# On the Validity of Bayesian Neural Networks for Uncertainty Estimation

arXiv: https://arxiv.org/abs/1912.01530 · HTML: https://mlsanity.com/paper/1912.01530/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 14 of 14)

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- [Diffusion Models for Implicit Image Segmentation Ensembles](https://mlsanity.com/paper/2112.03145/) (2021)
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- [When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem](https://mlsanity.com/paper/2511.17760/) (2025)
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- [IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data](https://mlsanity.com/paper/2405.07916/) (2024)
- [Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout](https://mlsanity.com/paper/2411.15944/) (2024)
- [Ensemble Distribution Distillation for Self-Supervised Human Activity Recognition](https://mlsanity.com/paper/2509.08225/) (2025)
- [Don't Collapse Your Features: Why CenterLoss Hurts OOD Detection and Multi-Scale Mahalanobis Wins](https://mlsanity.com/paper/2605.21493/) (2026)

## Abstract (arXiv)

Deep neural networks (DNN) are versatile parametric models utilised successfully in a diverse number of tasks and domains. However, they have limitations---particularly from their lack of robustness and over-sensitivity to out of distribution samples. Bayesian Neural Networks, due to their formulation under the Bayesian framework, provide a principled approach to building neural networks that address these limitations. This paper describes a study that empirically evaluates and compares Bayesian Neural Networks to their equivalent point estimate Deep Neural Networks to quantify the predictive uncertainty induced by their parameters, as well as their performance in view of this uncertainty. In this study, we evaluated and compared three point estimate deep neural networks against comparable Bayesian neural network alternatives using two well-known benchmark image classification datasets (CIFAR-10 and SVHN).