Equivalence of weak and strong modes of measures on topological vector spaces

TL;DR

This paper investigates the conditions under which weak and strong modes of measures on topological vector spaces are equivalent, introducing a new mode concept and exploring implications for Bayesian inference.

Contribution

It establishes the equivalence of weak and strong modes under certain conditions and introduces an intermediate mode, clarifying their relationship in topological vector spaces.

Findings

Density of subspace E is crucial for mode equivalence

Uniformity condition ensures the equivalence of modes

Failure of uniformity leads to inequivalence of modes

Abstract

A strong mode of a probability measure on a normed space $X$ can be defined as a point $u$ such that the mass of the ball centred at $u$ uniformly dominates the mass of all other balls in the small-radius limit. Helin and Burger weakened this definition by considering only pairwise comparisons with balls whose centres differ by vectors in a dense, proper linear subspace $E$ of $X$, and posed the question of when these two types of modes coincide. We show that, in a more general setting of metrisable vector spaces equipped with measures that are finite on bounded sets, the density of $E$ and a uniformity condition suffice for the equivalence of these two types of modes. We accomplish this by introducing a new, intermediate type of mode. We also show that these modes can be inequivalent if the uniformity condition fails. Our results shed light on the relationships between among various notions of maximum a posteriori estimator in non-parametric Bayesian inference.