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Hybrid Variational/Gibbs Collapsed Inference in Topic Models

11 years 2 months ago
Hybrid Variational/Gibbs Collapsed Inference in Topic Models
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is also inefficient for large count values and requires averaging over many samples to reduce variance. On the other hand, variational Bayesian inference is efficient and accurate for large count values but suffers from bias for small counts. We propose a hybrid algorithm that combines the best of both worlds: it samples very small counts and applies variational updates to large counts. This hybridization is shown to significantly improve testset perplexity relative to variational inference at no computational cost.
Max Welling, Yee Whye Teh, Bert Kappen
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where UAI
Authors Max Welling, Yee Whye Teh, Bert Kappen
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