Title: Adaptive and Scalable Sequential Detection Rules
Georgios Fellouris, UIUC Statistics
IMSE Seminar, April 16, noon, Grainger 329
Abstract: In this talk, I will discuss the problem of signal detection when observations are sequentially acquired from a “large” number of sources and the (unknown) subset of sources in which signal is present is “small”. I will propose a class of sequential detection rules that are characterized by adaptiveness, in the sense that they are asymptotically optimal under any scenario for the subset of affected sources, and scalability, in the sense that the operations required for the computation of the corresponding test statistic at any given time scales linearly with the number of sources.
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