Cover Image

Bayesian Inference for Stochastic Processes

Show other versions (1)

"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...

Full description

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
STATSnetBASE
STMnetBASE
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • chapter 1 Introduction to Bayesian Inference for Stochastic Processes / Lyle D. Broemeling
  • chapter 2 Bayesian Analysis / Lyle D. Broemeling
  • chapter 3 Introduction to Stochastic Processes / Lyle D. Broemeling
  • chapter 4 Bayesian Inference for Discrete Markov Chains / Lyle D. Broemeling
  • chapter 5 Examples of Markov Chains in Biology / Lyle D. Broemeling
  • chapter 6 Inferences for Markov Chains in Continuous Time / Lyle D. Broemeling
  • chapter 7 Bayesian Inference: Examples of Continuous-Time Markov Chains / Lyle D. Broemeling
  • chapter 8 Bayesian Inferences for Normal Processes / Lyle D. Broemeling
  • chapter 9 Queues and Time Series / Lyle D. Broemeling.