# Natural Gradient Variational Inference with Gaussian Mixture Models

arXiv: https://arxiv.org/abs/2111.08002 · HTML: https://mlsanity.com/paper/2111.08002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 7 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013)
- [Practical Deep Learning with Bayesian Principles](https://mlsanity.com/paper/1906.02506/) (2019)
- [Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam](https://mlsanity.com/paper/1806.04854/) (2018)
- [The Information Geometry of Mirror Descent](https://mlsanity.com/paper/1310.7780/) (2013)
- [Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations](https://mlsanity.com/paper/1906.02914/) (2019)

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

- [Robust and Scalable Variational Bayes](https://mlsanity.com/paper/2504.12528/) (2025)

## Abstract (arXiv)

Bayesian methods estimate a measure of uncertainty by using the posterior distribution. One source of difficulty in these methods is the computation of the normalizing constant. Calculating exact posterior is generally intractable and we usually approximate it. Variational Inference (VI) methods approximate the posterior with a distribution usually chosen from a simple family using optimization. The main contribution of this work is described is a set of update rules for natural gradient variational inference with mixture of Gaussians, which can be run independently for each of the mixture components, potentially in parallel.