Score-based methods have recently seen increasing popularity in modeling and generation. Methods have been constructed to perform hypothesis testing and change-point detection with score functions, but these methods are in general not as powerful as their likelihood-based peers. Recent works consider generalizing the score-based Fisher divergence into a diffusion-divergence by transforming score functions via multiplication with a matrix-valued function or a weight matrix. In this paper, we extend the score-based hypothesis test and change-point detection stopping rule into their diffusion-based analogs. Additionally, we theoretically quantify the performance of these diffusion-based algorithms and study scenarios where optimal performance is achievable. We propose a method of numerically optimizing the weight matrix and present numerical simulations to illustrate the advantages of diffusion-based algorithms.
View on arXiv@article{moushegian2025_2506.16089,
title={ Diffusion-Based Hypothesis Testing and Change-Point Detection },
author={ Sean Moushegian and Taposh Banerjee and Vahid Tarokh },
journal={arXiv preprint arXiv:2506.16089},
year={ 2025 }
}