2025/11/06 by Chen, Wuxia, Moushegian, Sean, Tarokh, Vahid +1
#FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Methodology (stat.ME) #Signal Processing (eess.SP) #Statistics Theory (math.ST) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2511.03967
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.