Bayesian Analysis of Stochastic Process Models

David Insua · Fabrizio Ruggeri · Mike Wiper

2012年4月 · John Wiley & Sons

電子書籍

316

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この電子書籍について

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.

著者について

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.