# Variational Bayes: A report on approaches and applications

arXiv: https://arxiv.org/abs/1905.10744 · HTML: https://mlsanity.com/paper/1905.10744/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 1 of 1)

- [Semantic Variational Bayes Based on Semantic Information G Theory for Solving Latent Variables](https://mlsanity.com/paper/2408.13122/) (2024)

## Abstract (arXiv)

Deep neural networks have achieved impressive results on a wide variety of tasks. However, quantifying uncertainty in the network's output is a challenging task. Bayesian models offer a mathematical framework to reason about model uncertainty. Variational methods have been used for approximating intractable integrals that arise in Bayesian inference for neural networks. In this report, we review the major variational inference concepts pertinent to Bayesian neural networks and compare various approximation methods used in literature. We also talk about the applications of variational bayes in Reinforcement learning and continual learning.