Rich, Structured and Efficient Learning of Big Bayesian Models


As datasets grow ever larger in scale, complexity and variety, there is an increasing need for powerful machine learning and statistical techniques that are capable of learning from such data. Bayesian nonparametrics is a promising approach to data analysis that is increasingly popular in machine learning and statistics. Bayesian nonparametric models are highly flexible models with infinite-dimensional parameter spaces that can be used to directly parameterise and learn about functions, densities, conditional distributions etc. This project aims to develop Bayesian nonparametric techniques for learning rich representations from structured data in a computationally efficient and scalable manner.

The project is generously funded by an ERC Consolidator Fellowship awared to Yee Whye Teh. It supports the statistical machine learning group at the Department of Statistics, University of Oxford.

Further Information

For more information, check out the public wiki.


Can be found at the group website.


Can be found at GitHub repositories.

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