Probabilistic programming is a general framework to express probabilistic models as programs. It lies at the intersection of machine learning, statistics, and programming languages. While it has classically been seen as mechanization of Bayesian statistical inference, it has recently emerged as a candidate for next-generation AI toolchains.
In this seminar, we will both read and discuss selected chapters from books and computational Bayesian data analysis, as well as research papers in the area of probabilistic programming. Theoretical reading will be supplemented with practical examples (i.e., written programs).
Seminar participants are expected to read the chapters or papers before attending sessions.
As a small final project, everyone (including the lecturer!) will present a probabilistic program implemented in a language of their choice (Gen, Stan, Pyro, Tensorflow Probability, etc.)
