# Asymptotic Classification Error for Heavy-Tailed Renewal Processes

arXiv: https://arxiv.org/abs/2408.10502 · HTML: https://mlsanity.com/paper/2408.10502/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach](https://mlsanity.com/paper/1810.09920/) (2018)
- [Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models](https://mlsanity.com/paper/2201.05044/) (2022)
- [Bayes Risk Consistency of Nonparametric Classification Rules for Spike Trains Data](https://mlsanity.com/paper/2308.04796/) (2023)
- [Asymptotic Error Rates for Point Process Classification](https://mlsanity.com/paper/2403.12531/) (2024)

## Abstract (arXiv)

Despite the widespread occurrence of classification problems and the increasing collection of point process data across many disciplines, study of error probability for point process classification only emerged very recently. Here, we consider classification of renewal processes. We obtain asymptotic expressions for the Bhattacharyya bound on misclassification error probabilities for heavy-tailed renewal processes.