{"product_id":"bayesian-analysis-of-stochastic-process-models-isbn-9780470744536","title":"Bayesian Analysis of Stochastic Process Models","description":"\u003cp\u003eBayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models.\u003c\/p\u003e \u003cp\u003eKey features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplores Bayesian analysis of models based on stochastic processes, providing a unified treatment. \u003c\/li\u003e \u003cli\u003eProvides a thorough introduction for research students. \u003c\/li\u003e \u003cli\u003eComputational tools to deal with complex problems are illustrated along with real life case studies \u003c\/li\u003e \u003cli\u003eLooks at inference, prediction and decision making. \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eResearchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.\u003c\/p\u003e  Preface  \u003cp\u003e\u003cb\u003ePART ONE BASIC CONCEPTS AND TOOLS\u003cbr\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Stochastic Processes 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 11\u003c\/p\u003e \u003cp\u003e1.2 Key Concepts in Stochastic Processes 11\u003c\/p\u003e \u003cp\u003e1.3 Main Classes of Stochastic Processes 16\u003c\/p\u003e \u003cp\u003e1.4 Inference, Prediction and Decision Making 21\u003c\/p\u003e \u003cp\u003e1.5 Discussion 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Bayesian Analysis 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 27\u003c\/p\u003e \u003cp\u003e2.2 Bayesian Statistics 28\u003c\/p\u003e \u003cp\u003e2.3 Bayesian Decision Analysis 37\u003c\/p\u003e \u003cp\u003e2.4 Bayesian Computation 39\u003c\/p\u003e \u003cp\u003e2.5 Discussion 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART TWO MODELS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Discrete Time Markov Chains 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 61\u003c\/p\u003e \u003cp\u003e3.2 Important Markov Chain Models 62\u003c\/p\u003e \u003cp\u003e3.3 Inference for First Order Chains 66\u003c\/p\u003e \u003cp\u003e3.4 Special Topics 76\u003c\/p\u003e \u003cp\u003e3.5 Case Study: Wind Directions at Gijon 87\u003c\/p\u003e \u003cp\u003e3.6 Markov Decision Processes 94\u003c\/p\u003e \u003cp\u003e3.7 Discussion 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Continuous Time Markov Chains and Extensions 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 105\u003c\/p\u003e \u003cp\u003e4.2 Basic Setup and Results 106\u003c\/p\u003e \u003cp\u003e4.3 Inference and Prediction for CTMCs 108\u003c\/p\u003e \u003cp\u003e4.4 Case Study: Hardware Availability through CTMCs 112\u003c\/p\u003e \u003cp\u003e4.5 Semi-Markovian Processes 118\u003c\/p\u003e \u003cp\u003e4.6 Decision Making with Semi-Markovian Decision Processes 122\u003c\/p\u003e \u003cp\u003e4.7 Discussion 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Poisson Processes and Extensions 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 133\u003c\/p\u003e \u003cp\u003e5.2 Basics on Poisson Processes 134\u003c\/p\u003e \u003cp\u003e5.3 Homogeneous Poisson Processes 138\u003c\/p\u003e \u003cp\u003e5.4 Nonhomogeneous Poisson Processes 147\u003c\/p\u003e \u003cp\u003e5.5 Compound Poisson Processes 153\u003c\/p\u003e \u003cp\u003e5.6 Further Extensions of Poisson Processes 154\u003c\/p\u003e \u003cp\u003e5.7 Case Study: Earthquake Occurrences 157\u003c\/p\u003e \u003cp\u003e5.8 Discussion 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Continuous Time Continuous Space Processes 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 169\u003c\/p\u003e \u003cp\u003e6.2 Gaussian Processes 170\u003c\/p\u003e \u003cp\u003e6.3 Brownian Motion and Fractional Brownian Motion 174\u003c\/p\u003e \u003cp\u003e6.4 Di®usions 181\u003c\/p\u003e \u003cp\u003e6.5 Case Study: Prey-predator Systems 184\u003c\/p\u003e \u003cp\u003e6.6 Discussion 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART THREE APPLICATIONS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Queueing Analysis 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 201\u003c\/p\u003e \u003cp\u003e7.2 Basic Queueing Concepts 201\u003c\/p\u003e \u003cp\u003e7.3 The Main Queueing Models 204\u003c\/p\u003e \u003cp\u003e7.4 Inference for Queueing Systems 208\u003c\/p\u003e \u003cp\u003e7.5 Inference for M=M=1 Systems 209\u003c\/p\u003e \u003cp\u003e7.6 Inference for Non Markovian Systems 220\u003c\/p\u003e \u003cp\u003e7.7 Decision Problems in Queueing Systems 229\u003c\/p\u003e \u003cp\u003e7.8 Case Study: Optimal Number of Beds in a Hospital 230\u003c\/p\u003e \u003cp\u003e7.9 Discussion 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Reliability 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 245\u003c\/p\u003e \u003cp\u003e8.2 Basic Reliability Concepts 246\u003c\/p\u003e \u003cp\u003e8.3 Renewal Processes 249\u003c\/p\u003e \u003cp\u003e8.4 Poisson Processes 251\u003c\/p\u003e \u003cp\u003e8.5 Other Processes 259\u003c\/p\u003e \u003cp\u003e8.6 Maintenance 262\u003c\/p\u003e \u003cp\u003e8.7 Case Study: Gas Escapes 263\u003c\/p\u003e \u003cp\u003e8.8 Discussion 271\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Discrete Event Simulation 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 279\u003c\/p\u003e \u003cp\u003e9.2 Discrete Event Simulation Methods 280\u003c\/p\u003e \u003cp\u003e9.3 A Bayesian View of DES 283\u003c\/p\u003e \u003cp\u003e9.4 Case Study: A G=G=1 Queueing System 286\u003c\/p\u003e \u003cp\u003e9.5 Bayesian Output Analysis 288\u003c\/p\u003e \u003cp\u003e9.6 Simulation and Optimization 292\u003c\/p\u003e \u003cp\u003e9.7 Discussion 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Risk Analysis 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 301\u003c\/p\u003e \u003cp\u003e10.2 Risk Measures 302\u003c\/p\u003e \u003cp\u003e10.3 Ruin Problems 316\u003c\/p\u003e \u003cp\u003e10.4 Case Study: Ruin Probability Estimation 320\u003c\/p\u003e \u003cp\u003e10.5 Discussion 327\u003c\/p\u003e \u003cp\u003eAppendix A Main Distributions 337\u003c\/p\u003e \u003cp\u003eAppendix B Generating Functions and the Laplace-Stieltjes Transform 347\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e  \u003cp\u003e\u003cstrong\u003eFabrizio Ruggeri\u003c\/strong\u003e, Research Director, CNR IMATI, Milano, Italy. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eMichael P. Wiper\u003c\/strong\u003e, Associate Professor in Statistics, Department of Statistics, Universidad Carlos III de Madrid, Spain. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eDavid Rios Insua\u003c\/strong\u003e, Professor of Statistics and Operations Research, Department of Statistics and Operations Research, Universidad Rey Juan Carlos, Spain.  Bayesian analysis of complex models based on stochastic processes has seen a surge in research activity in recent years. \u003ci\u003eBayesian Analysis of Stochastic Process Models\u003c\/i\u003e provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBayesian Analysis of Stochastic Process Models:\u003c\/i\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplores Bayesian analysis of models based on stochastic processes, providing a unified treatment. \u003c\/li\u003e \u003cli\u003eProvides a thorough introduction for research students.\u003c\/li\u003e \u003cli\u003eIncludes computational tools to deal with complex problems, illustrated with real life case studies\u003c\/li\u003e \u003cli\u003eComputational tools to deal with complex problems are illustrated along with real life case studies \u003c\/li\u003e \u003cli\u003eExamines inference, prediction and decision making. \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eResearchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988793901285,"sku":"NP9780470744536","price":129.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470744536.jpg?v=1761781613","url":"https:\/\/k12savings.com\/products\/bayesian-analysis-of-stochastic-process-models-isbn-9780470744536","provider":"K12savings","version":"1.0","type":"link"}