Covers rhythm generation techniques, including Markov models and hierarchical rhythm generation, with a focus on Nancarrow's Study 14.
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.
