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Probabilistic Programming

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What is the most general class of statistical models? And how can we perform inference without designing custom algorithms? Just as automated inference algorithms make working with graphical models easy (e.g. BUGS ), a new class of automated inference procedures is being developed for the more general case of Turing-complete generative models. In this tutorial, we introduce the practice of specifying generative models as programs which produce a stochastic output, and then automatically performing inference on the execution trace of that program, conditioned on the algorithm having produced a specific output. We give examples of how to specify complex models and run inference in several ways, including recent advances in automatic Hamiltonian Monte Carlo and variational inference.

Readers who wish to familiarize themselves with these ideas beforehand are encouraged to browse around at: and

This talk is part of the Machine Learning Reading Group @ CUED series.

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