Exact Partial Information Decompositions for Gaussian Systems Based on Dependency Constraints.

ABSTRACT: The Partial Information Decomposition, introduced by Williams P. L. et al. (2010), provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new method ( I dep ) has recently been proposed by James R. G. et al. (2017) for computing a two-predictor partial information decomposition over discrete spaces. A lattice of maximum entropy probability models is constructed based on marginal dependency constraints, and the unique information that a particular predictor has about the target is defined as the minimum increase in joint predictor-target mutual information when that particular predictor-target marginal dependency is constrained. Here, we apply the I dep approach to Gaussian systems, for which the marginally constrained maximum entr

SUBMITTER: Kay JW 

PROVIDER: S-EPMC7512755 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature