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.
Introduces Coq, covering defining propositions, proving theorems, and using tactics.
Covers the basic theory for continuous time Markov chains and discusses communication, hitting probabilities, recurrence, and transience.
Explores transience and recurrence in Markov chains, emphasizing birth and death chains and random walks.
