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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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