Markov Chains: Convergence and Equilibrium

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Explores Markov chain convergence, emphasizing invariant distribution, Law of Large Numbers, and mean rewards computation.

Explores conditional expectation, the grouping lemma, and the law of large numbers.

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

Explores recurrent states, invariant distributions, convergence to equilibrium, and PageRank algorithm.

Explores invariant distributions, recurrent states, and convergence in Markov chains, including practical applications like PageRank in Google.

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

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Covers conditional expectation, convergence of random variables, and the strong law of large numbers.

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

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