Continuous Time Markov Chains

Related lectures (32)

Page 1 of 4

Explores classification, communication, and irreducibility in Markov chains.

Explores the concepts of recurrence and transience in continuous time Markov chains.

Covers reversible discrete-time Markov chains in communication models.

Covers hitting probabilities in Markov chains with disjoint subsets, the function h(i), theorems, proofs, and expected time to hit calculations.

Explores the lower bound on total variation distance in Markov chains and its implications on mixing time.

Explores the classification of states in Markov chains, emphasizing equivalence classes and periodicity.

Explores challenges in human communication, context importance, compression problems, uncertain functionality, and proof verification.

Covers optimization algorithms, stable matching, and Big-O notation for algorithm efficiency.

Explores recurrence, transience, and invariant distributions in Markov chains with proofs and examples.

Covers Markov chains, coupled chains, and their applications, emphasizing the importance of irreducibility.