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.
Explores Markov chains, their properties, and algorithmic applications, emphasizing information quantification and state monotonicity.
Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.
Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.
