# Joint Distributions for TensorFlow Probability

arXiv: https://arxiv.org/abs/2001.11819 · HTML: https://mlsanity.com/paper/2001.11819/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models](https://mlsanity.com/paper/2106.00120/) (2021)
- [Automatically Marginalized MCMC in Probabilistic Programming](https://mlsanity.com/paper/2302.00564/) (2023)
- [Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling](https://mlsanity.com/paper/2110.06021/) (2021)
- [Pixyz: a Python library for developing deep generative models](https://mlsanity.com/paper/2107.13109/) (2021)

## Abstract (arXiv)

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.