ML Anthology

Pyro: Deep Universal Probabilistic Programming

Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, Noah D. Goodman
MLOSS 2019 pp. 1-6
/mloss/2019/bingham2019jmlr-pyro/

Abstract

Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large data sets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.

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Cite

Text

Bingham et al. "Pyro: Deep Universal Probabilistic Programming." Machine Learning Open Source Software, 2019.

Markdown

[Bingham et al. "Pyro: Deep Universal Probabilistic Programming." Machine Learning Open Source Software, 2019.](https://mlanthology.org/mloss/2019/bingham2019jmlr-pyro/)

BibTeX

@article{bingham2019jmlr-pyro,
  title     = {{Pyro: Deep Universal Probabilistic Programming}},
  author    = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and Horsfall, Paul and Goodman, Noah D.},
  journal   = {Machine Learning Open Source Software},
  year      = {2019},
  pages     = {1-6},
  volume    = {20},
  url       = {https://mlanthology.org/mloss/2019/bingham2019jmlr-pyro/}
}