## Inferring the posteriors in LDA through Gibbs sampling

In my last blog post, which was about a million years ago, I described the generative nature of LDA and left the interferential step open. In this blog post, I will explain one method to calculate estimations of the topic distribution θ and the term distribution ϕ. This approach, first formulated by Griffiths and Steyvers (2004) in the context of LDA, is to use Gibbs sampling, a common algorithm within the Markov Chain Monte Carlo (MCMC) family of sampling algorithms. Before applying Gibbs sampling directly to LDA, I will first give a short introduction to Gibbs sampling more generally.

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