LDAs
LDAs, or Latent Dirichlet Allocation, is a generative probabilistic model used for topic modeling. It assumes that documents are mixtures of topics, and that topics are probability distributions over words. The goal of LDA is to discover these underlying topics from a collection of documents.
The process begins by assuming a prior distribution on the topic mixtures for each document and a
LDA iteratively refines these topic and document assignments. By observing the words in the documents, it learns