LEADER 05736nam 2200793Ia 450 001 9910141318903321 005 20200520144314.0 010 $a1-280-58993-0 010 $a9786613619761 010 $a1-118-30403-9 010 $a0-470-97591-1 010 $a0-470-97592-X 035 $a(CKB)2670000000161800 035 $a(EBL)877779 035 $a(SSID)ssj0000831297 035 $a(PQKBManifestationID)12418726 035 $a(PQKBTitleCode)TC0000831297 035 $a(PQKBWorkID)10873095 035 $a(PQKB)10200283 035 $a(SSID)ssj0000661061 035 $a(PQKBManifestationID)11409502 035 $a(PQKBTitleCode)TC0000661061 035 $a(PQKBWorkID)10707515 035 $a(PQKB)10851825 035 $a(DLC) 2012004782 035 $a(Au-PeEL)EBL877779 035 $a(CaPaEBR)ebr10546528 035 $a(CaONFJC)MIL361976 035 $a(OCoLC)793103956 035 $a(CaSebORM)9781118304037 035 $a(MiAaPQ)EBC877779 035 $a(PPN)243652615 035 $a(OCoLC)841331213 035 $a(OCoLC)ocn841331213 035 $a(EXLCZ)992670000000161800 100 $a20120104d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian analysis of stochastic process models /$fDavid Rios Insua, Fabrizio Ruggeri, Michael P. Wiper 205 $a1st edition 210 $aHoboken, New Jersey $cWiley$d2012 215 $a1 online resource (316 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-470-74453-7 320 $aIncludes bibliographical references and index. 327 $aMachine 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 Gij?on 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. 330 $a"This book provides analysis of stochastic processes from a Bayesian perspective with coverage of the main classes of stochastic processing, including modeling, computational, inference, prediction, decision-making and important applied models based on stochastic processes. In offers an introduction of MCMC and other statistical computing machinery that have pushed forward advances in Bayesian methodology. Addressing the growing interest for Bayesian analysis of more complex models, based on stochastic processes, this book aims to unite scattered information into one comprehensive and reliable volume"--$cProvided by publisher. 330 $a"A unique book on Bayesian analyses of stochastic process based models"--$cProvided by publisher. 410 0$aWiley series in probability and statistics. 606 $aBayesian statistical decision theory 606 $aStochastic processes 615 0$aBayesian statistical decision theory. 615 0$aStochastic processes. 676 $a519.5/42 686 $aMAT029010$2bisacsh 700 $aRios Insua$b David$f1964-$0116831 701 $aWiper$b Michael P$0522178 701 $aRuggeri$b Fabrizio$0288618 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141318903321 996 $aBayesian analysis of stochastic process models$9835534 997 $aUNINA