LEADER 04034nam 2200889z- 450 001 9910566461703321 005 20220506 035 $a(CKB)5680000000037764 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/80958 035 $a(oapen)doab80958 035 $a(EXLCZ)995680000000037764 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aThe Statistical Foundations of Entropy 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (182 p.) 311 08$a3-0365-3557-8 311 08$a3-0365-3558-6 330 $aIn the last two decades, the understanding of complex dynamical systems underwent important conceptual shifts. The catalyst was the infusion of new ideas from the theory of critical phenomena (scaling laws, renormalization group, etc.), (multi)fractals and trees, random matrix theory, network theory, and non-Shannonian information theory. The usual Boltzmann-Gibbs statistics were proven to be grossly inadequate in this context. While successful in describing stationary systems characterized by ergodicity or metric transitivity, Boltzmann-Gibbs statistics fail to reproduce the complex statistical behavior of many real-world systems in biology, astrophysics, geology, and the economic and social sciences.The aim of this Special Issue was to extend the state of the art by original contributions that could contribute to an ongoing discussion on the statistical foundations of entropy, with a particular emphasis on non-conventional entropies that go significantly beyond Boltzmann, Gibbs, and Shannon paradigms. The accepted contributions addressed various aspects including information theoretic, thermodynamic and quantum aspects of complex systems and found several important applications of generalized entropies in various systems. 606 $aMathematics & science$2bicssc 606 $aResearch & information: general$2bicssc 610 $aadaptive Type-II progressive hybrid censoring scheme 610 $aBayesian estimation 610 $aBayesian inference 610 $acalibration invariance 610 $aconfidence interval 610 $acritical phenomena 610 $adistributional weighted regression 610 $aecological inference 610 $aentropic uncertainty relations 610 $aentropy 610 $aescort probabilities 610 $agaussian entropy 610 $ageneralized Bilal distribution 610 $ageneralized cross entropy 610 $ageneralized entropies 610 $aGENERIC 610 $aKolmogorov-Nagumo averages 610 $aLagrange multipliers 610 $alandsberg-vedral entropy 610 $aLindley's approximation 610 $aMarkov chain Monte Carlo method 610 $amatrix adjustment 610 $aMaxEnt distribution 610 $amaximum entropy 610 $amaximum entropy principle 610 $amaximum likelihood estimation 610 $amultiscale thermodynamics 610 $an/a 610 $anon-Diophantine arithmetic 610 $anon-equilibrium thermodynamics 610 $anon-Newtonian calculus 610 $aquantum metrology 610 $arenormalization 610 $are?nyi entropy 610 $aRe?nyi entropy 610 $asharma-mittal entropy 610 $atsallis entropy 610 $aTsallis entropy 610 $aupdating probabilities 610 $avariational entropy 610 $a?-channel capacity 610 $a?-mutual information 615 7$aMathematics & science 615 7$aResearch & information: general 700 $aJizba$b Petr$4edt$0618869 702 $aKorbel$b Jan$4edt 702 $aJizba$b Petr$4oth 702 $aKorbel$b Jan$4oth 906 $aBOOK 912 $a9910566461703321 996 $aThe Statistical Foundations of Entropy$93040910 997 $aUNINA