Continuous-Time Markov Chains: Reversible Chains

Related lectures (31)

Page 2 of 4

Covers examples of absorbing Markov chains in discrete time.

Explores the PageRank algorithm within Markov chains, emphasizing ergodicity and convergence for web page ranking.

Explains the proof of the ergodic theorem and the concept of positive-recurrence in Markov chains.

Explores limiting distribution in Markov chains and the implications of ergodicity and aperiodicity on stationary distributions.

Covers Markov chains, focusing on reversibility, convergence, ergodicity, and applications.

Explores ergodic theory in Markov chains, discussing irreducibility and unique stationary distributions.

Covers the concept of invariant measures in Markov chains and their role in analyzing irreducible recurrent processes.

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

Covers examples of absorbing chains in discrete-time Markov chains.

Explores Markov chain convergence, spectral gap, and acceleration techniques for faster convergence.