In fitting the Latent Dirichlet Allocation with collapsed Gibbs sampling one builds a sampled approximation to the topic posterior distribution, $P(z|w)$ and use that to calculate the topic and word probabilities for each document.

Is this posterior distribution uni-modal? Or are there several topic configurations that give rise to the same posterior probability (and hence different, but equally likely topic probabilities for each document)?


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