Bayesian Analysis of Stochastic Process Models (Wiley Series in Probability and Statistics) - Hardcover

Insua, David; Ruggeri, Fabrizio; Wiper, Mike

Hardcover

ISBN 10: 0470744537 ISBN 13: 9780470744536

Publisher: Wiley, 2012

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 this title

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.

From the Back 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.

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.

Edition details

• Publisher: Wiley
• Publication date: 2012
• Language: English
• ISBN 10: 0470744537
• ISBN 13: 9780470744536
• Binding: Hardcover
• Edition number: 1
• Number of pages: 316