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|May 8, 2023
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Summary
This summary is machine-generated.

We introduce embo, a Python package for analyzing data with the Information Bottleneck (IB) method. This tool simplifies complex data analysis for discrete variables, offering speed and ease of use.

Keywords:
Deterministic Information BottleneckInformation BottleneckInformation theoryPythondata analysisstatistics

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Area of Science:

  • Machine Learning
  • Information Theory
  • Computational Statistics

Background:

  • The Information Bottleneck (IB) method is a powerful tool for analyzing relationships between variables.
  • Existing implementations often lack accessibility for empirical data analysis.
  • A need exists for a user-friendly, efficient Python package for IB applications.

Purpose of the Study:

  • To introduce embo, a novel Python package designed for Information Bottleneck analysis.
  • To provide an accessible and efficient implementation of the IB method and its variants for discrete data.
  • To facilitate the application of IB to diverse empirical datasets.

Main Methods:

  • Development of the 'embo' Python package.
  • Implementation of the Information Bottleneck (IB) and Deterministic Information Bottleneck (DIB) algorithms.
  • Optimization for discrete, low-dimensional data with parallel processing capabilities.

Main Results:

  • Embo offers a fast and standard data-processing pipeline for IB analysis.
  • The package includes optimized defaults for IB method parameters.
  • Embo is broadly applicable to datasets with joint observations of two discrete variables.

Conclusions:

  • Embo addresses the need for an accessible and efficient Python package for Information Bottleneck analysis.
  • The package simplifies the application of IB to empirical data, enhancing its usability in various domains.
  • Embo is readily available via PyPI, Zenodo, and GitLab for researchers and practitioners.