# Laplace Redux - Effortless Bayesian Deep Learning

@article{Daxberger2021LaplaceR, title={Laplace Redux - Effortless Bayesian Deep Learning}, author={Erik A. Daxberger and Agustinus Kristiadi and Alexander Immer and Runa Eschenhagen and Matthias Bauer and Philipp Hennig}, journal={ArXiv}, year={2021}, volume={abs/2106.14806} }

Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection. The Laplace approximation (LA) is a classic, and arguably the simplest family of approximations for the intractable posteriors of deep neural networks. Yet, despite its simplicity, the LA is not as popular as alternatives like variational Bayes or deep ensembles. This may be due to…

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