Composing Modeling and Inference Operations with Probabilistic Program Combinators

14 Nov 2018Eli SenneshAdam ŚcibiorHao WuJan-Willem van de Meent

Probabilistic programs with dynamic computation graphs can define measures over sample spaces with unbounded dimensionality, which constitute programmatic analogues to Bayesian nonparametrics. Owing to the generality of this model class, inference relies on `black-box' Monte Carlo methods that are often not able to take advantage of conditional independence and exchangeability, which have historically been the cornerstones of efficient inference... (read more)

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