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Markov Chain Convergence Analysis: Uniformization, Mixing Times, and Spectral Gap
An end-to-end analysis of continuous-time Markov chain (CTMC) convergence using uniformization. Covers generator matrix construction, Doeblin minorization, TV-distance mixing times across multiple initial distributions, expected reward convergence, and spectral gap analysis. Includes a structural comparison between dense and sparse transition matrices, with theoretical proofs for shared stationary distributions and Dobrushin's ergodicity theorem.