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Priors in Bayesian Deep Learning: A Review

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  • Vincent Fortuin

Abstract

While the choice of prior is one of the most critical parts of the Bayesian inference workflow, recent Bayesian deep learning models have often fallen back on vague priors, such as standard Gaussians. In this review, we highlight the importance of prior choices for Bayesian deep learning and present an overview of different priors that have been proposed for (deep) Gaussian processes, variational autoencoders and Bayesian neural networks. We also outline different methods of learning priors for these models from data. We hope to motivate practitioners in Bayesian deep learning to think more carefully about the prior specification for their models and to provide them with some inspiration in this regard.

Suggested Citation

  • Vincent Fortuin, 2022. "Priors in Bayesian Deep Learning: A Review," International Statistical Review, International Statistical Institute, vol. 90(3), pages 563-591, December.
    • Handle: RePEc:bla:istatr:v:90:y:2022:i:3:p:563-591
    DOI: 10.1111/insr.12502
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    References listed on IDEAS

    as
    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Simone Cerreia-Vioglio & Lars Peter Hansen & Fabio Maccheroni & Massimo Marinacci, 2020. "Making Decisions under Model Misspecification," Papers 2008.01071, arXiv.org, revised Aug 2022.
    3. Anindya Bhadra & Jyotishka Datta & Nicholas G. Polson & Brandon Willard, 2016. "Default Bayesian analysis with global-local shrinkage priors," Biometrika, Biometrika Trust, vol. 103(4), pages 955-969.
    4. repec:dau:papers:123456789/1908 is not listed on IDEAS
    5. Andreas Kopf & Vincent Fortuin & Vignesh Ram Somnath & Manfred Claassen, 2021. "Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-17, June.
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