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 and , we are interested in the problem of determining from finite data whether or not is . 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}
}