Bayesian Analysis of Stochastic Process Models
Copyright © 2012 John Wiley & Sons, Ltd
Author(s): David Rios Insua, Fabrizio Ruggeri, Michael P. Wiper
Published Online: 8 APR 2012
Print ISBN: 9780470744536
Online ISBN: 9780470975916
Book Series: Wiley Series in Probability and Statistics
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.
- 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.