Title: Bayesian analysis of stochastic process models /
Authors: Ruggeri, Fabrizio.; Wiper, Michael P.; Ríos Insua, David, 1964-
Publisher: Wiley,
Year: 2012
Subjects: Bayesian statistical decision theory; Stochastic processes.; Stochastic Processes; Bayes Theorem; Data Interpretation, Statistical; MATHEMATICS / Probability & Statistics / Bayesian Analysis.
Abstract: "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"--
Contents: 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,^