LDA is a hierarchical Bayesian model that assumes topics are probability distributions over words, and documents are distributions over topics. More specifically, the model assumes that topics follow a sparse Dirichlet distribution, which implies that documents cover only a small set of topics, and topics use only a small set of words frequently.