论文标题
带包装的参数信息几何形状
Parametric information geometry with the package Geomstats
论文作者
论文摘要
我们介绍了Python软件包GeomStats的信息几何模块。该模块首先实现了广泛使用的概率分布的参数族的Fisher-rao riemannian流形，例如正常，伽马，β，dirichlet分布等。如果参数化的概率密度函数作为输入，则该模块进一步赋予了任何关注分布的参数分布族的Fisher-Rao Riemannian几何学。实施的Riemannian几何工具允许用户比较给定家庭内部分布之间的平均值，插值。重要的是，这种功能为概率分布的统计和机器学习打开了大门。我们介绍了该模块的面向对象的实现以及说明性示例，并展示了如何用于对参数概率分布的流形执行学习。
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.
