Drawing with Distance

TL;DR

This paper explores the geometric structure of drawing models in probability theory using Kantorovich distances, demonstrating isometries and limits that unify classical models through a metric perspective.

Contribution

It introduces a novel metric framework for analyzing drawing operations, establishing their isometric properties and limits in the context of Kantorovich distances.

Findings

Drawing operations are isometries preserving Kantorovich distances.

As urn size increases, hypergeometric and multinomial distances converge to zero.

As draw size increases, the distance between urns and normalized multinomial draws converges to zero.

Abstract

Drawing (a multiset of) coloured balls from an urn is one of the most basic models in discrete probability theory. Three modes of drawing are commonly distinguished: multinomial (draw-replace), hypergeometric (draw-delete), and Polya (draw-add). These drawing operations are represented as maps from urns to distributions over multisets of draws. The set of urns is a metric space via the Kantorovich distance. The set of distributions over draws is also a metric space, using Kantorovich-over-Kantorovich. It is shown that these three draw operations are all isometries, that is, they exactly preserve the Kantorovich distances. Further, drawing is studied in the limit, both for large urns and for large draws. First it is shown that, as the urn size increases, the Kantorovich distances go to zero between hypergeometric and multinomial draws, and also between P\'olya and multinomial draws. Second, it is shown that, as the drawsize increases, the Kantorovich distance goes to zero (in probability) between an urn and (normalised) multinomial draws from the urn. These results are known, but here, they are formulated in a novel metric manner as limits of Kantorovich distances. We call these two limit results the law of large urns and the law of large draws.