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Parametric information geometry with the package Geomstats
- Publication Year :
- 2022
- Publisher :
- HAL CCSD, 2022.
-
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.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
[INFO.INFO-MS] Computer Science [cs]/Mathematical Software [cs.MS]
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
[MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG]
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
Computer Science - Mathematical Software
[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]
[MATH.MATH-DG] Mathematics [math]/Differential Geometry [math.DG]
Mathematical Software (cs.MS)
[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
Machine Learning (cs.LG)
[INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS]
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....c052d807951fc9d32837ef769bfc6ef3