Accelerated Gibbs Sampling for the Indian Buffet Process

author: Finale Doshi, Department of Engineering, University of Cambridge
published: Aug. 26, 2009,   recorded: June 2009,   views: 6509
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Description

We often seek to identify co-occurring hidden features in a set of observations. The Indian Buffet Process (IBP) provides a nonparametric prior on the features present in each observation, but current inference techniques for the IBP often scale poorly. The collapsed Gibbs sampler for the IBP has a running time cubic in the number of observations, and the uncollapsed Gibbs sampler, while linear, is often slow to mix. We present a new linear-time collapsed Gibbs sampler for conjugate likelihood models and demonstrate its efficacy on large real-world datasets.

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