Combinatorial Information Theory: I Philosophical Basis of Cross-Entropy and Entropy
<|reference_start|>Combinatorial Information Theory: I Philosophical Basis of Cross-Entropy and Entropy: 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)<|reference_end|>
@article{niven2005combinatorial,
    title={Combinatorial Information Theory: I. Philosophical Basis of
  Cross-Entropy and Entropy},
    author={Robert K. Niven},
    journal={arXiv preprint arXiv:cond-mat/0512017},
    year={2005},
    archivePrefix={arXiv},
    eprint={cond-mat/0512017},
    primaryClass={cond-mat.stat-mech cs.IT math-ph math.IT math.MP physics.data-an}
}