English

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 P={pθ}θΘ\mathcal{P}=\{p_\theta\}_{\theta\in\Theta} on a homogeneous space G/HG/H from a pair (V,v0)(V,v_0). Here VV is a representation of GG and v0v_0 is an HH-fixed vector in VV. Then the following questions naturally arise: (Q1) when is the correspondence θpθ\theta\mapsto p_\theta injective? (Q2) when do distinct pairs (V,v0)(V,v_0) and (V,v0)(V',v_0') generate the same family? In this paper, we answer these two questions (Theorems 1 and 2). Moreover, in Section 3, we consider the case (G,H)=(R>0,{1})(G,H)=(\mathbb{R}_{>0}, \{1\}) with a certain representation on R2\mathbb{R}^2. 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

R2 v1 2026-06-23T10:16:17.348Z