On a method to construct exponential families by representation theory
Representation Theory
2019-07-10 v1 Machine Learning
Machine Learning
Abstract
Exponential family plays an important role in information geometry. In arXiv:1811.01394, we introduced a method to construct an exponential family on a homogeneous space from a pair . Here is a representation of and is an -fixed vector in . Then the following questions naturally arise: (Q1) when is the correspondence injective? (Q2) when do distinct pairs and generate the same family? In this paper, we answer these two questions (Theorems 1 and 2). Moreover, in Section 3, we consider the case with a certain representation on . Then we see the family obtained by our method is essentially generalized inverse Gaussian distribution (GIG).
Cite
@article{arxiv.1907.04212,
title = {On a method to construct exponential families by representation theory},
author = {Koichi Tojo and Taro Yoshino},
journal= {arXiv preprint arXiv:1907.04212},
year = {2019}
}
Comments
Conference paper at Geometric Science of Information 2019