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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.


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

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