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This lecture covers the Markov Chain Monte Carlo method, focusing on the Metropolis-Hastings algorithm. It explains how to generate samples from a target probability distribution using this algorithm, constructing a transition kernel. The lecture also discusses the detailed balance condition and the importance of density functions in the process.