Bayesian analysis of stochastic process models / David Rios Insua, Fabrizio Ruggeri, Michael P. Wiper.

Includes bibliographical references and index.

'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'-- Provided by publisher.

'A unique book on Bayesian analyses of stochastic process based models'-- Provided by publisher.