Holzinger, A., Hörtenhuber, M., Mayer, C., Bachler, M., Wassertheurer, S., Pinho, A.J. and Koslicki, D. (2014) On Entropy-Based Data Mining. In: Holzinger, A. and Jurisica, I., Eds., Knowledge Discovery and Data Mining, Springer, Berlin, 209-226.
https://doi.org/10.1007/978-3-662-43968-5_12

has been cited by the following article:

• TITLE: An Informational Proof of H-Theorem

  AUTHORS: Vincenzo Manca

  KEYWORDS: Thermodynamic Entropy, H-Theorem, Information Entropy, Entropic Divergence

  JOURNAL NAME: Open Access Library Journal, Vol.4 No.2, February 21, 2017

  ABSTRACT: After a historical reconstruction of the main Boltzmann’s ideas on mechanical statistics, a discrete version of Boltzmann’s H-theorem is proved, by using basic concepts of information theory. Namely, H-theorem follows from the central limit theorem, acting inside a closed physical system, and from the maximum entropy law for normal probability distributions, which is a consequence of Kullback-Leibler entropic divergence positivity. Finally, the relevance of discreteness and probability, for a deep comprehension of the relationship between physical and informational entropy, is analyzed and discussed in the light of new perspectives emerging in computational genomics.