Coupling of Markov Chains: Ergodic Theorem

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

Explores the proof of the ergodic theorem using total variation distance and coupling concepts.

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.

Explores correlations of the Liouville function along deterministic and independent sequences, covering key concepts and theorems.

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

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

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Covers the joint equidistribution of CM points and the ergodic decomposition theorem in compact abelian groups.

Covers stochastic models for communications, focusing on discrete-time Markov chains.