Bayesian Analysis of Stochastic Process Models

Bayesian Analysis of Stochastic Process Models

Author(s): David Rios Insua, Fabrizio Ruggeri, Michael P. Wiper

Published Online: 8 APR 2012

Print ISBN: 9780470744536

Online ISBN: 9780470975916

DOI: 10.1002/9780470975916

Series Editor(s): Walter A. Shewhart, Samuel S. Wilks

About this Book

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.

Table of contents

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  1. Part One: Basic Concepts and Tools

  2. Part Two: Models

  3. Part Three: Applications

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    1. You have free access to this content
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    1. You have free access to this content

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