Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.
Delves into Markov chains by analyzing a scenario with two fleas moving in opposite directions, exploring transition matrices and probabilities over time.
