5 papers
Closed-Form Gaussian Estimators for Multi-Source Partial Information Decomposition
Aobo Lyu, Andrew Clark, Netanel Raviv
Computing multi-source partial information decomposition (PID) for continuous data is hard: existing closed-form Gaussian estimators are restricted to two source variables, while c…
Structural Impossibility of Antichain-Lattice Partial Information Decomposition
Aobo Lyu, Andrew Clark, Netanel Raviv
Partial Information Decomposition (PID) represents multivariate mutual information via antichain-lattice that aims to specify which source groups can recover which informational co…
Multivariate Partial Information Decomposition: Constructions, Inconsistencies, and Alternative Measures
Aobo Lyu, Andrew Clark, Netanel Raviv
While mutual information effectively quantifies dependence between two variables, it does not by itself reveal the complex, fine-grained interactions among variables, i.e., how mul…
The Whole Is Less than the Sum of Parts: Subsystem Inconsistency in Partial Information Decomposition
Aobo Lyu, Andrew Clark, Netanel Raviv
Partial Information Decomposition (PID) was proposed by Williams and Beer in 2010 as a tool for analyzing fine-grained interactions between multiple random variables, and has since…
System Information Decomposition
Aobo Lyu, Bing Yuan, Ou Deng +2
To characterize the complex higher-order interactions among variables within a system, this study introduces a novel framework, termed System Information Decomposition (SID), aimed…