Broemeling L.D. Bayesian Inference for Stochastic Processes

Boca Raton: CRC Press, 2018. — 449 p.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.Introduction to Bayesian Inference for Stochastic Processes
Bayesian Analysis
Introduction to Stochastic Processes
Bayesian Inference for Discrete Markov Chains
Examples of Markov Chains in Biology
Inferences for Markov Chains in Continuous Time
Bayesian Inference: Examples of Continuous-Time Markov Chains
Bayesian Inferences for Normal Processes
Queues and Time Series