# Geometric and Probabilistic Limit Theorems in Topological Data Analysis

## TL;DR

This paper introduces a probabilistic framework for analyzing the topology of random point clouds, extending barcode concepts to compact metric spaces and proving convergence results for i.i.d. samples.

## Contribution

It generalizes barcode analysis to compact metric spaces and establishes convergence theorems for random samples, advancing the mathematical foundation of topological data analysis.

## Key findings

- Barcodes of i.i.d. samples converge to the barcode of the support
- Quantitative convergence results for uniform sampling from manifolds
- Expectations of barcode transforms in Banach spaces

## Abstract

We develop a general framework for the probabilistic analysis of random finite point clouds in the context of topological data analysis. We extend the notion of a barcode of a finite point cloud to compact metric spaces. Such a barcode lives in the completion of the space of barcodes with respect to the bottleneck distance, which is quite natural from an analytic point of view. As an application we prove that the barcodes of i.i.d. random variables sampled from a compact metric space converge to the barcode of the support of their distribution when the number of points goes to infinity. We also examine more quantitative convergence questions for uniform sampling from compact manifolds, including expectations of transforms of barcode valued random variables in Banach spaces. We believe that the methods developed here will serve as useful tools in studying more sophisticated questions in topological data analysis and related fields.