Applications of Information Theory: Statistics and Statistical Mechanics
Khizar Qureshi
TL;DR
This paper explores how entropy optimization from Information Theory can be applied to asymptotic hypothesis testing and particle distribution determination in statistical mechanics.
Contribution
It introduces entropy-based methods for hypothesis testing and particle distribution analysis within the contexts of statistics and statistical mechanics.
Findings
Entropy optimization aids in asymptotic hypothesis testing.
Maximizing entropy determines particle distributions.
The approach links information theory with physical and statistical models.
Abstract
The method of optimizing entropy is used to (i) conduct Asymptotic Hypothesis Testing and (ii) determine the particle distribution for which Entropy is maximized. This paper focuses on two related applications of Information Theory: Statistics and Statistical Mechanics.
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Taxonomy
TopicsWireless Communication Security Techniques · Statistical Mechanics and Entropy · Computability, Logic, AI Algorithms