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Biblioteca

MARC Lista (tabellare)
Learning in the Absence of Training Data [[electronic resource] /] / by Dalia Chakrabarty
Learning in the Absence of Training Data [[electronic resource] /] / by Dalia Chakrabarty
Autore Chakrabarty Dalia
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (241 pages)
Disciplina 006.31015195
Soggetto topico Statistics
Data mining
Probabilities
Statistical Theory and Methods
Bayesian Inference
Data Mining and Knowledge Discovery
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Probability Theory
Aprenentatge automàtic
Mètodes estadístics
Estadística bayesiana
Soggetto genere / forma Llibres electrònics
ISBN 3-031-31011-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1 Bespoke Learning to generate originally-absent training data -- 2 Forecasting by Learning Evolution-Driver - Application to Forecasting New COVID19 Infections -- 3 Potential to Density - Application to Learning Galactic Gravitational Mass Density -- 4 Bespoke Learning in Static Systems - Application to Learning Sub-surface Material Density Function -- 5 Bespoke Learning of Output using Inter-Network Distance - Application to Haematology-Oncology -- A Bayesian inference by posterior sampling using MCMC.
Record Nr. UNINA-9910734827103321
Chakrabarty Dalia  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Multi-level Bayesian models for environment perception / / Csaba Benedek
Multi-level Bayesian models for environment perception / / Csaba Benedek
Autore Benedek Csaba
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (208 pages)
Disciplina 006.4
Soggetto topico Markov processes
Bayesian statistical decision theory
Reconeixement de formes (Informàtica)
Visió per ordinador
Models matemàtics
Processos de Markov
Estadística bayesiana
Soggetto genere / forma Llibres electrònics
ISBN 9783030836542
9783030836535
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Acknowledgements -- Contents -- Acronyms and Notations -- Abbreviations and Concepts -- General Notations Used in the Book -- Specific Notations Used in MRF/CXM Models -- Specific Notations Used in MPP Models -- 1 Introduction -- 2 Fundamentals -- 2.1 Measurement Representation and Problem Formulations -- 2.2 Markovian Classification Models -- 2.2.1 Markov Random Fields, Gibbs Potentials, and Observation Processes -- 2.2.2 Bayesian Labeling Approach and the Potts Model -- 2.2.3 MRF-Based Image Segmentation -- 2.2.4 MRF Optimization -- 2.2.5 Mixed Markov Models -- 2.3 Object Population Extraction with Marked Point Processes -- 2.3.1 Definition of Marked Point Processes -- 2.3.2 MPP Energy Functions -- 2.3.3 MPP Optimization -- 2.4 Methodological Contributions of the Book -- 3 Bayesian Models for Dynamic Scene Analysis -- 3.1 Dynamic Scene Perception -- 3.2 Foreground Extraction in Video Sequences -- 3.2.1 Related Work in Video-Based Foreground Detection -- 3.2.2 MRF Model for Foreground Extraction -- 3.2.3 Probabilistic Model of the Background and Shadow Processes -- 3.2.4 Microstructural Features -- 3.2.5 Foreground Probabilities -- 3.2.6 Parameter Settings -- 3.2.7 MRF Optimization -- 3.2.8 Results -- 3.2.9 Summary and Applications of Foreground Segmentation -- 3.3 People Localization in Multi-camera Systems -- 3.3.1 A New Approach on Multi-view People Localization -- 3.3.2 Silhouette-Based Feature Extraction -- 3.3.3 3D Marked Point Process Model -- 3.3.4 Evaluation of Multi-camera People Localization -- 3.3.5 Applications and Alternative Ways of 3D Person Localization -- 3.4 Foreground Extraction in Lidar Point Cloud Sequences -- 3.4.1 Problem Formulation and Data Mapping -- 3.4.2 Background Model -- 3.4.3 DMRF Approach on Foreground Segmentation -- 3.4.4 Evaluation of DMRF-Based Foreground-Background Separation.
3.4.5 Application of the DMFR Method for Person and Activity Recognition -- 3.5 Conclusions -- 4 Multi-layer Label Fusion Models -- 4.1 Markovian Fusion Models in Computer Vision -- 4.2 A Label Fusion Model for Object Motion Detection -- 4.2.1 2D Image Registration -- 4.2.2 Change Detection with 3D Approach -- 4.2.3 Feature Selection -- 4.2.4 Multi-layer Segmentation Model -- 4.2.5 L3Mrf Optimization -- 4.2.6 Experiments on Object Motion Detection -- 4.3 Long-Term Change Detection in Aerial Photos -- 4.3.1 Image Model and Feature Extraction -- 4.3.2 A Conditional Mixed Markov Image Segmentation Model -- 4.3.3 Experiments on Long-Term Change Detection -- 4.4 Parameter Settings in Multi-layer Segmentation Models -- 4.5 Conclusions -- 5 Multitemporal Data Analysis with Marked Point Processes -- 5.1 Introducing the Time Dimension in MPP Models -- 5.2 Object-Level Change Detection -- 5.2.1 Building Development Monitoring-Problem Definition -- 5.2.2 Feature Selection -- 5.2.3 Multitemporal MPP Configuration Model and Optimization -- 5.2.4 Experimental Study of the mMPP Model -- 5.3 A Point Process Model for Target Sequence Analysis -- 5.3.1 Application on Moving Target Analysis in ISAR Image Sequences -- 5.3.2 Problem Definition and Notations -- 5.3.3 Data Preprocessing in a Bottom-Up Approach -- 5.3.4 Multiframe Marked Point Process Model -- 5.3.5 Multiframe MPP Optimization -- 5.3.6 Experimental Results on Target Sequence Analysis -- 5.4 Parameter Settings in Dynamic MPP Models -- 5.5 Conclusions -- 6 Multi-level Object Population Analysis with an Embedded MPP Model -- 6.1 A Hierarchical MPP Approach -- 6.2 Problem Formulation and Notations -- 6.3 EMPP Energy Model -- 6.4 Multi-level MPP Optimization -- 6.5 Applications of the EMPP Model -- 6.5.1 Built-in Area Analysis in Aerial and Satellite Images -- 6.5.2 Traffic Monitoring-Based on Lidar Data.
6.5.3 Automatic Optical Inspection of Printed Circuit Boards -- 6.6 Implementation Details -- 6.7 Quantitative Evaluation Framework -- 6.7.1 EMPP Benchmark Database -- 6.7.2 Quantitative Evaluation Methodology -- 6.8 Experimental Results -- 6.8.1 EMPP Versus an Ensemble of Single Layer MPPs -- 6.8.2 Application Level Comparison to Non-MPP-Based Techniques -- 6.8.3 Effects on Data Term Parameter Settings -- 6.8.4 Computational Time -- 6.8.5 Experiment Repeatability -- 6.9 Conclusion -- 7 Concluding Remarks -- Appendix References -- -- Index.
Record Nr. UNISA-996472038103316
Benedek Csaba  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Multi-level Bayesian models for environment perception / / Csaba Benedek
Multi-level Bayesian models for environment perception / / Csaba Benedek
Autore Benedek Csaba
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (208 pages)
Disciplina 006.4
Soggetto topico Markov processes
Bayesian statistical decision theory
Reconeixement de formes (Informàtica)
Visió per ordinador
Models matemàtics
Processos de Markov
Estadística bayesiana
Soggetto genere / forma Llibres electrònics
ISBN 9783030836542
9783030836535
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Acknowledgements -- Contents -- Acronyms and Notations -- Abbreviations and Concepts -- General Notations Used in the Book -- Specific Notations Used in MRF/CXM Models -- Specific Notations Used in MPP Models -- 1 Introduction -- 2 Fundamentals -- 2.1 Measurement Representation and Problem Formulations -- 2.2 Markovian Classification Models -- 2.2.1 Markov Random Fields, Gibbs Potentials, and Observation Processes -- 2.2.2 Bayesian Labeling Approach and the Potts Model -- 2.2.3 MRF-Based Image Segmentation -- 2.2.4 MRF Optimization -- 2.2.5 Mixed Markov Models -- 2.3 Object Population Extraction with Marked Point Processes -- 2.3.1 Definition of Marked Point Processes -- 2.3.2 MPP Energy Functions -- 2.3.3 MPP Optimization -- 2.4 Methodological Contributions of the Book -- 3 Bayesian Models for Dynamic Scene Analysis -- 3.1 Dynamic Scene Perception -- 3.2 Foreground Extraction in Video Sequences -- 3.2.1 Related Work in Video-Based Foreground Detection -- 3.2.2 MRF Model for Foreground Extraction -- 3.2.3 Probabilistic Model of the Background and Shadow Processes -- 3.2.4 Microstructural Features -- 3.2.5 Foreground Probabilities -- 3.2.6 Parameter Settings -- 3.2.7 MRF Optimization -- 3.2.8 Results -- 3.2.9 Summary and Applications of Foreground Segmentation -- 3.3 People Localization in Multi-camera Systems -- 3.3.1 A New Approach on Multi-view People Localization -- 3.3.2 Silhouette-Based Feature Extraction -- 3.3.3 3D Marked Point Process Model -- 3.3.4 Evaluation of Multi-camera People Localization -- 3.3.5 Applications and Alternative Ways of 3D Person Localization -- 3.4 Foreground Extraction in Lidar Point Cloud Sequences -- 3.4.1 Problem Formulation and Data Mapping -- 3.4.2 Background Model -- 3.4.3 DMRF Approach on Foreground Segmentation -- 3.4.4 Evaluation of DMRF-Based Foreground-Background Separation.
3.4.5 Application of the DMFR Method for Person and Activity Recognition -- 3.5 Conclusions -- 4 Multi-layer Label Fusion Models -- 4.1 Markovian Fusion Models in Computer Vision -- 4.2 A Label Fusion Model for Object Motion Detection -- 4.2.1 2D Image Registration -- 4.2.2 Change Detection with 3D Approach -- 4.2.3 Feature Selection -- 4.2.4 Multi-layer Segmentation Model -- 4.2.5 L3Mrf Optimization -- 4.2.6 Experiments on Object Motion Detection -- 4.3 Long-Term Change Detection in Aerial Photos -- 4.3.1 Image Model and Feature Extraction -- 4.3.2 A Conditional Mixed Markov Image Segmentation Model -- 4.3.3 Experiments on Long-Term Change Detection -- 4.4 Parameter Settings in Multi-layer Segmentation Models -- 4.5 Conclusions -- 5 Multitemporal Data Analysis with Marked Point Processes -- 5.1 Introducing the Time Dimension in MPP Models -- 5.2 Object-Level Change Detection -- 5.2.1 Building Development Monitoring-Problem Definition -- 5.2.2 Feature Selection -- 5.2.3 Multitemporal MPP Configuration Model and Optimization -- 5.2.4 Experimental Study of the mMPP Model -- 5.3 A Point Process Model for Target Sequence Analysis -- 5.3.1 Application on Moving Target Analysis in ISAR Image Sequences -- 5.3.2 Problem Definition and Notations -- 5.3.3 Data Preprocessing in a Bottom-Up Approach -- 5.3.4 Multiframe Marked Point Process Model -- 5.3.5 Multiframe MPP Optimization -- 5.3.6 Experimental Results on Target Sequence Analysis -- 5.4 Parameter Settings in Dynamic MPP Models -- 5.5 Conclusions -- 6 Multi-level Object Population Analysis with an Embedded MPP Model -- 6.1 A Hierarchical MPP Approach -- 6.2 Problem Formulation and Notations -- 6.3 EMPP Energy Model -- 6.4 Multi-level MPP Optimization -- 6.5 Applications of the EMPP Model -- 6.5.1 Built-in Area Analysis in Aerial and Satellite Images -- 6.5.2 Traffic Monitoring-Based on Lidar Data.
6.5.3 Automatic Optical Inspection of Printed Circuit Boards -- 6.6 Implementation Details -- 6.7 Quantitative Evaluation Framework -- 6.7.1 EMPP Benchmark Database -- 6.7.2 Quantitative Evaluation Methodology -- 6.8 Experimental Results -- 6.8.1 EMPP Versus an Ensemble of Single Layer MPPs -- 6.8.2 Application Level Comparison to Non-MPP-Based Techniques -- 6.8.3 Effects on Data Term Parameter Settings -- 6.8.4 Computational Time -- 6.8.5 Experiment Repeatability -- 6.9 Conclusion -- 7 Concluding Remarks -- Appendix References -- -- Index.
Record Nr. UNINA-9910564689303321
Benedek Csaba  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
New frontiers in Bayesian Statistics : Baysm 2021, online, September 1-3 / / edited by Raffaele Argiento, Federico Camerlenghi, and Sally Paganin
New frontiers in Bayesian Statistics : Baysm 2021, online, September 1-3 / / edited by Raffaele Argiento, Federico Camerlenghi, and Sally Paganin
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (122 pages)
Disciplina 519.542
Collana Springer Proceedings in Mathematics and Statistics
Soggetto topico Bayesian statistical decision theory
Estadística bayesiana
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-031-16427-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996499868203316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
New frontiers in Bayesian Statistics : Baysm 2021, online, September 1-3 / / edited by Raffaele Argiento, Federico Camerlenghi, and Sally Paganin
New frontiers in Bayesian Statistics : Baysm 2021, online, September 1-3 / / edited by Raffaele Argiento, Federico Camerlenghi, and Sally Paganin
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (122 pages)
Disciplina 519.542
Collana Springer Proceedings in Mathematics and Statistics
Soggetto topico Bayesian statistical decision theory
Estadística bayesiana
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-031-16427-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910632477903321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Probabilistic risk analysis and Bayesian decision theory / / Marcel van Oijen, Mark Brewer
Probabilistic risk analysis and Bayesian decision theory / / Marcel van Oijen, Mark Brewer
Autore van Oijen Marcel
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (118 pages)
Disciplina 810
Collana SpringerBriefs in Statistics
Soggetto topico Probabilities
Estadística bayesiana
Probabilitats
Avaluació del risc
Soggetto genere / forma Llibres electrònics
ISBN 3-031-16333-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
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.
Record Nr. UNISA-996499869203316
van Oijen Marcel  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Probabilistic risk analysis and Bayesian decision theory / / Marcel van Oijen, Mark Brewer
Probabilistic risk analysis and Bayesian decision theory / / Marcel van Oijen, Mark Brewer
Autore van Oijen Marcel
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (118 pages)
Disciplina 810
Collana SpringerBriefs in Statistics
Soggetto topico Probabilities
Estadística bayesiana
Probabilitats
Avaluació del risc
Soggetto genere / forma Llibres electrònics
ISBN 3-031-16333-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
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.
Record Nr. UNINA-9910632480803321
van Oijen Marcel  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Test Data Engineering [[electronic resource] ] : Latent Rank Analysis, Biclustering, and Bayesian Network / / by Kojiro Shojima
Test Data Engineering [[electronic resource] ] : Latent Rank Analysis, Biclustering, and Bayesian Network / / by Kojiro Shojima
Autore Shojima Kojiro
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (xxii, 579 pages) : illustrations
Disciplina 519.542
Collana Behaviormetrics: Quantitative Approaches to Human Behavior
Soggetto topico Social sciences - Statistical methods
Statistics
Political planning
Psychometrics
Machine learning
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
Statistical Theory and Methods
Public Policy
Machine Learning
Estadística bayesiana
Anàlisi de conglomerats
Mineria de dades
Tests i proves en educació
Processament de dades
Visualització de la informació
Soggetto genere / forma Llibres electrònics
ISBN 9789811699863
9789811699856
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Concept of Test Data Engineering -- Test Data and Item Analysis -- Classical Test Theory -- Item Response Theory -- Latent Class Analysis -- Biclustering -- Bayesian Network Model.
Record Nr. UNISA-996485661803316
Shojima Kojiro  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Test Data Engineering [[electronic resource] ] : Latent Rank Analysis, Biclustering, and Bayesian Network / / by Kojiro Shojima
Test Data Engineering [[electronic resource] ] : Latent Rank Analysis, Biclustering, and Bayesian Network / / by Kojiro Shojima
Autore Shojima Kojiro
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (xxii, 579 pages) : illustrations
Disciplina 519.542
Collana Behaviormetrics: Quantitative Approaches to Human Behavior
Soggetto topico Social sciences - Statistical methods
Statistics
Political planning
Psychometrics
Machine learning
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
Statistical Theory and Methods
Public Policy
Machine Learning
Estadística bayesiana
Anàlisi de conglomerats
Mineria de dades
Tests i proves en educació
Processament de dades
Visualització de la informació
Soggetto genere / forma Llibres electrònics
ISBN 9789811699863
9789811699856
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Concept of Test Data Engineering -- Test Data and Item Analysis -- Classical Test Theory -- Item Response Theory -- Latent Class Analysis -- Biclustering -- Bayesian Network Model.
Record Nr. UNINA-9910586633203321
Shojima Kojiro  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui