Explores Markov chains, their properties, and algorithmic applications, emphasizing information quantification and state monotonicity.
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.
Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.
Covers quantum entropy, density matrices, and entanglement in quantum information theory.
