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Lecture
Markov Chain Monte Carlo: Sampling and Convergence
Description
This lecture covers the concept of Markov Chain Monte Carlo (MCMC) for sampling from high-dimensional distributions. It explains the challenges of sampling when the normalization constant is unknown and introduces the Metropolis-Hastings algorithm. The lecture discusses the construction of a Markov chain with a desired stationary distribution, detailed balance conditions, and convergence properties. It also explores the application of MCMC in practice for optimizing functions and sampling from complex distributions.
Official source
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