Amari's Information Geometry is a dually affine formalism for parametric probability models. The literature proposes various nonparametric functional versions. Our approach uses classical Weyl's axioms so that the affine velocity of a one-parameter statistical model equals the classical Fisher's score. In the present note, we first offer a concise review of the notion of a statistical bundle as a set of couples of probability densities and Fisher's scores. Then, we show how the nonparametric dually affine setup deals with the basic Bayes and Kullback-Leibler divergence computations.

翻译：Amari的信息几何是参数概率模型的对偶仿射形式体系。文献中提出了多种非参数函数版本。我们的方法采用经典的Weyl公理，使得单参数统计模型的仿射速度等于经典的Fisher得分。在本研究中，我们首先简要回顾统计丛作为概率密度与Fisher得分对集合的概念。随后，我们展示非参数对偶仿射框架如何处理基本的贝叶斯计算与Kullback-Leibler散度计算。