Home Bayesian Analysis of Stochastic Process Models by David Insua; Fabrizio Ruggeri; Mike Wiper (ISBN: 9780470744536)

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Bayesian Analysis of Stochastic Process Models Hardback - 2012 - 1st Edition

Name: Bayesian Analysis of Stochastic Process Models
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Author: David Insua
ISBN: 9780470744536

by David Insua; Fabrizio Ruggeri; Mike Wiper

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From the publisher

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.

From the rear cover

Bayesian analysis of complex models based on stochastic processes has seen a surge in research activity in recent years. Bayesian Analysis of Stochastic Process Models 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.

Bayesian Analysis of Stochastic Process Models:

Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment.
Provides a thorough introduction for research students.
Includes computational tools to deal with complex problems, illustrated with real life case studies
Computational tools to deal with complex problems are illustrated along with real life case studies
Examines inference, prediction and decision making.

Details

Title Bayesian Analysis of Stochastic Process Models
Author David Insua; Fabrizio Ruggeri; Mike Wiper
Binding Hardback
Edition number 1st
Edition 1
Pages 316
Volumes 1
Language ENG
Publisher Wiley
Publication date 2012-05-07
Features Bibliography, Illustrated, Index
ISBN 9780470744536 / 0470744537
Weight 1.27 lbs (0.58 kg)
Dimensions 9.1 x 6.1 x 0.9 in (23.11 x 15.49 x 2.29 cm)
Themes
- Aspects (Academic): Science/Technology Aspects
Category Mathematics
Library of Congress subjects Bayesian statistical decision theory, Stochastic processes
Library of Congress Catalogue Number 2012000092
Dewey Decimal Code 519.542

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.

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Bayesian Analysis of Stochastic Process Models

by David Insua

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Description:

Hardback. New. 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.