Bayesian Analysis of Stochastic Process Models - Rilegato

Rios Insua, David; Ruggeri, Fabrizio; Wiper, Michael P.

Rilegato

ISBN 10: 0470744537 ISBN 13: 9780470744536

Casa editrice: John Wiley & Sons Inc, 2012

Riassunto

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.

Su questo libro

Informazioni sull?autore

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.

Dalla quarta di copertina

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.

Dal risvolto di copertina interno

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.

Dettagli dell'edizione

• Editore: John Wiley & Sons Inc
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 0470744537
• ISBN 13: 9780470744536
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 290
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile