2022/11/21 by Alice Le Brigant, Jules Deschamps, Brigant, Alice Le +5 · 1 citation
Computer Science · Environmental Science · Mathematics · #FOS: Computer and information sciences #Hydrology and Drought Analysis #Machine Learning (cs.LG) #Mathematical Software (cs.MS) #Probability and Statistical Research #Statistical and Computational Modeling
paper · pdf · doi:10.48550/arxiv.2211.11643
openalex publication_date 2022/11/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.