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An Introduction to Statistical Inference, Data Modelling & Pattern Recogntion

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

Two Lectures!

Lecture 9 & 10 of a lecture series on Information Theory, Pattern Recognition and Neural Networks will be covered. Please see

This is the beginning of part 2 of the lecture series, and will mainly focus on inference algorithms, briefly returning to information theory at the end.

Topics that will be covered (to be continued):

  • The likelihood function and Bayes’ theorem
  • Clustering as an example
  • Laplace’s method
  • Monte Carlo Methods (I): Importance sampling, rejection sampling, Gibbs sampling, Metropolis method.

Schedule: Two one hour lectures will be given, with a 15 minute break in between.

The course used to be a Part III Physics course. Lectures will be given on Monday afternoons – some lectures might be canceled, so please consult before each lecture (at least a week’s notice will be given). All are welcome, and encouraged to ask many questions.

This talk is part of the Inference Group series.

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