# Variational Bayes Made Easy

arXiv: https://arxiv.org/abs/2304.14251 · HTML: https://mlsanity.com/paper/2304.14251/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [The Bayesian Learning Rule](https://mlsanity.com/paper/2107.04562/) (2021)
- [Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models](https://mlsanity.com/paper/1703.04265/) (2017)
- [Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations](https://mlsanity.com/paper/1906.02914/) (2019)
- [Tractable structured natural gradient descent using local parameterizations](https://mlsanity.com/paper/2102.07405/) (2021)
- [Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions](https://mlsanity.com/paper/1511.00146/) (2015)
- [The Lie-Group Bayesian Learning Rule](https://mlsanity.com/paper/2303.04397/) (2023)
- [On the Convergence of Stochastic Variational Inference in Bayesian Networks](https://mlsanity.com/paper/1507.04505/) (2015)

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

- [The Memory Perturbation Equation: Understanding Model's Sensitivity to Data](https://mlsanity.com/paper/2310.19273/) (2023)

## Abstract (arXiv)

Variational Bayes is a popular method for approximate inference but its derivation can be cumbersome. To simplify the process, we give a 3-step recipe to identify the posterior form by explicitly looking for linearity with respect to expectations of well-known distributions. We can then directly write the update by simply ``reading-off'' the terms in front of those expectations. The recipe makes the derivation easier, faster, shorter, and more general.