,z] -- 2.2.1 Hazard Probability and Conditional Distributions -- 2.2.2 Conditional Expectations and PRA -- 2.3 Approximation Formulas for the Conditional Bivariate Gaussian Expectations -- 3 Sampling-Based Single-Threshold PRA -- 3.1 Example of Sampling-Based PRA: Linear Relationship -- 3.1.1 Varying the Threshold -- 3.2 Example of Sampling-Based PRA: Nonlinear Relationship -- 4 Sampling-Based Single-Threshold PRA: Uncertainty Quantification (UQ) -- 4.1 Uncertainty in p[H] -- 4.2 Uncertainty in V -- 4.3 Uncertainty in R -- 4.4 Extension of R-Code for PRA: Adding the UQ -- 4.5 PRA with UQ on the Nonlinear Data Set -- 4.6 Verification of the UQ by Simulating Multiple Data Sets -- 4.6.1 UQ-Verification: Nonlinear Relationship -- 4.6.2 UQ-Verification: Linear Relationship -- 4.7 Approximation Formulas for the Conditional Bivariate Gaussian Variances -- 5 Density Estimation to Move from Sampling- to Distribution-Based PRA -- 6 Copulas for Distribution-Based PRA -- 6.1 Sampling from Copulas and Carrying out PRA -- 6.2 Copula Selection -- 6.3 Using Copulas in PRA -- 7 Bayesian Model-Based PRA. ; 7.1 Linear Example: Full Bayesian PRA with Uncertainty -- 7.1.1 Checking the MCMC -- 7.1.2 PRA -- 7.2 Nonlinear Example: Full Bayesian PRA with Uncertainty -- 7.3 Advantages of the Bayesian Modelling Approach -- 8 Sampling-Based Multi-Threshold PRA:Gaussian Linear Example -- 9 Distribution-Based Continuous PRA: Gaussian Linear Example -- 10 Categorical PRA with Other Splits than for Threshold-Levels: Spatio-Temporal Example -- 10.1 Spatio-Temporal Environmental Data: x(s,t) -- 10.2 Spatio-Temporal System Data: z(s,t) -- 10.3 Single-Category Single-Threshold PRA for the Spatio-Temporal Data -- 10.4 Two-Category Single-Threshold PRA for Spatio-Temporal Data -- 11 Three-Component PRA -- 11.1 Three-Component PRA for Spatio-Temporal Data -- 11.2 Country-Wide Application of Three-Component PRA -- 11.3 UQ for Three-Component PRA -- 12 Introduction to Bayesian Decision Theory (BDT) -- 12.1 Example of BDT in Action -- 13 Implementation of BDT Using Bayesian Networks -- 13.1 Three Ways to Specify a Multivariate Gaussian -- 13.1.1 Switching Between the Three Different Specifications of the Multivariate Gaussian -- 13.2 Sampling from a GBN and Bayesian Updating -- 13.2.1 Updating a GBN When Information About Nodes Becomes Available -- 13.3 A Linear BDT Example Implemented as a GBN -- 13.4 A Linear BDT Example Implemented Using \texttt{Nimble} -- 13.4.1 Varying IRRIG to Identify the Value for Which E[U] Is Maximized -- 13.5 A Nonlinear BDT Example Implemented Using \texttt{Nimble} -- 14 A Spatial Example: Forestry in Scotland -- 14.1 A Decision Problem: Forest Irrigation in Scotland -- 14.2 Computational Demand of BDT and Emulation -- 14.3 Data -- 14.4 A Simple Model for Forest Yield Class (YC) -- 14.5 Emulation -- 14.6 Application of the Emulator -- 15 Spatial BDT Using Model and Emulator -- 15.1 Multiple Action Levels -- 16 Linkages Between PRA and BDT. ; 16.1 Risk Management -- 16.2 The Relationship Between Utility Maximisation in BDT and Risk Assessment in PRA: R_c -- 16.3 Simplified Accounting for Both Benefits and Costs of the Action: R_b -- 16.4 Only Correcting for Costs: R_a -- 17 PRA vs. BDT in the Spatial Example -- 18 Three-Component PRA in the Spatial Example -- 19 Discussion -- 19.1 PRA and Its Application -- 19.2 Data and Computational Demand of PRA -- 19.3 BDT -- 19.4 Computational Demand of BDT -- 19.5 PRA as a Tool for Simplifying and Elucidating BDT -- 19.6 Parameter and Model Uncertainties -- 19.7 Modelling and Decision-Support for Forest Response to Hazards -- 19.8 Spatial Statistics -- References -- Index.
• Titolo autorizzato: Probabilistic risk analysis and Bayesian decision theory
• ISBN: 3-031-16333-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910632480803321
• Lo trovi qui: Univ. Federico II
• Serie: SpringerBriefs in Statistics