Foundations of Nonparametric Bayesian Methods (Part II)
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If you have a question about this talk, please contact Peter Orbanz.
This 3-part tutorial will address a machine learning audience, not
assumed to be familiar with measure theory or the theory stochastic
processes. The course is intended to provide (1) an overview of what
nonparametric Bayesian models exist beyond those already used in
machine learning, and (2) a basic understanding of the mathematical
construction of ’’process’’ models, both existing ones and new models
on a variety of possible domains.
Part II: Models on the simplex
Most of the existing Bayesian nonparametric literature, especially in
statistics, focusses on models on the simplex, i.e. probabilities on
probabilities. This second part will discuss different classes of
existing models and their properties, including Dirichlet, tailfree
and Levy processes.
Webpage:
http://mlg.eng.cam.ac.uk/porbanz/npb-tutorial.html
This talk is part of the Machine Learning @ CUED series.
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