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Lecture
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
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.