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- W3100876332 abstract "We examine the {combinatorial} or {probabilistic} definition (Boltzmann's principle) of the entropy or cross-entropy function $H propto ln mathbb{W}$ or $D propto - ln mathbb{P}$, where $mathbb{W}$ is the statistical weight and $mathbb{P}$ the probability of a given realization of a system. Extremisation of $H$ or $D$, subject to any constraints, thus selects the most probable (MaxProb) realization. If the system is multinomial, $D$ converges asymptotically (for number of entities $N back to back infty$) to the Kullback-Leibler cross-entropy $D_{KL}$; for equiprobable categories in a system, $H$ converges to the Shannon entropy $H_{Sh}$. However, in many cases $mathbb{W}$ or $mathbb{P}$ is not multinomial and/or does not satisfy an asymptotic limit. Such systems cannot meaningfully be analysed with $D_{KL}$ or $H_{Sh}$, but can be analysed directly by MaxProb. This study reviews several examples, including (a) non-asymptotic systems; (b) systems with indistinguishable entities (quantum statistics); (c) systems with indistinguishable categories; (d) systems represented by urn models, such as neither independent nor identically distributed (ninid) sampling; and (e) systems representable in graphical form, such as decision trees and networks. Boltzmann's combinatorial definition of entropy is shown to be of greater importance for {probabilistic inference} than the axiomatic definition used in information theory." @default.
- W3100876332 created "2020-11-23" @default.
- W3100876332 creator A5014169616 @default.
- W3100876332 date "2009-05-16" @default.
- W3100876332 modified "2023-09-27" @default.
- W3100876332 title "Combinatorial entropies and statistics" @default.
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- W3100876332 doi "https://doi.org/10.1140/epjb/e2009-00168-5" @default.
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