When estimating the parameters of the LDA (Latent Dirichlet Allocation) model using Gibbs sampling, if we set the Dirichlet priors to the fixed target values (usually small), then we reduce the mixing of the samples from the target distribution from the beginning, even though we haven’t found a good …

The original articles on LDA (Latent Dirichlet Allocation) assume symmetric Dirichlet priors on topic-words and document-topics distributions. This means that a-priori we assume that all topics are equally likely to appear within each document, and all words are equally likely to appear within each topic. However, if we want to …