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An Introduction to Non-parametric Bayesian Methods

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

Room changed this week!

Bayesian methods provide a sound statistical framework for modelling and decision making. However, most simple parametric models are not realistic for modelling real-world data. Non-parametric models are much more flexible and therefore are much more likely to capture our beliefs about the data. They also often result in better predictive performance.

I will give a survey/tutorial of the field of non-parametric Bayesian statistics from the perspective of machine learning (a slightly revised version of my tutorial at the 2005 UAI Conference). Topics will include:

  • The need for non-parametric models
  • A very brief review of Gaussian processes
  • Chinese restaurant processes, different constructions, Pitman-Yor processes
  • Dirichlet processes, Dirichlet process mixtures
  • Polya trees
  • Dirichlet diffusion trees
  • Time permitting, some new work on Indian buffet processes

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

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The Martin Cohen Lecture Theatre, CRUK, Cambridge Research Institute, Cambridge Trinity College Chapel, Trinity Street Hicks Room, The University Centre, Granta Place, Cambridge CB2 1RU

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