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Saturday, November 17 • 10:40am - 11:00am
Motivating Probabilistic Programming

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Probabilistic Programming is a well established field within statistics and machine learning and over time researchers have noticed a few things: - it’s quite easy to construct a generative model; and - these models have the same kind of structure; but - writing an inference algorithm for each particular model is slow and error-prone. Wouldn’t it be great if we could derive an inference algorithm given a model description? We can think of this an analogous to writing in assembly versus a high-level language. When we write assembly we optimize everything by hand, implementing our own control structures and so on tailored to our specific problem. This is the situation with creating a custom inference algorithm for a particular generative model. When we write in a high-level language we use predefined constructs that the compiler translates into assembly for us. We gain a lot of productivity by giving up a little bit of performance (which we often don’t miss). This is the goal with probabilistic programming.

avatar for Rahul Chitturi

Rahul Chitturi

Principal Engineer, Coatue

Saturday November 17, 2018 10:40am - 11:00am

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