Bivariate Partial Information Decomposition: The Optimization Perspective
Institute of Computer Science, University of Tartu, 51014 Tartu, Estonia
Author to whom correspondence should be addressed.
Received: 7 July 2017 / Revised: 21 September 2017 / Accepted: 28 September 2017 / Published: 7 October 2017
Bertschinger, Rauh, Olbrich, Jost, and Ay (Entropy, 2014) have proposed a definition of a decomposition of the mutual information
into shared, synergistic, and unique information by way of solving a convex optimization problem. In this paper, we discuss the solution of their Convex Program from theoretical and practical points of view.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Printed Edition Available!
A printed edition of this Special Issue is available here
Share & Cite This Article
MDPI and ACS Style
Makkeh, A.; Theis, D.O.; Vicente, R. Bivariate Partial Information Decomposition: The Optimization Perspective. Entropy 2017, 19, 530.
Makkeh A, Theis DO, Vicente R. Bivariate Partial Information Decomposition: The Optimization Perspective. Entropy. 2017; 19(10):530.
Makkeh, Abdullah; Theis, Dirk O.; Vicente, Raul. 2017. "Bivariate Partial Information Decomposition: The Optimization Perspective." Entropy 19, no. 10: 530.
Show more citation formats
Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
[Return to top]
For more information on the journal statistics, click here
Multiple requests from the same IP address are counted as one view.