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Computer science / Coursework / Randomized algorithms
Lecture 5: Markov Chains, Random Walks & MC Sampling
Memoryless random motion settling into a unique stationary distribution, made into a sampler (MCMC, Metropolis–Hastings); mixing via coupling, stopping times and expanders; sampling becomes counting.