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.
Covers hitting probabilities in Markov chains with disjoint subsets, the function h(i), theorems, proofs, and expected time to hit calculations.
Explores low discrepancy sequences and their significance in stochastic simulation and numerical methods.
