Probabilistic programming is a general-purpose means of expressing and automatically performing model-based inference. A key characteristic of many probabilistic programming systems is that models can be compactly expressed in terms of executable generative procedures, rather than in declarative mathematical notation. For this reason, along with automated or programmable inference, probabilistic programming has the potential to increase the number of people who can build and understand their own models. It also could make the development and testing of new general-purpose inference algorithms more efficient, and could accelerate the exploration and development of new models for application-specific use.

The primary goals of this tutorial will be to introduce probabilistic programming both as a general concept and in terms of how current systems work, to examine the historical academic context in which probabilistic programming arose, and to expose some challenges unique to probabilistic programming.