PyMC4: Exploiting Coroutines for Implementing a Probabilistic Programming Framework
Abstract
PyMC4 is an open-source probabilistic programming library whose goal is to give users access to cutting-edge algorithms in Bayesian statistical computing while being extensible enough to help researchers to implement novel algorithms. Like its predecessor PyMC3 and the C++ library Stan, the aim of PyMC4 is to provide users with a high-level API to specify probabilistic models. Our focus is on Bayesian models where inference can be performed using (dynamic) Hamiltonian Monte Carlo and variational inference, both of which require automatic differentiation of user-defined joint density functions.
Cite
Text
Kochurov et al. "PyMC4: Exploiting Coroutines for Implementing a Probabilistic Programming Framework." NeurIPS 2019 Workshops: Program_Transformations, 2019.Markdown
[Kochurov et al. "PyMC4: Exploiting Coroutines for Implementing a Probabilistic Programming Framework." NeurIPS 2019 Workshops: Program_Transformations, 2019.](https://mlanthology.org/neuripsw/2019/kochurov2019neuripsw-pymc4/)BibTeX
@inproceedings{kochurov2019neuripsw-pymc4,
title = {{PyMC4: Exploiting Coroutines for Implementing a Probabilistic Programming Framework}},
author = {Kochurov, Max and Carroll, Colin and Wiecki, Thomas and Lao, Junpeng},
booktitle = {NeurIPS 2019 Workshops: Program_Transformations},
year = {2019},
url = {https://mlanthology.org/neuripsw/2019/kochurov2019neuripsw-pymc4/}
}