This month's speaker series will be hosted by Monetate (http://www.monetate.com/).
• An Introduction to Probabilistic Programming (Austin Rochford (https://twitter.com/austinrochford))
• That's like, so random! Monte Carlo for Data Scientists (Corey Chivers (https://twitter.com/cjbayesian))
An Introduction to Probabilistic Programming
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 (approximate) values/distributions of unobserved parameters. By separating the task of model specification from inference, probabilistic programming allows the modeler to “tell the story” of how the data were generated without explicitly developing an inference algorithm. This separation makes inference in many complex models more accessible.
This talk will give an introduction to probabilistic programming in Python using pymc3 (https://pymc-devs.github.io/pymc3/) and will also give a brief overview of the wider probabilistic programming ecosystem.
Bio: Austin Rochford is a Data Scientist at Monetate (http://www.monetate.com/). He is a former mathematician who is interested in Bayesian nonparametrics, multilevel models, probabilistic programming, and efficient Bayesian computation.
Corey Chivers (https://twitter.com/cjbayesian) (Penn Medicine (http://goo.gl/XFPoUc))
Bio: Corey Chivers is a Senior Data Scientist at Penn Medicine where he is building machine learning systems to improve patient outcomes. He received his PhD in Biology from McGill University in 2014. When he’s not pouring over data, he’s likely to be found cycling around his adoptive city of Philadelphia or blogging about all things probability and data at bayesianbiologist.com (http://bayesianbiologist.com/).