Coverart for item
The Resource Bayesian analysis of stochastic process models, David Rios Insua, Department of Statistics and Operations Research Universidad Rey Juan Carlos, Madrid, Spain, Fabrizio Ruggeri, CNR-IMATI, Milan, Italy, Michael P. Wiper, Department of Statistics, Universidad Carlos III de Madrid, Spain

Bayesian analysis of stochastic process models, David Rios Insua, Department of Statistics and Operations Research Universidad Rey Juan Carlos, Madrid, Spain, Fabrizio Ruggeri, CNR-IMATI, Milan, Italy, Michael P. Wiper, Department of Statistics, Universidad Carlos III de Madrid, Spain

Label
Bayesian analysis of stochastic process models
Title
Bayesian analysis of stochastic process models
Statement of responsibility
David Rios Insua, Department of Statistics and Operations Research Universidad Rey Juan Carlos, Madrid, Spain, Fabrizio Ruggeri, CNR-IMATI, Milan, Italy, Michael P. Wiper, Department of Statistics, Universidad Carlos III de Madrid, Spain
Creator
Contributor
Subject
Language
eng
Summary
  • "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"--
  • "A unique book on Bayesian analyses of stochastic process based models"--
Member of
Assigning source
  • Provided by publisher
  • Provided by publisher
Cataloging source
DLC
Dewey number
519.5/42
Illustrations
illustrations
Index
index present
LC call number
QA279.5
LC item number
.R56 2012
Literary form
non fiction
Nature of contents
bibliography
Series statement
Wiley series in probability and statistics
Label
Bayesian analysis of stochastic process models, David Rios Insua, Department of Statistics and Operations Research Universidad Rey Juan Carlos, Madrid, Spain, Fabrizio Ruggeri, CNR-IMATI, Milan, Italy, Michael P. Wiper, Department of Statistics, Universidad Carlos III de Madrid, Spain
Instantiates
Publication
Note
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 Chains 66 3.4 Special Topics 76 3.5 Case Study: Wind Directions at Gij́on 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 Fractional Brownian Motion 174 6.4 Dilusions 181 6.5 Case Study: Prey-predator 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 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 9 Discrete Event Simulation 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: Ruin Probability Estimation 320 10.5 Discussion 327 Appendix A Main Distributions 337 Appendix B Generating Functions and the Laplace-Stieltjes Transform 347 Index
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Dimensions
24 cm.
Extent
xiii, 290 pages
Isbn
9780470744536
Isbn Type
(hardback)
Lccn
2012000092
Media category
unmediated
Media MARC source
rdamedia
Other physical details
illustrations
System control number
  • (OCoLC)ocn767563919
  • UtOrBLW
  • (OCoLC)ocn795921141
Label
Bayesian analysis of stochastic process models, David Rios Insua, Department of Statistics and Operations Research Universidad Rey Juan Carlos, Madrid, Spain, Fabrizio Ruggeri, CNR-IMATI, Milan, Italy, Michael P. Wiper, Department of Statistics, Universidad Carlos III de Madrid, Spain
Publication
Note
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 Chains 66 3.4 Special Topics 76 3.5 Case Study: Wind Directions at Gij́on 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 Fractional Brownian Motion 174 6.4 Dilusions 181 6.5 Case Study: Prey-predator 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 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 9 Discrete Event Simulation 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: Ruin Probability Estimation 320 10.5 Discussion 327 Appendix A Main Distributions 337 Appendix B Generating Functions and the Laplace-Stieltjes Transform 347 Index
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Dimensions
24 cm.
Extent
xiii, 290 pages
Isbn
9780470744536
Isbn Type
(hardback)
Lccn
2012000092
Media category
unmediated
Media MARC source
rdamedia
Other physical details
illustrations
System control number
  • (OCoLC)ocn767563919
  • UtOrBLW
  • (OCoLC)ocn795921141

Library Locations

    • Harold B. Lee Library Brigham Young University, Provo, UT, 84602, US
      40.249156 -111.649242
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