Fisher information as a metric of locally optimal processing and stochastic resonance.

The origins of Fisher information are in its use as a performance measure for parametric estimation. We augment this and show that the Fisher information can characterize the performance in several other significant signal processing operations. For processing of a weak signal in additive white nois...

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Autores principales: Fabing Duan, François Chapeau-Blondeau, Derek Abbott
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Publicado: Public Library of Science (PLoS) 2012
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Acceso en línea:https://doaj.org/article/f61edc9f7a3c4f07a8c1e1e1001a8b6d
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spelling oai:doaj.org-article:f61edc9f7a3c4f07a8c1e1e1001a8b6d2021-11-18T07:23:02ZFisher information as a metric of locally optimal processing and stochastic resonance.1932-620310.1371/journal.pone.0034282https://doaj.org/article/f61edc9f7a3c4f07a8c1e1e1001a8b6d2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22493686/?tool=EBIhttps://doaj.org/toc/1932-6203The origins of Fisher information are in its use as a performance measure for parametric estimation. We augment this and show that the Fisher information can characterize the performance in several other significant signal processing operations. For processing of a weak signal in additive white noise, we demonstrate that the Fisher information determines (i) the maximum output signal-to-noise ratio for a periodic signal; (ii) the optimum asymptotic efficacy for signal detection; (iii) the best cross-correlation coefficient for signal transmission; and (iv) the minimum mean square error of an unbiased estimator. This unifying picture, via inequalities on the Fisher information, is used to establish conditions where improvement by noise through stochastic resonance is feasible or not.Fabing DuanFrançois Chapeau-BlondeauDerek AbbottPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 4, p e34282 (2012)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Fabing Duan
François Chapeau-Blondeau
Derek Abbott
Fisher information as a metric of locally optimal processing and stochastic resonance.
description The origins of Fisher information are in its use as a performance measure for parametric estimation. We augment this and show that the Fisher information can characterize the performance in several other significant signal processing operations. For processing of a weak signal in additive white noise, we demonstrate that the Fisher information determines (i) the maximum output signal-to-noise ratio for a periodic signal; (ii) the optimum asymptotic efficacy for signal detection; (iii) the best cross-correlation coefficient for signal transmission; and (iv) the minimum mean square error of an unbiased estimator. This unifying picture, via inequalities on the Fisher information, is used to establish conditions where improvement by noise through stochastic resonance is feasible or not.
format article
author Fabing Duan
François Chapeau-Blondeau
Derek Abbott
author_facet Fabing Duan
François Chapeau-Blondeau
Derek Abbott
author_sort Fabing Duan
title Fisher information as a metric of locally optimal processing and stochastic resonance.
title_short Fisher information as a metric of locally optimal processing and stochastic resonance.
title_full Fisher information as a metric of locally optimal processing and stochastic resonance.
title_fullStr Fisher information as a metric of locally optimal processing and stochastic resonance.
title_full_unstemmed Fisher information as a metric of locally optimal processing and stochastic resonance.
title_sort fisher information as a metric of locally optimal processing and stochastic resonance.
publisher Public Library of Science (PLoS)
publishDate 2012
url https://doaj.org/article/f61edc9f7a3c4f07a8c1e1e1001a8b6d
work_keys_str_mv AT fabingduan fisherinformationasametricoflocallyoptimalprocessingandstochasticresonance
AT francoischapeaublondeau fisherinformationasametricoflocallyoptimalprocessingandstochasticresonance
AT derekabbott fisherinformationasametricoflocallyoptimalprocessingandstochasticresonance
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