We review three algorithms for parameter estimation of the Latent Dirichlet Allocation model: batch variational Bayesian inference, online variational Bayesian inference and inference using collapsed Gibbs sampling. We experimentally compare their time complexity and performance. We find that the online variational Bayesian inference converges faster than the other two inference techniques, with comparable quality of the results.