Bayesian analysis of stochastic process models

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 andother 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. INDICE: Preface1 Stochastic Processes 111.1 Introduction 111.2 Key Concepts in Stochastic Processes 111.3 Main Classes of Stochastic Processes 161.4 Inference, Prediction and Decision Making 211.5 Discussion 232 Bayesian Analysis 272.1 Introduction 272.2 Bayesian Statistics 282.3 Bayesian Decision Analysis 372.4 Bayesian Computation 392.5 Discussion 513 Discrete Time Markov Chains 613.1 Introduction 613.2 Important Markov Chain Models 623.3 Inference for FirstOrder Chains 663.4 Special Topics 763.5 Case Study: Wind Directions at Gijon 873.6 Markov Decision Processes 943.7 Discussion 974 Continuous Time Markov Chains and Extensions 1054.1 Introduction 1054.2 Basic Setup and Results 1064.3 Inference and Prediction for CTMCs 1084.4 Case Study: Hardware Availability through CTMCs 1124.5 Semi-Markovian Processes 1184.6 Decision Making with Semi-Markovian Decision Processes 1224.7 Discussion 1285 Poisson Processes and Extensions 1335.1 Introduction 1335.2 Basics on Poisson Processes 1345.3 Homogeneous Poisson Processes 1385.4 Nonhomogeneous Poisson Processes 1475.5 Compound Poisson Processes 1535.6 Further Extensions of Poisson Processes 1545.7 Case Study: Earthquake Occurrences 1575.8 Discussion 1626 Continuous Time Continuous Space Processes 1696.1 Introduction 1696.2 Gaussian Processes 1706.3 Brownian Motion and Fractional Brownian Motion 1746.4 Diusions 1816.5 Case Study: Prey-predator Systems 1846.6 Discussion 1907 Queueing Analysis 2017.1 Introduction 2017.2 Basic Queueing Concepts 2017.3 The Main Queueing Models 2047.4 Inferencefor Queueing Systems 2087.5 Inference for M=M=1 Systems 2097.6 Inference for Non Markovian Systems 2207.7 Decision Problems in Queueing Systems 2297.8 CaseStudy: Optimal Number of Beds in a Hospital 2307.9 Discussion 2358 Reliability 2458.1 Introduction 2458.2 Basic Reliability Concepts 2468.3 Renewal Processes 2498.4 Poisson Processes 2518.5 Other Processes 2598.6 Maintenance 2628.7 Case Study: Gas Escapes 2638.8 Discussion 2719 Discrete Event Simulation 2799.1Introduction 2799.2 Discrete Event Simulation Methods 2809.3 A Bayesian View of DES 2839.4 Case Study: A G=G=1 Queueing System 2869.5 Bayesian Output Analysis 2889.6 Simulation and Optimization 2929.7 Discussion 29410 Risk Analysis 30110.1 Introduction 30110.2 Risk Measures 30210.3 Ruin Problems 31610.4 Case Study: Ruin Probability Estimation 32010.5 Discussion 327Appendix A Main Distributions 337Appendix B Generating Functions and the Laplace-Stieltjes Transform347Index

• ISBN: 978-0-470-74453-6
• Editorial: John Wiley & Sons
• Encuadernacion: Cartoné
• Páginas: 320
• Fecha Publicación: 20/04/2012
• Nº Volúmenes: 1
• Idioma: Inglés