In this chapter, we review the foundations of statistical inference over adaptive networks by considering two canonical problems: distributed estimation and distributed detection. In the former setting, agents cooperate to estimate a model of interest while in the second setting, the agents cooperate to detect a state of nature. We focus on adaptive learning solutions where agents are able to track drifts in the underlying models, and examine performance limits under both estimation and detection formulations. Special attention is paid to the detailed characterization of the steady-state performance. Certain universal laws are highlighted and compared against known laws for estimation and detection in traditional (centralized or decentralized, nonadaptive) inferential systems.

Estimation and Detection Over Adaptive Networks

Matta V.;
2018-01-01

Abstract

In this chapter, we review the foundations of statistical inference over adaptive networks by considering two canonical problems: distributed estimation and distributed detection. In the former setting, agents cooperate to estimate a model of interest while in the second setting, the agents cooperate to detect a state of nature. We focus on adaptive learning solutions where agents are able to track drifts in the underlying models, and examine performance limits under both estimation and detection formulations. Special attention is paid to the detailed characterization of the steady-state performance. Certain universal laws are highlighted and compared against known laws for estimation and detection in traditional (centralized or decentralized, nonadaptive) inferential systems.
9780128136775
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4750894
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