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Dirichlet Processes: Tutorial and Practical Course

Dirichlet Processes: Tutorial and Practical Course

This video was recorded at Machine Learning Summer School (MLSS), Tübingen 2007. The Bayesian approach allows for a coherent framework for dealing with uncertainty in machine learning. By integrating out parameters, Bayesian models do not suffer from overfitting, thus it is conceivable to consider models with infinite numbers of parameters, aka Bayesian nonparametric models. An example of such models is the Gaussian process, which is a distribution over functions used in regression and classification problems. Another example is the Dirichlet process, which is a distribution over distributions. Dirichlet processes are used in density estimation, clustering, and nonparametric relaxations of parametric models. It has been gaining popularity in both the statistics and machine learning... Show More
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