{"ID":2845130,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03967","arxiv_id":"2511.03967","title":"Score-Based Quickest Change Detection and Fault Identification for Multi-Stream Signals","abstract":"This paper introduces an approach to multi-stream quickest change detection and fault isolation for unnormalized and score-based statistical models. Traditional optimal algorithms in the quickest change detection literature require explicit pre-change and post-change distributions to calculate the likelihood ratio of the observations, which can be computationally expensive for higher-dimensional data and sometimes even infeasible for complex machine learning models. To address these challenges, we propose the min-SCUSUM method, a Hyvarinen score-based algorithm that computes the difference of score functions in place of log-likelihood ratios. We provide a delay and false alarm analysis of the proposed algorithm, showing that its asymptotic performance depends on the Fisher divergence between the pre- and post-change distributions. Furthermore, we establish an upper bound on the probability of fault misidentification in distinguishing the affected stream from the unaffected ones.","short_abstract":"This paper introduces an approach to multi-stream quickest change detection and fault isolation for unnormalized and score-based statistical models. Traditional optimal algorithms in the quickest change detection literature require explicit pre-change and post-change distributions to calculate the likelihood ratio of t...","url_abs":"https://arxiv.org/abs/2511.03967","url_pdf":"https://arxiv.org/pdf/2511.03967v1","authors":"[\"Wuxia Chen\",\"Sean Moushegian\",\"Vahid Tarokh\",\"Taposh Banerjee\"]","published":"2025-11-06T01:40:43Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"math.ST\",\"stat.ME\"]","methods":"[]","has_code":false}