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Bayesian Inference for Stochastic Processes

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"The book aims to introduce Bayesian inference methods for stochastic processes. The Bayesian approach has advantages compared to non-Bayesian, among which is the optimal use of prior information via data from previous similar experiments. Examples from biology, economics, and astronomy reinfor...

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Bibliographic Details
Main Author: Broemeling, Lyle D. (Author)
Corporate Author: Taylor & Francis
Format: eBook
Language:English
Published: Boca Raton, FL : CRC Press, 2017.
Edition:First edition.
Subjects:
Bayesian statistical decision theory.
MATHEMATICS > Applied.
MATHEMATICS > Probability & Statistics > General.
Probabilities.
Stochastic processes.
MATHnetBASE
Probability Theory & Applications
SCI-TECHnetBASE
Statistical Theory & Methods
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Electronic books.
Online Access:Connect to the full text of this electronic book
Description
Summary:"The book aims to introduce Bayesian inference methods for stochastic processes. The Bayesian approach has advantages compared to non-Bayesian, among which is the optimal use of prior information via data from previous similar experiments. Examples from biology, economics, and astronomy reinforce the basic concepts of the subject. R and WinBUGS."--Provided by publisher.
Physical Description:1 online resource
ISBN:9781315303567
9781315303598