A Maxwell principle for generalized Orlicz balls

TL;DR

This paper generalizes classical results on high-dimensional projections by studying random vectors on sets defined via a potential function, revealing a phase transition in their coordinate distributions.

Contribution

It introduces a unifying framework for analyzing projections of vectors on generalized Orlicz balls using large deviation principles, extending previous Gaussian and exponential models.

Findings

Identifies a critical parameter for phase transition in coordinate behavior.

Shows a shift from uniform to Gibbs-like distribution based on the parameter.

Provides a new perspective connecting geometry and probability via large deviations.

Abstract

In [A dozen de {F}inetti-style results in search of a theory, Ann. Inst. H. Poincar\'{e} Probab. Statist. 23(2)(1987), 397--423], Diaconis and Freedman studied low-dimensional projections of random vectors from the Euclidean unit sphere and the simplex in high dimensions, noting that the individual coordinates of these random vectors look like Gaussian and exponential random variables respectively. In subsequent works, Rachev and R\"uschendorf and Naor and Romik unified these results by establishing a connection between $\ell_p^N$ balls and a $p$-generalized Gaussian distribution. In this paper, we study similar questions in a significantly generalized and unifying setting, looking at low-dimensional projections of random vectors uniformly distributed on sets of the form \[B_{\phi,t}^N := \Big\{(s_1,\ldots,s_N)\in\mathbb{R}^N : \sum_{ i =1}^N\phi(s_i)\leq t N\Big\},\] where $\phi:\mathbb{R}\to [0,\infty]$ is a potential (including the case of Orlicz functions). Our method is different from both Rachev-R\"uschendorf and Naor-Romik, based on a large deviation perspective in the form of quantitative versions of Cram\'er's theorem and the Gibbs conditioning principle, providing a natural framework beyond the $p$-generalized Gaussian distribution while simultaneously unraveling the role this distribution plays in relation to the geometry of $\ell_p^N$ balls. We find that there is a critical parameter $t_{\mathrm{crit}}$ at which there is a phase transition in the behaviour of the projections: for $t > t_{\mathrm{crit}}$ the coordinates of random points sampled from $B_{\phi,t}^N$ behave like uniform random variables, but for $t \leq t_{\mathrm{crit}}$ the Gibbs conditioning principle comes into play, and here there is a parameter $\beta_t>0$ (the inverse temperature) such that the coordinates are approximately distributed according to a density proportional to $e^{ -\beta_t\phi(s)}$.