Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
