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BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition

Makkeh, Theis, and Vicente found that Cone Programming model is the most robust to compute the Bertschinger et al. partial information decomposition (BROJA PID) measure. We developed a production-quality robust software that computes the BROJA PID measure based on the Cone Programming model. In this...

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Detalles Bibliográficos
Autores principales: Makkeh, Abdullah, Theis, Dirk Oliver, Vicente, Raul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512785/
https://www.ncbi.nlm.nih.gov/pubmed/33265362
http://dx.doi.org/10.3390/e20040271
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author Makkeh, Abdullah
Theis, Dirk Oliver
Vicente, Raul
author_facet Makkeh, Abdullah
Theis, Dirk Oliver
Vicente, Raul
author_sort Makkeh, Abdullah
collection PubMed
description Makkeh, Theis, and Vicente found that Cone Programming model is the most robust to compute the Bertschinger et al. partial information decomposition (BROJA PID) measure. We developed a production-quality robust software that computes the BROJA PID measure based on the Cone Programming model. In this paper, we prove the important property of strong duality for the Cone Program and prove an equivalence between the Cone Program and the original Convex problem. Then, we describe in detail our software, explain how to use it, and perform some experiments comparing it to other estimators. Finally, we show that the software can be extended to compute some quantities of a trivaraite PID measure.
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spelling pubmed-75127852020-11-09 BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition Makkeh, Abdullah Theis, Dirk Oliver Vicente, Raul Entropy (Basel) Article Makkeh, Theis, and Vicente found that Cone Programming model is the most robust to compute the Bertschinger et al. partial information decomposition (BROJA PID) measure. We developed a production-quality robust software that computes the BROJA PID measure based on the Cone Programming model. In this paper, we prove the important property of strong duality for the Cone Program and prove an equivalence between the Cone Program and the original Convex problem. Then, we describe in detail our software, explain how to use it, and perform some experiments comparing it to other estimators. Finally, we show that the software can be extended to compute some quantities of a trivaraite PID measure. MDPI 2018-04-11 /pmc/articles/PMC7512785/ /pubmed/33265362 http://dx.doi.org/10.3390/e20040271 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Makkeh, Abdullah
Theis, Dirk Oliver
Vicente, Raul
BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition
title BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition
title_full BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition
title_fullStr BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition
title_full_unstemmed BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition
title_short BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition
title_sort broja-2pid: a robust estimator for bivariate partial information decomposition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512785/
https://www.ncbi.nlm.nih.gov/pubmed/33265362
http://dx.doi.org/10.3390/e20040271
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AT vicenteraul broja2pidarobustestimatorforbivariatepartialinformationdecomposition