# Bayesian Deep Learning: Uncertainty Quantification and Robust Predictions
Status: public
Confidence: medium (0.78) (verified)
Last verified: 2026-05-28
Generation: ai_structured


## TL;DR

Bayesian deep learning represents uncertainty in neural networks using approximate Bayesian methods such as variational weights and dropout. This repair maps claims to primary papers.

## Core Explanation

The sampled entry had low source coverage. This version keeps three canonical Bayesian deep learning facts.

## Further Reading

- [Weight Uncertainty in Neural Networks](https://arxiv.org/abs/1505.05424)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://arxiv.org/abs/1506.02142)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://arxiv.org/abs/1703.04977)