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<!DOCTYPE html>
<html>
<head>
<meta charset='utf-8'>
<meta http-equiv="X-UA-Compatible" content="chrome=1">
<link rel="stylesheet" type="text/css" href="stylesheets/stylesheet.css" media="screen">
<link rel="stylesheet" type="text/css" href="stylesheets/github-dark.css" media="screen">
<link rel="stylesheet" type="text/css" href="stylesheets/print.css" media="print">
<title>BigBayes by BigBayes</title>
</head>
<body>
<header>
<div class="container">
<h1>BigBayes</h1>
<h2>Rich, Structured and Efficient Learning of Big Bayesian Models</h2>
</div>
</header>
<div class="container">
<section id="main_content">
<h3>
<a id="welcome" class="anchor" href="#welcome" aria-hidden="true"><span class="octicon octicon-link"></span></a>Welcome</h3>
<p>
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.
</p>
<p>
The project is generously funded by an <a href="http://erc.europa.eu/">ERC</a> Consolidator Fellowship awared to Yee Whye Teh.
It supports the <a href="http://mlcs.stats.ox.ac.uk/learning">statistical machine learning</a> group at the Department of Statistics, University of Oxford.
</p>
<h3>
<a id="further-information" class="anchor" href="#further-information" aria-hidden="true"><span class="octicon octicon-link"></span></a>Further Information</h3>
<p>For more information, check out the <a href="https://github.com/BigBayes/bigbayes.github.io/wiki">public wiki</a>.</p>
<h3>
<a id="publications" class="anchor" href="#publications" aria-hidden="true"><span class="octicon octicon-link"></span></a>Publications</h3>
<p>Can be found at the <a href="http://mlcs.stats.ox.ac.uk/projects/bigbayes/">group website</a>.</p>
<h3>
<a id="repositories" class="anchor" href="#repositories" aria-hidden="true"><span class="octicon octicon-link"></span></a>Repositories</h3>
<p>Can be found at <a href="https://github.com/BigBayes">GitHub repositories</a>.</p>
<h3>Some Fun Movies</h3>
<p>(Download movies <a href="images/ibeta.mp4">here</a>
and <a href="images/uniform_color.mp4">here</a>).</p>
<embed src="images/ibeta.mp4" width="396" height="287" CONTROLLER="true" LOOP="true" AUTOPLAY="true" name="Beta Mondrian"></embed>
<embed src="images/uniform_color.mp4" width="396" height="287" CONTROLLER="true" LOOP="true" AUTOPLAY="true" name="Uniform Mondrian"></embed>
</section>
</div>
</body>
</html>