Probabilistic Programming
A probabilistic programming language (PPL) is a programming language designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible. Probabilistic programming represents an attempt to "[unify] general purpose programming with probabilistic modeling."
source: https://en.wikipedia.org/wiki/Probabilistic_programming_language
Position on the Adoption Curve
Presentations about Probabilistic Programming
Probabilistic Programming from Scratch
Software Is Eating the World, ML Is Going to Eat Software
Interviews
Probabilistic Programming from Scratch
What do you want someone to leave your talk with?
The audience will leave with a strong non-mathematical intuition for how Bayesian inference allows us to quantify the strength of conclusions drawn from real-world data. They’ll hopefully be excited to solve other toy problems with the tool we put together during the talk, and keen to check out PyMC3.
Is probabilistic programming a real thing? Can you give me an example of where it's being used today?
Yes, it's a real thing! The most prominent examples of tech companies using these ideas in the real world are Facebook's Prophet time series forecasting system (which I'll discuss in the talk), and Uber's release of Pyro, an open source deep probabilistic programming system built on top of PyTorch. And Google are now getting involved with Tensorflow Probability
