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.