Partial information decomposition for discrete target and continuous source random variables
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel partial information decomposition (PID) method for mixed discrete-continuous variables. It accurately quantifies information flow without altering data, crucial for complex systems analysis.
Area Of Science
- Information theory
- Network systems analysis
- Computational neuroscience
Background
- Partial Information Decomposition (PID) quantifies complex interactions in network systems by analyzing mutual information (MI).
- Existing PID methods are primarily for discrete variables, with recent extensions to continuous systems.
- Current PID schemes for mixed discrete-continuous variables require data manipulation, potentially altering information content.
Purpose Of The Study
- To develop a new PID scheme for mixed discrete-continuous variables that avoids data manipulation.
- To accurately estimate the mutual information between a discrete target and continuous sources.
- To provide a robust tool for analyzing information flow in complex systems.
Main Methods
- Introduced a PID scheme expressing MI as Kullback-Leibler divergence for discrete target states and continuous sources.
- Employed a nearest-neighbor strategy for estimating the Kullback-Leibler divergence.
- Validated the method on simulated mixed-variable systems and benchmark datasets.
Main Results
- The proposed PID scheme effectively quantifies information decomposition in mixed variable systems.
- The method accurately estimates mutual information without altering original data.
- Demonstrated effectiveness in simulated environments and on established benchmark data.
Conclusions
- The novel PID approach overcomes limitations of existing methods for mixed discrete-continuous variables.
- This technique offers a non-invasive way to analyze information flow in complex systems.
- Applications include sensory coding in neuroscience and feature selection in machine learning.
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