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Dirichlet Process Mixture Models and Bayesian Nonparametric Density Estimation

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Often we are unsure about what probability density functions to use in our models. Ideally we would like the flexibility to infer any true underlying density but without overfitting. Surprisingly, this is possible using Bayesian nonparametric approaches like the Dirichlet process infinite mixture model. I will give a tutorial on Dirichlet process mixture models, and discuss alternative Bayesian Nonparametric approaches to density estimation, including the Gaussian process density sampler (Adams et. al, 2009) and Pitman Yor diffusion trees (Knowles and Ghahramani, 2011).

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

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