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- W4309850954 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." @default.
- W4309850954 created "2022-11-29" @default.
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- W4309850954 date "2023-10-13" @default.
- W4309850954 modified "2023-10-14" @default.
- W4309850954 title "Parametric information geometry with the package Geomstats" @default.
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- W4309850954 doi "https://doi.org/10.1145/3627538" @default.
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