Recorded Lectures: MCMC
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The lectures, after an introduction to the problem, discuss two fundamental sampling techniques, rejection and importance sampling, and then turn their focus to MCMC, introducing the required background, the Metropolis Hastings algorithms, Gibbs sampling, and finally discussing some diagnostics and an extension of MCMC, Hamiltonian Monte Carlo, heavily used nowadays in inference engines, e.g. Pyro.
Introduction
Rejection Sampling
Importance Sampling
Markov Chains
Detailed Balance
Metropolis-Hastings MCMC
Gibbs sampling
Convergence diagnostics
Hamiltonian Monte Carlo
Ultime modifiche: venerdì, 28 febbraio 2025, 16:46