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Related Experiment Video

Updated: Jul 26, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Pooling probability distributions and partial information decomposition.

S J van Enk1

  • 1Department of Physics, University of Oregon, Eugene, Oregon 97403, USA.

Physical Review. E
|June 17, 2023
PubMed
Summary
This summary is machine-generated.

This study explores partial information decomposition (PID) by defining synergistic, redundant, and unique information. It proposes a novel pooling approach to resolve ambiguities in defining these information measures for multiple variables.

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Area of Science:

  • Information Theory
  • Multivariate Statistics
  • Computational Neuroscience

Background:

  • Partial Information Decomposition (PID) aims to quantify synergistic, redundant, and unique information among multiple variables.
  • Existing PID frameworks lack consensus on precise definitions for these information measures, leading to ambiguity.

Purpose of the Study:

  • To illustrate the sources of ambiguity in defining synergistic, redundant, and unique information within PID.
  • To propose a novel framework for PID based on probability distribution pooling.

Main Methods:

  • Defining information as the average reduction in uncertainty between probability distributions.
  • Interpreting synergistic information as the difference between the whole and the sum of its parts, using aggregated probability distributions.
  • Developing a lattice structure based on optimal probability distribution pooling.

Main Results:

  • The proposed pooling approach introduces a new lattice structure for PID, distinct from redundancy-based lattices.
  • This framework associates probability distributions with lattice nodes, not just average entropies.
  • Overlap between probability distributions emerges as a key quantity for characterizing synergistic and unique information.

Conclusions:

  • The study provides a method to resolve ambiguities in partial information decomposition by leveraging probability distribution pooling.
  • The proposed pooling approach offers a principled way to define synergistic and unique information, enhancing the understanding of multivariate information.
  • This work contributes a flexible framework for analyzing complex information relationships in multivariate systems.