Accelerated sampling for the Indian Buffet Process

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Accelerated sampling for the Indian Buffet Process
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.
Finale Doshi-Velez, Zoubin Ghahramani
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICML
Authors Finale Doshi-Velez, Zoubin Ghahramani
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