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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-7512785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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