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Joint Distributions for TensorFlow Probability

2020/01/22 by Piponi, Dan, Moore, Dave, Dillon, Joshua V. · 1 citation
#Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Programming Languages (cs.PL)

paper · doi:10.48550/arxiv.2001.11819

Abstract

A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms. We describe JointDistributions, a family of declarative representations of directed graphical models in TensorFlow Probability.

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