Coupling of Markov Chains: Ergodic Theorem

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Page 3 of 4

Explores Bourgain's theorem on sparsest cut in graphs, emphasizing semimetrics and cut optimization.

Explores stable laws, limit theorems, and random variable properties.

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

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

Explores probability convergence, discussing conditions for random variable sequences to converge and the uniqueness of convergence.

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

Explores optimal transport in heat equations and metric spaces.

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

Covers the Zig Zag Lemma and the long exact sequence of relative homology.

Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.