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Andrew D. Gordon: Probabilistic Functional Programming as a Platform for Machine Learning

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When Sep 25, 2012
from 01:00 PM to 02:00 PM
Where G.03
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I will describe progress on Fun, an embedded domain-specific language which turns the simple succinct syntax of F# into an executable modelling language for Bayesian machine learning.  Eventually, we are aiming for Fun to be a portable format for declaring conditional probability distributions, independent of any particular backend inference algorithm.  We plan that Fun gives functional programmers a competitive advantage over users of conventional toolkits when building custom graphical models for machine learning tasks.

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