Bayesian Analysis of Stochastic Process Models (Hardcover)
Language: English
Published by John Wiley & Sons Inc, New York, 2012
- First Edition
- Hardcover
- New

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Hardcover. 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …
Seller Inventory # 9780470744536
- Title
- Bayesian Analysis of Stochastic Process Models (Hardcover)
- Author
- David Insua
- Publisher
- John Wiley & Sons Inc, New York
- Publication year
- 2012
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0470744537
- ISBN 13
- 9780470744536
- Edition
- 1st Edition
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.
"Synopsis" may belong to another edition of 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.
"About the title" may belong to another edition of this title.
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