Continuous Time Dynamic Topic Models

Understand

In this paper, we develop the continuous time dynamic topic model (cDTM).

• The cDTM is a dynamic topic model that uses Brownian motion to model the latent topics through a sequential collection of documents, where a "topic" is a pattern of word use that we expect to evolve over the course of the collection.
• We derive an efficient variational approximate inference algorithm that takes advantage of the sparsity of observations in text, a property that lets us easily handle many time points.
• In contrast to the cDTM, the original discrete-time dynamic topic model (dDTM) requires that time be discretized.