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Computable Probability Theory

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If you have a question about this talk, please contact Zoubin Ghahramani .

How much of statistics can we automate? At MIT , I’m working as a member of a team to develop a probabilistic programming language, Church, suitable for rigorously and formally specifying probabilistic models and a language implementation, MIT -Church, capable of performing automatic inference. In this talk, I’ll discuss some of the theoretical limits of this endeavor, in particular work concerning universality, representational equivalence, and computability of conditioning. I might even wax philosophical on the last point.

This is joint work with Nate Ackerman (UPenn) and Cameron Freer (MIT).

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

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