Combinatorial Information Theory: I. Philosophical Basis of Cross-Entropy and Entropy

Abstract

This study critically analyses the information-theoretic, axiomatic and combinatorial philosophical bases of the entropy and cross-entropy concepts. The combinatorial basis is shown to be the most fundamental (most primitive) of these three bases, since it gives (i) a derivation for the Kullback-Leibler cross-entropy and Shannon entropy functions, as simplified forms of the multinomial distribution subject to the Stirling approximation; (ii) an explanation for the need to maximize entropy (or minimize cross-entropy) to find the most probable realization; and (iii) new, generalized definitions of entropy and cross-entropy - supersets of the Boltzmann principle - applicable to non-multinomial systems. The combinatorial basis is therefore of much broader scope, with far greater power of application, than the information-theoretic and axiomatic bases. The generalized definitions underpin a new discipline of ``{\it combinatorial information theory}'', for the analysis of probabilistic systems of any type. Jaynes' generic formulation of statistical mechanics for multinomial systems is re-examined in light of the combinatorial approach. (abbreviated abstract)

arXiv comment45 pp; 1 figure; REVTex; updated version 5 (incremental changes)

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Cited by in corpus (7)

• Steady State of a Dissipative Flow-Controlled System and the Maximum Entropy Production Principle
• Multiplicative duality, q-triplet and (mu,nu,q)-relation derived from the one-to-one correspondence between the (mu,nu)-multinomial coefficient and Tsallis entropy Sq
• Asymptotic equivalence of empirical likelihood and Bayesian MAP
• Jaynes' Maximum Entropy Principle, Riemannian Metrics and Generalised Least Action Bound
• Combinatorial Entropy for Distinguishable Entities in Indistinguishable States
• The Polya Urn: Limit Theorems, Polya Divergence, Maximum Entropy and Maximum Probability
• Maximum Probability and Relative Entropy Maximization. Bayesian Maximum Probability and Empirical Likelihood