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Dirichlet Processes and Hierarchical Dirichlet Processes

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Advanced Machine Learning Tutorial Lecture

Dirichlet processes (DPs) are the most widely used class of Bayesian nonparametric models. DPs are most commonly used for mixture modelling where the nonparametric nature of DPs provide an elegant alternative to model selection of finite mixtures. Hierarchical Dirichlet processes (HDPs) are an extension of DPs to mixture modelling of grouped data, where mixture components can be shared across different groups. I shall give an in depth tutorial into both DPs and HDPs. In particular I shall cover the different representations of DPs and HDPs and applications of DPs and HDPs in a variety of fields. If time permits I shall touch upon generalizations of these models and inference schemes.

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

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