Markov Chains: Applications and Coupled Chains

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Explores the coupling of Markov chains and the proof of the ergodic theorem, emphasizing distribution convergence and chain properties.

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

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

Explores proof concepts, techniques, and applications in logic, mathematics, and algorithms.

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

Explores Markov chains and algorithm applications, including exact simulation and Propp-Wilson algorithms.

Covers the theory of Markov chains, focusing on reversible chains and detailed balance.

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

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

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.