Multiple-Experiment Quickest Change Detection under Cost Constraints
Lubenia, Patrick Vincent N.; Banerjee, Taposh
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https://hdl.handle.net/2142/130302
Description
Title
Multiple-Experiment Quickest Change Detection under Cost Constraints
Author(s)
Lubenia, Patrick Vincent N.
Banerjee, Taposh
Issue Date
2025-09-17
Keyword(s)
Controlled sensing
CUSUM
Data-efficient
Experiment design
Multiple-experiment
Quickest change detection
Sampling control
Abstract
In the classical quickest change detection problem, an observer performs only one experiment to monitor a stochastic process. This paper considers the case where, at each observation time, the decision-maker needs to choose between multiple experiments with different information qualities and costs. The goal is to minimize the worst-case average detection delay subject to false alarm and cost constraints. An algorithm called the 2E-CUSUM algorithm is developed to achieve this goal for the two-experiment case. Multiple-experiment designs are also studied, and the 2E-CUSUM algorithm is extended accordingly. The proposed algorithms are shown to be asymptotically optimal.
Publisher
Allerton Conference on Communication, Control, and Computing
Series/Report Name or Number
2025 61st Allerton Conference on Communication, Control, and Computing Proceedings
ISSN
2836-4503
Type of Resource
Text
Genre of Resource
Conference Paper/Presentation
Language
eng
Handle URL
https://hdl.handle.net/2142/130302&&
Copyright and License Information
Copyright 2025 is held by Patrick Vincent N. Lubenia and Taposh Banerjee.
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