Quickest change detection for multi-task problems under unknown parameters
We consider the quickest change detection problem where both the parameters of pre- and post- change distributions are unknown, which prevent the use of classical simple hypothesis testing. Without additional assumptions, optimal solutions are not tractable as they rely on some minimax and robust variant of the objective. As a consequence, change points might be detected too late for practical applications (in economics, health care or maintenance for instance). Other approaches solve a relaxed version of the problem through the use of particular probability distributions or the use of domain knowledge. We tackle this problem in the more complex Markovian case and we provide a new scalable approximate algorithm with near optimal performance that runs in $\mathcal{O}(1)$.
Code (0)
등록된 구현이 없습니다.
Tasks
Change DetectionTwo-sample testingSimilar Papers 제목 키워드 기반
Data-Driven Quickest Change Detection in (Hidden) Markov Models
The paper investigates the problems of quickest change detection in Markov models and hidden Markov models (HMMs). Sequential observations are taken from a (hidden) Markov model. At some unknown time, an event occurs in …
Change DetectionFault DetectionQuickest Intruder Detection for Multiple User Active Authentication
In this paper, we investigate how to detect intruders with low latency for Active Authentication (AA) systems with multiple-users. We extend the Quickest Change Detection (QCD) framework to the multiple-user case and for…
Change DetectionQuickest Change Detection of Time Inconsistent Anticipatory Agents. Human-Sensor and Cyber-Physical Systems
In behavioral economics, human decision makers are modeled as anticipatory agents that make decisions by taking into account the probability of future decisions (plans). We consider cyber-physical systems involving the i…
Change DetectionDecision MakingRobust Score-Based Quickest Change Detection
Methods in the field of quickest change detection rapidly detect in real-time a change in the data-generating distribution of an online data stream. Existing methods have been able to detect this change point when the de…
Change DetectionQuickest Change Detection for Unnormalized Statistical Models
Classical quickest change detection algorithms require modeling pre-change and post-change distributions. Such an approach may not be feasible for various machine learning models because of the complexity of computing th…
Change Detection