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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-04-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/20/4/271 |
| _version_ | 1817996619750047744 |
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| author | Abdullah Makkeh Dirk Oliver Theis Raul Vicente |
| author_facet | Abdullah Makkeh Dirk Oliver Theis Raul Vicente |
| author_sort | Abdullah Makkeh |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-14T02:25:01Z |
| format | Article |
| id | doaj.art-0cbe8d24ee6c4c8ca4e0605958f0cd96 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-14T02:25:01Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-0cbe8d24ee6c4c8ca4e0605958f0cd962022-12-22T02:17:54ZengMDPI AGEntropy1099-43002018-04-0120427110.3390/e20040271e20040271BROJA-2PID: A Robust Estimator for Bivariate Partial Information DecompositionAbdullah Makkeh0Dirk Oliver Theis1Raul Vicente2Institute of Computer Science, University of Tartu, Ülikooli 17, 51014 Tartu, EstoniaInstitute of Computer Science, University of Tartu, Ülikooli 17, 51014 Tartu, EstoniaInstitute of Computer Science, University of Tartu, Ülikooli 17, 51014 Tartu, EstoniaMakkeh, 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.http://www.mdpi.com/1099-4300/20/4/271bivariate information decompositionCone Programming |
| spellingShingle | Abdullah Makkeh Dirk Oliver Theis Raul Vicente BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition Entropy bivariate information decomposition Cone Programming |
| 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 | bivariate information decomposition Cone Programming |
| url | http://www.mdpi.com/1099-4300/20/4/271 |
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