Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA

1 code implementation • 8 May 2015 • Yannis Papanikolaou, James R. Foulds, Timothy N. Rubin, Grigorios Tsoumakas

We introduce a novel approach for estimating Latent Dirichlet Allocation (LDA) parameters from collapsed Gibbs samples (CGS), by leveraging the full conditional distributions over the latent variable assignments to efficiently average over multiple samples, for little more computational cost than drawing a single additional collapsed Gibbs sample.

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