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Interfacing biology, category theory and mathematical statistics

descriptionPublicationkeyboard_double_arrow_right Article , Conference object , Preprint , Other literature type 15 Sep 2020Embargo end date: 01 Jan 2020 France English Publisher:Open Publishing AssociationJournal:Electronic Proceedings in Theoretical Computer Science, volume 323, pages 136-148 (eissn: 2075-2180, Copyright policy )
Authors: Pastor, Dominique; Beurier, Erwan; Ehresmann, Andrée; Waldeck, Roger;

doi: 10.4204/eptcs.323.9 , 10.48550/arxiv.2009.06832 , 10.5281/zenodo.3934482 , 10.5281/zenodo.3934481

arXiv: 2009.06832

Interfacing biology, category theory and mathematical statistics

Abstract

Motivated by the concept of degeneracy in biology (Edelman, Gally 2001), we establish a first connection between the Multiplicity Principle (Ehresmann, Vanbremeersch 2007) and mathematical statistics. Specifically, we exhibit two families of statistical tests that satisfy this principle to achieve the detection of a signal in noise.

In Proceedings ACT 2019, arXiv:2009.06334

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Keywords

FOS: Computer and information sciences, Degeneracy, Multiplicity principle, [STAT.TH] Statistics [stat]/Statistics Theory [stat.TH], Statistics, Mathematics - Category Theory, Mathematics - Statistics Theory, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Statistics Theory (math.ST), [MATH.MATH-CT] Mathematics [math]/Category Theory [math.CT], 510, Computational Engineering, Finance, and Science (cs.CE), FOS: Mathematics, Category Theory (math.CT), Computer Science - Computational Engineering, Finance, and Science, Noise, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Biology, [MATH.MATH-CT]Mathematics [math]/Category Theory [math.CT], [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Category theory, Signal detection

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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