Abstract: Probabilistic programming is a paradigm in which the programmer specifies a generative probability model for observed data and the language/software library infers the distributions of unobserved quantities. By separating model specification from inference, probabilistic programming allows the modeler to "tell the story" of how the data were generated and then perform inference without explicitly developing an inference algorithm. This separation makes inference more accessible for many complex models. PyMC3 is a Python package for probabilistic programming built on top of Theano that provides advanced sampling and variational inference algorithms and is undergoing rapid development. This talk will give an introduction to probabilistic programming using PyMC3 and will conclude with a brief overview of the wider probabilistic programming ecosystem.
Bio: Austin Rochford is a Principal Data Scientist at Monetate. He is a recovering mathematician and is passionate about math education, Bayesian statistics, and machine learning. His writing is available online at austinrochford.com.
Principal Data Scientist at Monetate