University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Probabilistic Programming

Probabilistic Programming

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Colorado Reed.

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: http://probabilistic-programming.org/wiki/Home and http://projects.csail.mit.edu/church/wiki/Church

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

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity