Abstract
We develop a family of infinite-dimensional (non-parametric) manifolds of probability measures. The latter are defined on underlying Banach spaces, and have densities of class Cbk with respect to appropriate reference measures. The case k = ∞, in which the manifolds are modelled on Fréchet spaces, is included. The manifolds admit the Fisher-Rao metric and, unusually for the non-parametric setting, Amari’s α-covariant derivatives for all α ∈ ℝ. By construction, they are C∞-embedded submanifolds of particular manifolds of finite measures. The statistical manifolds are dually (α = ±1) flat, and admit mixture and exponential representations as charts. Their curvatures with respect to the α-covariant derivatives are derived. The likelihood function associated with a finite sample is a continuous function on each of the manifolds, and the α-divergences are of class C∞.
Subject
Statistics and Probability
Reference28 articles.
1. Amari S.-I. and
Nagaoka H.,
Methods of Information Geometry. Vol. 191 of Translations of Mathematical Monographs.
American Mathematical Society,
Providence
(2000).
2. Information geometry and sufficient statistics
3. Parametrized measure models
4. Barndorff-Nielsen O.E.,
Information and Exponential Families in Statistical Theory.
Wiley
(1978).
5. Uniqueness of the Fisher–Rao metric on the space of smooth densities
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