Bayesian Analysis of Stochastic Process Models
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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.
Bayesian 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.
Key features:
Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment.
Provides a thorough introduction for research students.
Computational tools to deal with complex problems are illustrated along with real life case studies
Looks at inference, prediction and decision making.
Researchers, 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.
Key features:
Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment.
Provides a thorough introduction for research students.
Computational tools to deal with complex problems are illustrated along with real life case studies
Looks at inference, prediction and decision making.
Researchers, 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.
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