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October 18, 2016 @ 6:30 pm - 8:00 pm
Latent Dirichlet Allocation and related topic models are often presented in the form of complicated equations and confusing diagrams. Research Triangle Analyst’s Tim Hopper will present 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.
Those without training in probabilistic graphical models and measure theory may have a hard time understanding Latent Dirichlet Allocation and other probabilistic topic models. However, because LDA is a generative model, Python code can be written to generate data based on the model assumptions.