I am new in the probabilistic topic modeling, and I need to understand deeply the Latent Dirichlet Allocation (LDA) process.
I understand what want to do the inference process in LDA, and I understand too that there are 2 "types" of inference: probabilistic methods (like the Gibbs sampling) and deterministic ones.
In the deterministic type some papers talk about Expectation Maximization algorithm and some others talk about something called "variational inference". So, are those things the same?
Also, can you explain how is applied the EM algorithm for the LDA?