dpiras/GMM-MI
Estimation of mutual information (MI) distribution with Gaussian mixture models (GMMs)
This tool helps researchers and data scientists understand the strength and nature of relationships between different measurements in their data, even when those relationships are complex and non-linear. You provide your experimental observations or data points, and it quantifies how much information one variable tells you about another, along with a measure of confidence in that result. It's designed for anyone who needs to rigorously analyze dependencies in their datasets without making assumptions about linearity.
No commits in the last 6 months. Available on PyPI.
Use this if you need to calculate mutual information between variables in your dataset and also quantify the uncertainty of that estimate, especially when dealing with complex, non-linear relationships.
Not ideal if you only need a simple, fast correlation measure and don't require advanced statistical modeling or uncertainty quantification for non-linear dependencies.
Stars
23
Forks
4
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Apr 01, 2025
Commits (30d)
0
Dependencies
7
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