# On a prior based on the Wasserstein information matrix

arXiv: https://arxiv.org/abs/2202.03217 · HTML: https://mlsanity.com/paper/2202.03217/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2022.

## Built on by (most cited first, 2 of 2)

- [Neural FIM for learning Fisher Information Metrics from point cloud data](https://mlsanity.com/paper/2306.06062/) (2023)
- [Scaling Limits of the Wasserstein information matrix on Gaussian Mixture Models](https://mlsanity.com/paper/2309.12997/) (2023)

## Abstract (arXiv)

We introduce a prior for the parameters of univariate continuous distributions, based on the Wasserstein information matrix, which is invariant under reparameterisations. We discuss the links between the proposed prior with information geometry. We present sufficient conditions for the propriety of the posterior distribution for general classes of models. We present a simulation study that shows that the induced posteriors have good frequentist properties.