# Parametric information geometry with the package Geomstats

arXiv: https://arxiv.org/abs/2211.11643 · HTML: https://mlsanity.com/paper/2211.11643/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 44 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [geomstats: a Python Package for Riemannian Geometry in Machine Learning](https://mlsanity.com/paper/1805.08308/) (2018)
- [Geomstats: A Python Package for Riemannian Geometry in Machine Learning](https://mlsanity.com/paper/2004.04667/) (2020)
- [Classifying histograms of medical data using information geometry of beta distributions](https://mlsanity.com/paper/2006.04511/) (2020)

## Built on by (most cited first, 3 of 3)

- [Approximation and bounding techniques for the Fisher-Rao distances between parametric statistical models](https://mlsanity.com/paper/2403.10089/) (2024)
- [pyBregMan: A Python library for Bregman Manifolds](https://mlsanity.com/paper/2408.04175/) (2024)
- [TAGTorch: A PyTorch Library for Geometry, Topology, and Symmetry-Aware Machine Learning](https://mlsanity.com/paper/2607.28755/) (2026)

## Abstract (arXiv)

We introduce the information geometry module of the Python package Geomstats. The module first implements Fisher-Rao Riemannian manifolds of widely used parametric families of probability distributions, such as normal, gamma, beta, Dirichlet distributions, and more. The module further gives the Fisher-Rao Riemannian geometry of any parametric family of distributions of interest, given a parameterized probability density function as input. The implemented Riemannian geometry tools allow users to compare, average, interpolate between distributions inside a given family. Importantly, such capabilities open the door to statistics and machine learning on probability distributions. We present the object-oriented implementation of the module along with illustrative examples and show how it can be used to perform learning on manifolds of parametric probability distributions.