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

Über die Autorin bzw. den Autor

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.

Von der hinteren Coverseite

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.

Aus dem Klappentext

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.