Network inference and maximum entropy estimation on information diagrams

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F17%3A43919244" target="_blank" >RIV/00023752:_____/17:43919244 - isvavai.cz</a>

Alternative codes found

RIV/68081740:_____/17:00486884 RIV/67985807:_____/17:00477754 RIV/00023001:_____/17:00076053

Result on the web

<a href="https://www.nature.com/articles/s41598-017-06208-w#Abs1" target="_blank" >https://www.nature.com/articles/s41598-017-06208-w#Abs1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-017-06208-w" target="_blank" >10.1038/s41598-017-06208-w</a>

Result language

angličtina

Original language name

Network inference and maximum entropy estimation on information diagrams

Original language description

Maximum entropy estimation is of broad interest for inferring properties of systems across many disciplines. Using a recently introduced technique for estimating the maximum entropy of a set of random discrete variables when conditioning on bivariate mutual informations and univariate entropies, we show how this can be used to estimate the direct network connectivity between interacting units from observed activity. As a generic example, we consider phase oscillators and show that our approach is typically superior to simply using the mutual information. In addition, we propose a nonparametric formulation of connected informations, used to test the explanatory power of a network description in general. We give an illustrative example showing how this agrees with the existing parametric formulation, and demonstrate its applicability and advantages for resting-state human brain networks, for which we also discuss its direct effective connectivity. Finally, we generalize to continuous random variables and vastly expand the types of information-theoretic quantities one can condition on. This allows us to establish significant advantages of this approach over existing ones. Not only does our method perform favorably in the undersampled regime, where existing methods fail, but it also can be dramatically less computationally expensive as the cardinality of the variables increases.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

—

Volume of the periodical

7

Issue of the periodical within the volume

Article Number: 7062

Country of publishing house

GB - UNITED KINGDOM

Number of pages

15

Pages from-to

1-15

UT code for WoS article

000406764200094

EID of the result in the Scopus database

2-s2.0-85026738778