Parametric information geometry with the package Geomstats
Abstract
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.
Keywords
Cite
@article{arxiv.2211.11643,
title = {Parametric information geometry with the package Geomstats},
author = {Alice Le Brigant and Jules Deschamps and Antoine Collas and Nina Miolane},
journal= {arXiv preprint arXiv:2211.11643},
year = {2022}
}