Bayesian Analysis of Stochastic Process Models

Nonfiction, Science & Nature, Mathematics, Probability, Statistics
Cover of the book Bayesian Analysis of Stochastic Process Models by Mike Wiper, Fabrizio Ruggeri, David Insua, Wiley
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Author: Mike Wiper, Fabrizio Ruggeri, David Insua ISBN: 9781118304037
Publisher: Wiley Publication: April 2, 2012
Imprint: Wiley Language: English
Author: Mike Wiper, Fabrizio Ruggeri, David Insua
ISBN: 9781118304037
Publisher: Wiley
Publication: April 2, 2012
Imprint: Wiley
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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:

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.

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