Asymptotically Optimal Quickest Change Detection In Multistream Data - Part 1: General Stochastic Models
Alexander Tartakovsky

TL;DR
This paper develops asymptotically optimal Bayesian procedures for quickest change detection across multiple dependent data streams with unknown affected subset and post-change parameters, extending single-stream methods.
Contribution
It introduces two mixture-based sequential detection rules for multistream data with unknown change characteristics, generalizing previous single-stream approaches.
Findings
Proposed procedures are asymptotically optimal as false alarm probability approaches zero.
Conditions established for first-order asymptotic optimality.
Extension of single-stream change detection methods to multistream scenarios.
Abstract
Assume that there are multiple data streams (channels, sensors) and in each stream the process of interest produces generally dependent and non-identically distributed observations. When the process is in a normal mode (in-control), the (pre-change) distribution is known, but when the process becomes abnormal there is a parametric uncertainty, i.e., the post-change (out-of-control) distribution is known only partially up to a parameter. Both the change point and the post-change parameter are unknown. Moreover, the change affects an unknown subset of streams, so that the number of affected streams and their location are unknown in advance. A good changepoint detection procedure should detect the change as soon as possible after its occurrence while controlling for a risk of false alarms. We consider a Bayesian setup with a given prior distribution of the change point and propose two…
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