Learning Approximately Objective Priors

Eric Nalisnick, Padhraic Smyth
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:491-500, 2017.

Abstract

Informative Bayesian priors are often difficult to elicit, and when this is the case, modelers usually turn to noninformative or objective pri- ors. However, objective priors such as the Jef- freys and reference priors are not tractable to derive for many models of interest. We address this issue by proposing techniques for learn- ing reference prior approximations: we select a parametric family and optimize a black-box lower bound on the reference prior objective to find the member of the family that serves as a good approximation. We experimentally demonstrate the method’s effectiveness by re- covering Jeffreys priors and learning the Vari- ational Autoencoder’s reference prior.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-nalisnick17a, title = {Learning Approximately Objective Priors}, author = {Nalisnick, Eric and Smyth, Padhraic}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {491--500}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/nalisnick17a/nalisnick17a.pdf}, url = {https://proceedings.mlr.press/r15/nalisnick17a.html}, abstract = {Informative Bayesian priors are often difficult to elicit, and when this is the case, modelers usually turn to noninformative or objective pri- ors. However, objective priors such as the Jef- freys and reference priors are not tractable to derive for many models of interest. We address this issue by proposing techniques for learn- ing reference prior approximations: we select a parametric family and optimize a black-box lower bound on the reference prior objective to find the member of the family that serves as a good approximation. We experimentally demonstrate the method’s effectiveness by re- covering Jeffreys priors and learning the Vari- ational Autoencoder’s reference prior.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Learning Approximately Objective Priors %A Eric Nalisnick %A Padhraic Smyth %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-nalisnick17a %I PMLR %P 491--500 %U https://proceedings.mlr.press/r15/nalisnick17a.html %V R15 %X Informative Bayesian priors are often difficult to elicit, and when this is the case, modelers usually turn to noninformative or objective pri- ors. However, objective priors such as the Jef- freys and reference priors are not tractable to derive for many models of interest. We address this issue by proposing techniques for learn- ing reference prior approximations: we select a parametric family and optimize a black-box lower bound on the reference prior objective to find the member of the family that serves as a good approximation. We experimentally demonstrate the method’s effectiveness by re- covering Jeffreys priors and learning the Vari- ational Autoencoder’s reference prior. %Z Reissued by PMLR on 04 October 2026.
APA
Nalisnick, E. & Smyth, P.. (2017). Learning Approximately Objective Priors. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:491-500 Available from https://proceedings.mlr.press/r15/nalisnick17a.html. Reissued by PMLR on 04 October 2026.

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