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Bayesian Analysis of Stochastic Process Models Hardback - 2011 - 1st Edition
by Ruggeri, Fabrizio
- New
- Hardback
A$266.13
A$29.34
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Details
- Title Bayesian Analysis of Stochastic Process Models
- Author Ruggeri, Fabrizio
- Binding Hardback
- Edition number 1st
- Edition 1
- Condition New
- Pages 316
- Volumes 1
- Language ENG
- Publisher John Wiley & Sons Inc
- Publication date 2011
- Features Bibliography, Illustrated, Index
- Bookseller's Inventory # x-0470744537
- 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
- Quantity available 2
About Revaluation Books Devon, United Kingdom
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General bookseller of both fiction and non-fiction.
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From the publisher
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.
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.
