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Autori principali: Franke, Paula, Hamacher, Kay, Manns, Paul
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2509.05099
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author Franke, Paula
Hamacher, Kay
Manns, Paul
author_facet Franke, Paula
Hamacher, Kay
Manns, Paul
contents The mutual information (MI) between two random variables is an important correlation measure in data analysis. The Shannon entropy of a joint probability distribution is the variable part under fixed marginals. We aim to minimize and maximize it to obtain the largest and smallest MI possible in this case, leading to a scaled MI ratio for better comparability. We present algorithmic approaches and optimal solutions for a set of problem instances based on data from molecular evolution. We show that this allows us to construct a sensible, systematic correction to raw MI values.
format Preprint
id arxiv_https___arxiv_org_abs_2509_05099
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Minimizing and Maximizing the Shannon Entropy for Fixed Marginals
Franke, Paula
Hamacher, Kay
Manns, Paul
Optimization and Control
90C26, 90C90
The mutual information (MI) between two random variables is an important correlation measure in data analysis. The Shannon entropy of a joint probability distribution is the variable part under fixed marginals. We aim to minimize and maximize it to obtain the largest and smallest MI possible in this case, leading to a scaled MI ratio for better comparability. We present algorithmic approaches and optimal solutions for a set of problem instances based on data from molecular evolution. We show that this allows us to construct a sensible, systematic correction to raw MI values.
title Minimizing and Maximizing the Shannon Entropy for Fixed Marginals
topic Optimization and Control
90C26, 90C90
url https://arxiv.org/abs/2509.05099