BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Bayesian Analysis of Stochastic Process Models

Bayesian Analysis of Stochastic Process Models

Bayesian Analysis of Stochastic Process Models
Stock photo: cover may vary

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

by DAVID INSUA

Add to wish list
  • Used
New

Description

like new.
Ask the seller a question Add to wish list
A$182.16
A$5.86 Delivery within USA
Standard delivery: 2 to 14 days
More delivery options
Ships from GreatBookPrices (Maryland, United States)

Details

  • Title Bayesian Analysis of Stochastic Process Models
  • Author DAVID INSUA
  • Binding Hardback
  • Edition number 1st
  • Edition 1
  • Condition New
  • Pages 316
  • Volumes 1
  • Language ENG
  • Publisher Wiley
  • Publication date 2012-05-07
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # 5846350
  • 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 5

About GreatBookPrices Maryland, United States

Biblio member since 2024

Since 1991, we have worked every day to serve our customers with state-of-the-art technology and world class service. We are dedicated to providing customers around the world with the widest selection of books, DVDs, and CDs at the absolute lowest price.

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from GreatBookPrices

Reader reviews for Bayesian Analysis of Stochastic Process Models

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.

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.

tracking-