Covers hitting probabilities in Markov chains with disjoint subsets, the function h(i), theorems, proofs, and expected time to hit calculations.
Covers the basic theory for continuous time Markov chains and discusses communication, hitting probabilities, recurrence, and transience.
Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.
Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.
Covers the Meyers-Serrin theorem in analysis, discussing the conditions for functions in different spaces.
