Bayesian Inference for Stochastic Processes

✍ Scribed by Lyle D Broemeling

Publisher: CRC Press
Year: 2018
Tongue: English
Leaves: 449
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Content: 1. Introduction to Bayesian Inference for Stochastic Processes2. Bayesian Analysis3. Introduction to Stochastic Processes4. Bayesian Inference for Discrete Markov Chains5. Examples of Markov Chains in Biology6. Inferences for Markov Chains in Continuous Time7. Bayesian Inference: Examples of Continuous-Time Markov Chains8. Bayesian Inferences for Normal Processes9. Queues and Time Series

✦ Subjects

Probabilities;Bayesian statistical decision theory;Stochastic processes

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