# The Inverse G-Wishart Distribution and Variational Message Passing

arXiv: https://arxiv.org/abs/2005.09876 · HTML: https://mlsanity.com/paper/2005.09876/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

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

- [A variational inference framework for inverse problems](https://mlsanity.com/paper/2103.05909/) (2021)

## Abstract (arXiv)

Message passing on a factor graph is a powerful paradigm for the coding of approximate inference algorithms for arbitrarily graphical large models. The notion of a factor graph fragment allows for compartmentalization of algebra and computer code. We show that the Inverse G-Wishart family of distributions enables fundamental variational message passing factor graph fragments to be expressed elegantly and succinctly. Such fragments arise in models for which approximate inference concerning covariance matrix or variance parameters is made, and are ubiquitous in contemporary statistics and machine learning.