David Rios Insua, Fabrizio Ruggeri, Michael P. Wiper
Imprint
Chichester, West Sussex : Wiley, 2012
Descript
xiii, 290 p. : ill. ; 24 cm
SUMMARY
"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
CONTENT
Stochastic processes -- Bayesian analysis -- Discrete time Markov chains and extensions -- Continuous time Markov chains and extensions -- Poisson processes and extensions -- Continuous time continuous space processes -- Queueing analysis -- Reliability -- Discrete event simulation -- Risk analysis
