Bayesian Analysis of Stochastic Process Models
Aus der Reihe
Wiley Series in Probability and Statistics
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Form:Einzelkauf Download
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Sprache:Englisch
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eBook Format:ePUB
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81,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
02.04.2012
Verlag
John Wiley & SonsSeitenzahl
320 (Printausgabe)
Dateigröße
6011 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9781118304037
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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