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Advanced MCMC Methods

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If you have a question about this talk, please contact Zoubin Ghahramani.

Advanced Machine Learning Tutorial Lecture

Markov chain Monte Carlo (MCMC) algorithms draw correlated samples from probability distributions. These allow approximate computation of complex high-dimensional integrals; obvious applications include Bayesian statistics and statistical physics. This tutorial will not assume any prior knowledge of MCMC , but will cover state-of-the-art techniques.

Tentative Schedule:

  • Introduction: Metropolis—Hastings vs “simpler” Monte Carlo methods
  • Hamiltonian (Hybrid) Monte Carlo
  • Auxiliary variables and Slice sampling
  • Out of equilibrium: tempering/annealing and related advances.
  • Possibly a few words on infinite models and doubly-intractable distributions.

This talk is part of the Machine Learning @ CUED series.

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