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Approximating Probability Distributions (I)-(II): Monte Carlo Methods and Variational Inference

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If you have a question about this talk, please contact Emli-Mari Nel.

Two lectures!

Lectures 11 & 12 of a lecture series on Information Theory, Pattern Recognition and Neural Networks. The lectures will focus on approximating probability distributions (to be continued):

  • Monte Carlo Methods (I): Importance sampling, rejection sampling, Gibbs sampling, Metropolis method.
  • Monte Carlo Methods (II): Slice sampling, Hybrid Monte Carlo, Over-relaxation, Exact Sampling.
  • Variational Methods

Schedule:

Two 40-60 minute lectures will be given, with a 15 minute break in between. All are welcome, and encouraged to ask many questions.

Previous lectures can be downloaded: http://www.inference.phy.cam.ac.uk/itprnn_lectures/

This talk is part of the Inference Group series.

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