Joint Distributions for TensorFlow Probability

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Jan 22, 2020
Dan Piponi, Dave Moore, Joshua V. Dillon
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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.

* Based on extended abstract submitted to PROBPROG 2020  
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