Bijectors.jl: Flexible transformations for probability distributions

Tor Erlend Fjelde, Kai Xu, Mohamed Tarek, Sharan Yalburgi, Hong Ge
; Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, PMLR 118:1-17, 2020.

Abstract

Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning. Some prominent examples are constrained-to-unconstrained transformations of distributions for use in Hamiltonian Monte Carlo and constructing exible and learnable densities such as normalizing ows. We present Bijectors.jl, a software package in Julia for transforming distributions, available at github.com/TuringLang/Bijectors.jl. The package provides a exible and composable way of implementing transformations of distributions without being tied to a computational framework. We demonstrate the use of Bijectors.jl on improving variational inference by encoding known statistical dependencies into the variational posterior using normalizing ows, providing a general approach to relaxing the mean-field assumption usually made in variational inference.

Cite this Paper


BibTeX
@InProceedings{pmlr-v118-fjelde20a, title = { Bijectors.jl: Flexible transformations for probability distributions}, author = {Fjelde, Tor Erlend and Xu, Kai and Tarek, Mohamed and Yalburgi, Sharan and Ge, Hong}, pages = {1--17}, year = {2020}, editor = {Cheng Zhang and Francisco Ruiz and Thang Bui and Adji Bousso Dieng and Dawen Liang}, volume = {118}, series = {Proceedings of Machine Learning Research}, address = {}, month = {08 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v118/fjelde20a/fjelde20a.pdf}, url = {http://proceedings.mlr.press/v118/fjelde20a.html}, abstract = {Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning. Some prominent examples are constrained-to-unconstrained transformations of distributions for use in Hamiltonian Monte Carlo and constructing exible and learnable densities such as normalizing ows. We present Bijectors.jl, a software package in Julia for transforming distributions, available at github.com/TuringLang/Bijectors.jl. The package provides a exible and composable way of implementing transformations of distributions without being tied to a computational framework. We demonstrate the use of Bijectors.jl on improving variational inference by encoding known statistical dependencies into the variational posterior using normalizing ows, providing a general approach to relaxing the mean-field assumption usually made in variational inference. } }
Endnote
%0 Conference Paper %T Bijectors.jl: Flexible transformations for probability distributions %A Tor Erlend Fjelde %A Kai Xu %A Mohamed Tarek %A Sharan Yalburgi %A Hong Ge %B Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference %C Proceedings of Machine Learning Research %D 2020 %E Cheng Zhang %E Francisco Ruiz %E Thang Bui %E Adji Bousso Dieng %E Dawen Liang %F pmlr-v118-fjelde20a %I PMLR %J Proceedings of Machine Learning Research %P 1--17 %U http://proceedings.mlr.press %V 118 %W PMLR %X Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning. Some prominent examples are constrained-to-unconstrained transformations of distributions for use in Hamiltonian Monte Carlo and constructing exible and learnable densities such as normalizing ows. We present Bijectors.jl, a software package in Julia for transforming distributions, available at github.com/TuringLang/Bijectors.jl. The package provides a exible and composable way of implementing transformations of distributions without being tied to a computational framework. We demonstrate the use of Bijectors.jl on improving variational inference by encoding known statistical dependencies into the variational posterior using normalizing ows, providing a general approach to relaxing the mean-field assumption usually made in variational inference.
APA
Fjelde, T.E., Xu, K., Tarek, M., Yalburgi, S. & Ge, H.. (2020). Bijectors.jl: Flexible transformations for probability distributions. Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, in PMLR 118:1-17

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