Joint Distributions for TensorFlow Probability
arXiv:2001.11819v117 citations
Originality Synthesis-oriented
AI Analysis
This provides a tool for probabilistic programming users to streamline model specification and inference, but it is incremental as it builds on existing frameworks.
The authors introduced JointDistributions, a family of declarative representations for directed graphical models in TensorFlow Probability, to enable models to be specified once and used by inference algorithms.
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.