Probabilistic Programming in the Real World - Zach Anglin

PyData DC 2018

Probabilistic programming frameworks get a lot of press, but relatively little attention is paid to the indicators that a problem is a good fit for a Bayesian approach, how to pick a framework, or the right first steps to structuring a probabilistic model. This talk aims at building a structural underpinning for Bayesian modeling and lays out concrete strategies for solving real world problems.
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