This particular implementation performs Gibbs sampling, a form of Markov chain Monte Carlo (MCMC), to estimate the optimal model parameters fitted to the training data.
the algorithm creates a Markov chain whose stationary distribution matches the expected distribution of the state and topic labels for each word in the training corpus.