2024/09/20 by Chiara Barà, Barà, Chiara, Yuri Antonacci +7
Computer Science · Decision Sciences · #Data Analysis #Distributed Sensor Networks and Detection Algorithms #FOS: Physical sciences #Probability and Risk Models #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2409.13506
openalex publication_date 2024/09/20 · openalex created_date 2024/10/26 · openalex updated_date 2026/07/28
The framework of Partial Information Decomposition (PID) unveils complex nonlinear interactions in network systems by dissecting the mutual information (MI) between a target variable and several source variables. While PID measures have been formulated mostly for discrete variables, with only recent extensions to continuous systems, the case of mixed variables where the target is discrete and the sources are continuous is not yet covered properly. Here, we introduce a PID scheme whereby the MI between a specific state of the discrete target and (subsets of) the continuous sources is expressed as a Kullback-Leibler divergence and is estimated through a data-efficient nearest-neighbor strategy. The effectiveness of this PID is demonstrated in simulated systems of mixed variables and showcased in a physiological application. Our approach is relevant to many scientific problems, including sensory coding in neuroscience and feature selection in machine learning.