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Foundations of Nonparametric Bayesian Methods (Part I)

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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 I: Basics

At least half of the first part will probably be spent reviewing concepts from measure-theoretic probability (and motivating why we need them for Bayesian nonparametrics). We will then define Bayesian estimation in these terms, introduce the basic construction tools for stochastic processes, and see how the Gaussian and Dirichlet processes are constructed from finite-dimensional Gaussian and Dirichlet distributions.


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

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