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Generalized entropic structures and non-generality of Jaynes' Formalism

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Thomas Oikonomou, Ugur Tirnakli

Statistical Mechanics · Submitted 12 Aug 2008

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In this study we analyse these inconsistencies in detail and demonstrate that Jaynes' formalism leads to correct results only for specific entropy definitions.

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arXiv ID0808.1673 (version 2)
Pith IDT2JDYRNQ, Pith's permanent record of this paper and its review
CitationThomas Oikonomou, Ugur Tirnakli. "Generalized entropic structures and non-generality of Jaynes' Formalism." arXiv:0808.1673 (2008). https://pith.science/paper/T2JDYRNQ
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What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.18 · human participants 0.02 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.88 · mentions code or data availability 0.02.

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arXiv listing abstract v2. Results and the main claim are underlined; .

The extremization of an appropriate entropic functional may yield to the probability distribution functions maximizing the respective entropic structure. This procedure is known in Statistical Mechanics and Information Theory as Jaynes' Formalism and has been up to now a standard methodology for deriving the aforementioned distributions. However, the results of this formalism do not always coincide with the ones obtained following different approaches. In this study we analyse these inconsistencies in detail and demonstrate that Jaynes' formalism leads to correct results only for specific entropy definitions.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The extremization of an appropriate entropic functional may yield to the probability distribution functions maximizing the respective entropic structure. background 0.98 0.08 0.04 hedged possibility 1.00
s1 This procedure is known in Statistical Mechanics and Information Theory as Jaynes' Formalism and has been up to now a standard methodology for deriving the aforementioned distributions. background 1.00 0.03 0.03 asserted 1.00
s2 However, the results of this formalism do not always coincide with the ones obtained following different approaches. background 0.59 0.08 0.07 asserted 0.86
s3 In this study we analyse these inconsistencies in detail and demonstrate that Jaynes' formalism leads to correct results only for specific entropy definitions. result 0.59 0.92 0.94 asserted 1.00

What kind of paper the classifier reports this is: theoretical result 0.94 · evidence from proof or derivation 0.70.

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  • C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

The extremization of an appropriate entropic functional may yield to the probability distribution functions maximizing the respective entropic structure. This procedure is known in Statistical Mechanics and Information Theory as Jaynes' Formalism and has been up to now a standard methodology for deriving the aforementioned distributions. However, the results of this formalism do not always coincide with the ones obtained following different approaches. In this study we analyse these inconsistencies in detail and demonstrate that Jaynes' formalism leads to correct results only for specific entropy definitions.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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