Bayesian Analysis of Stochastic Process Models

David Insua

ISBN 10: 0470744537 / ISBN 13: 9780470744536

Published by John Wiley & Sons Inc, 2012

About this Item

Bayesian Analysis of Stochastic Process Models 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. Series: Wiley Series in Probability and Statistics. Num Pages: 332 pages, Illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 159 x 235 x 22. Weight in Grams: 588. . 2012. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780470744536

Bibliographic Details

• Title: Bayesian Analysis of Stochastic Process Models
• Publisher: John Wiley & Sons Inc
• Publication Date: 2012
• Language: English
• ISBN 10: 0470744537
• ISBN 13: 9780470744536
• Binding: Hardcover
• Condition: New

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.