Understanding Probabilistic Topic Models By Simulation

Presentation on understanding probabilistic topic models by simulating the generative model

I gave a talk last week at Research Triangle Analysts on understanding probabilistic topic models (specificly LDA) by using Python for simulation. Here’s the description:

Latent Dirichlet Allocation and related topic models are often presented in the form of complicated equations and confusing diagrams. Tim Hopper presents LDA as a generative model through probabilistic simulation in simple Python. Simulation will help data scientists to understand the model assumptions and limitations and more effectively use black box LDA implementations.

You can watch the video on Youtube:

I gave a shorter version of the talk at PyData NYC 2015.

Last updated on Feb 14, 2024 19:48 -0500