2017
DOI: 10.1016/j.physleta.2016.11.005
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Misusing the entropy maximization in the jungle of generalized entropies

Abstract: It is well-known that the partition function can consistently be factorized from the canonical equilibrium distribution obtained through the maximization of the Shannon entropy. We show that such a normalized and factorized equilibrium distribution is warranted if and only if the entropy measure I{(p)} has an additive slope i.e. ∂I{(p)}/∂pi when the ordinary linear averaging scheme is used. Therefore, we conclude that the maximum entropy principle of Jaynes should not be used for the justification of the parti… Show more

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Cited by 13 publications
(26 citation statements)
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“…A very interesting result was recently reported in [24]. Let a and β stand for the two Lagrange multipliers appearing in a canonical ensemble MaxEnt treatment.…”
Section: Introductionmentioning
confidence: 81%
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“…A very interesting result was recently reported in [24]. Let a and β stand for the two Lagrange multipliers appearing in a canonical ensemble MaxEnt treatment.…”
Section: Introductionmentioning
confidence: 81%
“…We show here by construction, appealing to conventional techniques of Functional Analysis, not used in [24], that the functional forms (1.1), (1.2) are indeed recoveredà la Tsallis (or Renyi), thus surmounting the formidable obstacle posed by [24] to the use of MaxEnt for these two entropies. We are not claiming that the arguments of [24] are wrong, but that they are Calculus-based, while the proper MaxEnt scenario requires functional Analysis.…”
Section: Introductionmentioning
confidence: 87%
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“…Extremizing s q subject to suitable constraints yields more general canonical ensembles(see Appendix B), where the probability to observe a microstate with energy ǫ i is given by: [1,44,45] …”
Section: Connection Between Entropy and Microstates In Tsallis Statismentioning
confidence: 99%
“…Why? Because MaxEnt workings with q-generalized entropies have recently received serious questioning [2,3], and we wish to disentangle our findings from MaxEnt.…”
Section: Introductionmentioning
confidence: 99%