Bayesian Analysis of Stochastic Process Models
Aus der Reihe
Wiley Series in Probability and Statistics
-
- Englisch ausgewählt
147,99 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Gebundene Ausgabe
Erscheinungsdatum
07.05.2012
Verlag
John Wiley & Sons IncSeitenzahl
332
Maße (L/B/H)
23,5/15,7/2,2 cm
Gewicht
585 g
Auflage
1. Auflage
Sprache
Englisch
ISBN
978-0-470-74453-6
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.
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.
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