Presentation: Probabilistic Programming from Scratch

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.

This talk is perhaps most useful for people who deal with real world data and face concrete statistical problems. But Bayesian inference provides a powerful day-to-day mental model for thinking about data and belief. And in keeping with the CS track, this talk will be an introduction to a new programming language paradigm for some. So I hope it will be at least interesting to a very wide audience! 

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

This might seem like a talk about statistics, mathematics and computer science. But my goal is that everyone who can write a for loop will understand everything we do. I attempt to ensure this by implementing things from scratch, and choosing a Bayesian inference algorithm that is particularly transparent and non-mathematical. I happen to use Python a little in this talk, but it's not essential that you can code in Python. And very importantly: no mathematics is required!