000			06381cam a2200685 i 4500
001			ocn775099472
003			OCoLC
005			20171224113825.0
006			m o d
007			cr |||||||||||
008			120202s2012 nju ob 001 0 eng
010			_a 2012004782
040			_aDLC _beng _erda _epn _cDLC _dN$T _dDG1 _dCUS _dOCLCF _dMERUC _dOCLCQ _dDEBBG
019			_a793103956 _a794822653
020			_a9780470975923 _q(Adobe PDF)
020			_a047097592X _q(Adobe PDF)
020			_a9781118304037 _q(ePub)
020			_a1118304039 _q(ePub)
020			_a9781118304204 _q(MobiPocket)
020			_a1118304209 _q(MobiPocket)
020			_a9780470975916 _q(electronic bk.)
020			_a0470975911 _q(electronic bk.)
020			_z9780470744536 _q(hardback)
029	1		_aAU@ _b000048545199
029	1		_aNZ1 _b15351430
029	1		_aDEBBG _bBV043394076
035			_a(OCoLC)775099472 _z(OCoLC)793103956 _z(OCoLC)794822653
037			_a10.1002/9780470975916 _bWiley InterScience _nhttp://www3.interscience.wiley.com
042			_apcc
050	0	0	_aQA279.5
072		7	_aMAT _x029010 _2bisacsh
082	0	0	_a519.5/42 _223
084			_aMAT029010 _2bisacsh
049			_aMAIN
100	1		_aRuggeri, Fabrizio.
245	1	0	_aBayesian analysis of stochastic process models / _cFabrizio Ruggeri, Michael P. Wiper, David Rios Insua.
260			_aHoboken, New Jersey : _bWiley, _c2012.
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
520			_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.
520			_a"A unique book on Bayesian analyses of stochastic process based models"-- _cProvided by publisher.
504			_aIncludes bibliographical references and index.
500			_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 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.
588	0		_aPrint version record and CIP data provided by publisher.
505	0		_aFront Matter -- Basic Concepts and Tools. Stochastic Processes -- Bayesian Analysis -- Models. Discrete Time Markov Chains and Extensions -- Continuous Time Markov Chains and Extensions -- Poisson Processes and Extensions -- Continuous Time Continuous Space Processes -- Applications. Queueing Analysis -- Reliability -- Discrete Event Simulation -- Risk Analysis -- Appendix A: Main Distributions -- Appendix B: Generating Functions and the Laplace₆Stieltjes Transform -- Index -- Wiley Series in Probability and Statistics.
650		0	_aBayesian statistical decision theory.
650		0	_aStochastic processes.
650		7	_aMATHEMATICS _xProbability & Statistics _xBayesian Analysis. _2bisacsh
650		7	_aBayesian statistical decision theory. _2fast _0(OCoLC)fst00829019
650		7	_aStochastic processes. _2fast _0(OCoLC)fst01133519
655		4	_aElectronic books.
700	1		_aWiper, Michael P.
700	1		_aRíos Insua, David, _d1964-
776	0	8	_iPrint version: _aRuggeri, Fabrizio. _tBayesian analysis of stochastic process models. _dHoboken, New Jersey : Wiley, 2012 _z9780470744536 _w(DLC) 2012000092
856	4	0	_uhttp://onlinelibrary.wiley.com/book/10.1002/9780470975916 _zWiley Online Library
938			_aEBSCOhost _bEBSC _n443018
994			_a92 _bDG1
999			_c11572 _d11572