Bayesian Analysis of Stochastic Process Models

David Insua

ISBN 10: 0470744537 / ISBN 13: 9780470744536

Published by Wiley Mai 2012, 2012

About this Item

Neuware - 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.

Seller Inventory # 9780470744536

Bibliographic Details

• Title: Bayesian Analysis of Stochastic Process Models
• Publisher: Wiley Mai 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 0470744537
• ISBN 13: 9780470744536
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 157H x 22D millimeters 235 millimeters width by 157 millimeters height by 22 millimeters depth
• Weight: 612 grams

About this title

Synopsis

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.

About the Author

Fabrizio Ruggeri, Research Director, CNR IMATI, Milano, Italy.

Michael P. Wiper, Associate Professor in Statistics, Department of Statistics, Universidad Carlos III de Madrid, Spain.

David Rios Insua, Professor of Statistics and Operations Research, Department of Statistics and Operations Research, Universidad Rey Juan Carlos, Spain.