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.
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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 reinforce the basic concepts of the subject. R and WinBUGS."--Provided by publisher.