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Bayesian analysis of stochastic proc...

Ríos Insua, David (1964-)

Bayesian analysis of stochastic process models

紀錄類型:	書目-語言資料,印刷品 : 單行本
作者:	Ríos InsuaDavid, 1964-
合作者:	RuggeriFabrizio,
合作者:	WiperMichael P,
出版地:	Chichester, West Sussex
出版者:	Wiley;
出版年:	2012
面頁冊數:	xiii, 290 p.ill. : 24 cm.;
集叢名:	Wiley series in probability and statistics
標題:	Bayesian statistical decision theory -
標題:	Stochastic processes -
附註:	Machine generated contents note: Preface 1 Stochastic Processes 11 1.1 Introduction 11 1.2 Key Concepts in Stochastic Processes 11 1.3 Main Classes of Stochastic Processes 16 1.4 Inference, Prediction and Decision Making 21 1.5 Discussion 23 2 Bayesian Analysis 27 2.1 Introduction 27 2.2 Bayesian Statistics 28 2.3 Bayesian Decision Analysis 37 2.4 Bayesian Computation 39 2.5 Discussion 51 3 Discrete Time Markov Chains 61 3.1 Introduction 61 3.2 Important Markov Chain Models 62 3.3 Inference for First Order, time homogeneous, Markov chains 66 3.4 Special Topics 76 3.5 Case Study: Wind Directions at Gij??n 87 3.6 Markov Decision Processes 94 3.7 Discussion 97 4 Continuous Time Markov Chains and Extensions 105 4.1 Introduction 105 4.2 Basic Setup and Results 106 4.3 Inference and Prediction for CTMCs 108 4.4 Case Study: Hardware Availability through CTMCs 112 4.5 Semi-Markovian Processes 118 4.6 Decision Making with Semi-Markovian Decision Processes 122 4.7 Discussion 128 5 Poisson Processes and Extensions 133 5.1 Introduction 133 5.2 Basics on Poisson Processes 134 5.3 Homogeneous Poisson Processes 138 5.4 Nonhomogeneous Poisson Processes 147 5.5 Compound Poisson Processes 153 5.6 Further Extensions of Poisson Processes 154 5.7 Case Study: Earthquake Occurrences 157 5.8 Discussion 162 6 Continuous Time Continuous Space Processes 169 6.1 Introduction 169 6.2 Gaussian Processes 170 6.3 Brownian Motion and FBM [Fractional Brownian Motion] 174 6.4 Diffusions 181 6.5 Case Study: Predator-prey Systems 184 6.6 Discussion 190 7 Queueing Analysis 201 7.1 Introduction 201 7.2 Basic Queueing Concepts 201 7.3 The Main Queueing Models 204 7.4 Bayesian inference for Queueing Systems 208 7.5 Inference for M/M/1 Systems 209 7.6 Inference for Non-Markovian Systems 220 7.7 Decision Problems in Queueing Systems 229 7.8 Case Study: Optimal Number of Beds in a Hospital 230 7.9 Discussion 235 8 Reliability 245 8.1 Introduction 245 8.2 Basic Reliability Concepts 246 8.3 Renewal Processes 249 8.4 Poisson Processes 251 8.5 Other Processes 259 8.6 Maintenance 262 8.7 Case Study: Gas Escapes 263 8.8 Discussion 271 279 9.1 Introduction 279 9.2 Discrete Event Simulation Methods 280 9.3 A Bayesian View of DES 283 9.4 Case Study: A G/G/1 Queueing System 286 9.5 Bayesian Output Analysis 288 9.6 Simulation and Optimization 292 9.7 Discussion 294 10 Risk Analysis 301 10.1 Introduction 301 10.2 Risk Measures 302 10.3 Ruin Problems 316 10.4 Case Study: Estimation of finite-time ruin probabilities in the Sparre Andersen model 320 10.5 Discussion 327 Appendix A Main Distributions 337 Appendix B Generating Functions and the Laplace-Stieltjes Transform 347 Index
摘要註:	"This book provides analysis of stochastic processes from a Bayesian perspective with coverage of the main classes of stochastic processing, including modeling, computational, inference, prediction, decision-making and important applied models based on stochastic processes. In offers an introduction of MCMC and other statistical computing machinery that have pushed forward advances in Bayesian methodology. Addressing the growing interest for Bayesian analysis of more complex models, based on stochastic processes, this book aims to unite scattered information into one comprehensive and reliable volume"--
ISBN:	9780470744536
內容註:	Stochastic processes -- Bayesian analysis -- Discrete time Markov chains and extensions -- Continuous time Markov chains and extensions -- Poisson processes and extensions -- Continuous time continuous space processes -- Queueing analysis -- Reliability -- Discrete event simulation -- Risk analysis

Bayesian analysis of stochastic process models
Ríos Insua, David

Bayesian analysis of stochastic process models / David Rios Insua, Fabrizio Ruggeri, Michael P. Wiper - Chichester, West Sussex : Wiley, 2012. - xiii, 290 p. ; ill. ; 24 cm.. - (Wiley series in probability and statistics).
Stochastic processes -- Bayesian analysis -- Discrete time Markov chains and extensions -- Continuous time Markov chains and extensions -- Poisson processes and extensions -- Continuous time continuous space processes -- Queueing analysis -- Reliability -- Discrete event simulation -- Risk analysis.
Machine generated contents note: Preface 1 Stochastic Processes 11 1.1 Introduction 11 1.2 Key Concepts in Stochastic Processes 11 1.3 Main Classes of Stochastic Processes 16 1.4 Inference, Prediction and Decision Making 21 1.5 Discussion 23 2 Bayesian Analysis 27 2.1 Introduction 27 2.2 Bayesian Statistics 28 2.3 Bayesian Decision Analysis 37 2.4 Bayesian Computation 39 2.5 Discussion 51 3 Discrete Time Markov Chains 61 3.1 Introduction 61 3.2 Important Markov Chain Models 62 3.3 Inference for First Order, time homogeneous, Markov chains 66 3.4 Special Topics 76 3.5 Case Study: Wind Directions at Gij??n 87 3.6 Markov Decision Processes 94 3.7 Discussion 97 4 Continuous Time Markov Chains and Extensions 105 4.1 Introduction 105 4.2 Basic Setup and Results 106 4.3 Inference and Prediction for CTMCs 108 4.4 Case Study: Hardware Availability through CTMCs 112 4.5 Semi-Markovian Processes 118 4.6 Decision Making with Semi-Markovian Decision Processes 122 4.7 Discussion 128 5 Poisson Processes and Extensions 133 5.1 Introduction 133 5.2 Basics on Poisson Processes 134 5.3 Homogeneous Poisson Processes 138 5.4 Nonhomogeneous Poisson Processes 147 5.5 Compound Poisson Processes 153 5.6 Further Extensions of Poisson Processes 154 5.7 Case Study: Earthquake Occurrences 157 5.8 Discussion 162 6 Continuous Time Continuous Space Processes 169 6.1 Introduction 169 6.2 Gaussian Processes 170 6.3 Brownian Motion and FBM [Fractional Brownian Motion] 174 6.4 Diffusions 181 6.5 Case Study: Predator-prey Systems 184 6.6 Discussion 190 7 Queueing Analysis 201 7.1 Introduction 201 7.2 Basic Queueing Concepts 201 7.3 The Main Queueing Models 204 7.4 Bayesian inference for Queueing Systems 208 7.5 Inference for M/M/1 Systems 209 7.6 Inference for Non-Markovian Systems 220 7.7 Decision Problems in Queueing Systems 229 7.8 Case Study: Optimal Number of Beds in a Hospital 230 7.9 Discussion 235 8 Reliability 245 8.1 Introduction 245 8.2 Basic Reliability Concepts 246 8.3 Renewal Processes 249 8.4 Poisson Processes 251 8.5 Other Processes 259 8.6 Maintenance 262 8.7 Case Study: Gas Escapes 263 8.8 Discussion 271 279 9.1 Introduction 279 9.2 Discrete Event Simulation Methods 280 9.3 A Bayesian View of DES 283 9.4 Case Study: A G/G/1 Queueing System 286 9.5 Bayesian Output Analysis 288 9.6 Simulation and Optimization 292 9.7 Discussion 294 10 Risk Analysis 301 10.1 Introduction 301 10.2 Risk Measures 302 10.3 Ruin Problems 316 10.4 Case Study: Estimation of finite-time ruin probabilities in the Sparre Andersen model 320 10.5 Discussion 327 Appendix A Main Distributions 337 Appendix B Generating Functions and the Laplace-Stieltjes Transform 347 Index.
Includes bibliographical references and index.
ISBN 9780470744536ISBN 0470744537ISBN 9780470975916ISBN 0470975911
Bayesian statistical decision theoryStochastic processes

Ruggeri, Fabrizio

Bayesian analysis of stochastic process models
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320 $a Includes bibliographical references and index
327 1 $a Stochastic processes -- Bayesian analysis -- Discrete time Markov chains and extensions -- Continuous time Markov chains and extensions -- Poisson processes and extensions -- Continuous time continuous space processes -- Queueing analysis -- Reliability -- Discrete event simulation -- Risk analysis
330 $a "This book provides analysis of stochastic processes from a Bayesian perspective with coverage of the main classes of stochastic processing, including modeling, computational, inference, prediction, decision-making and important applied models based on stochastic processes. In offers an introduction of MCMC and other statistical computing machinery that have pushed forward advances in Bayesian methodology. Addressing the growing interest for Bayesian analysis of more complex models, based on stochastic processes, this book aims to unite scattered information into one comprehensive and reliable volume"--
330 $a "A unique book on Bayesian analyses of stochastic process based models"--
410 0 $1 2001 $a Wiley series in probability and statistics
606 $a Bayesian statistical decision theory $2 lc $3 118539
606 $a Stochastic processes $2 lc $3 65253
676 $a 519.5/42 $v 23
680 $a QA279.5 $b R84 2012
700 1 $a Ríos Insua $b David $f 1964- $3 320060
701 1 $a Ruggeri $b Fabrizio $3 320061
701 1 $a Wiper $b Michael P $3 320062
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