Joint Distributions for TensorFlow Probability
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jan 2020.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1.
4 papers.
- 2021Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Modelscited by 4
- 2023Automatically Marginalized MCMC in Probabilistic Programmingcited by 4
- 2021Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modelingcited by 3
- 2021Pixyz: a Python library for developing deep generative modelscited by 0
- 2023Automatically Marginalized MCMC in Probabilistic Programming1 Feb 2023
- 2021Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling12 Oct 2021
- 2021Pixyz: a Python library for developing deep generative models28 Jul 2021
- 2021Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models31 May 2021