Probability Theory of Random Polygons from the Quaternionic Viewpoint

TL;DR

This paper introduces a new probability measure on space polygons using quaternionic Hopf maps, enabling explicit calculations of geometric properties and efficient sampling, differing from traditional Gaussian-based models.

Contribution

It constructs a novel quaternionic-based probability measure on polygons, allowing explicit expectation calculations and fast sampling, with potential applications in studying various polygon measures.

Findings

Edgelengths follow beta distributions under the new measures.

Explicit formulas for expectations of chordlengths and radii of gyration.

Sampling method is linear in the number of edges and computationally efficient.

Abstract

We build a new probability measure on closed space and plane polygons. The key construction is a map, given by Knutson and Hausmann using the Hopf map on quaternions, from the complex Stiefel manifold of 2-frames in n-space to the space of closed n-gons in 3-space of total length 2. Our probability measure on polygon space is defined by pushing forward Haar measure on the Stiefel manifold by this map. A similar construction yields a probability measure on plane polygons which comes from a real Stiefel manifold. The edgelengths of polygons sampled according to our measures obey beta distributions. This makes our polygon measures different from those usually studied, which have Gaussian or fixed edgelengths. One advantage of our measures is that we can explicitly compute expectations and moments for chordlengths and radii of gyration. Another is that direct sampling according to our measures is fast (linear in the number of edges) and easy to code. Some of our methods will be of independent interest in studying other probability measures on polygon spaces. We define an edge set ensemble (ESE) to be the set of polygons created by rearranging a given set of n edges. A key theorem gives a formula for the average over an ESE of the squared lengths of chords skipping k vertices in terms of k, n, and the edgelengths of the ensemble. This allows one to easily compute expected values of squared chordlengths and radii of gyration for any probability measure on polygon space invariant under rearrangements of edges.