Diffusion-Based Hypothesis Testing and Change-Point Detection

Sean Moushegian; Taposh Banerjee; Vahid Tarokh

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arXiv · 2506.16089

Diffusion-Based Hypothesis Testing and Change-Point Detection

Sean Moushegian·Taposh Banerjee·Vahid Tarokh

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Abstract

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.

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BibTeX RIS

Sean Moushegian, Taposh Banerjee, Vahid Tarokh. 2025-06-19. Diffusion-Based Hypothesis Testing and Change-Point Detection. https://arxiv.org/abs/2506.16089

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