English

Testing to distinguish measures on metric spaces

Methodology 2018-02-06 v1 Computational Geometry Machine Learning

Abstract

We study the problem of distinguishing between two distributions on a metric space; i.e., given metric measure spaces (X,d,μ1)({\mathbb X}, d, \mu_1) and (X,d,μ2)({\mathbb X}, d, \mu_2), we are interested in the problem of determining from finite data whether or not μ1\mu_1 is μ2\mu_2. The key is to use pairwise distances between observations and, employing a reconstruction theorem of Gromov, we can perform such a test using a two sample Kolmogorov--Smirnov test. A real analysis using phylogenetic trees and flu data is presented.

Cite

@article{arxiv.1802.01152,
  title  = {Testing to distinguish measures on metric spaces},
  author = {Andrew J. Blumberg and Prithwish Bhaumik and Stephen G. Walker},
  journal= {arXiv preprint arXiv:1802.01152},
  year   = {2018}
}
R2 v1 2026-06-23T00:10:14.231Z