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Entropy2016,18, 110 notconsiderhere,butwhichwillbediscussed inmoredetail inupcomingwork. Forexample,one canhaveasituationwith twolanguages inwhichaparameter isentailedbythevaluesof twoother parameters,butentailed to twodifferentvalues in the twolanguages. In thiscase, theproposalabove need tobemodiïŹed, because this entailedparameter should contribute to theHammingdistance betweenthe twolanguages. Insuchasituationtheentailedparametershould increase, rather than spoil, theefïŹciencyof thecode.Keepingentailedparameterscanbeusedforerror-correctingpurposes, ascontributingtoerrordetection. Theroleofentailmentofparameterswasconsideredin[8], in the useofspinglassmodels for languagechange,where theentailmentrelationsappearascouplingsat thevertices (interaction terms)betweendifferent Ising/Pottsmodelsonthesameunderlyinggraphof language interactions. Inupcomingwork,nowinpreparation,wewilldiscusshowtreatingdifferent formsofentailmentofparameters in thecodingtheorysettingdescribedhererelatedto the treatment ofentailmentrelations in thespinglassmodelof [8]. 3. EntropyandComplexityforLanguageFamilies 3.1.WhytheAsymptoticBound? In theexamplesdiscussedabovewecomparedthepositionof thecodepointassociatedtoagiven setof languages tocertaincurves in thespaceofcodeparameters. Inparticular,we focusedonthe asymptoticboundcurveandtheGilbert–Varshamovcurve. It shouldbepointedout that these two curveshaveaverydifferentnature. Theasymptoticbound is theonlycurve that separates regions in thespaceofparameters that correspondtocodepointswithentirelydifferentbehavior.Asshownin[13,24], codepoints in thearea belowtheasymptoticboundarerealizedwith inïŹnitemultiplicityandïŹlldensely theregion,while codepoints that lieabovetheasymptoticboundare isolatedandrealizedwithïŹnitemultiplicity. TheGilbert–Varshamovcurve, by contrast, is related to the statistical behavior of sufïŹciently randomcodes (aswerecall inSection3.2below),butdoesnotseparate tworegionswithsigniïŹcantly differentbehavior in thespaceofcodepoints. Thus, in this respect, theasymptoticboundisamore natural curve toconsider thantheGilbert–Varshamovcurve. Thus,aheuristic interpretationof thepositionofcodesobtainedfromgroupsof languages,with respect to theasymptoticboundcanbeunderstoodas follows. Thepositionofacodepointaboveor belowtheasymptoticboundreïŹ‚ectsaverydifferentbehaviorof thecorrespondingcodewithrespect tohoweasily“deformable” it is. Thesporadic codes that lieabove theasymptoticboundare rigid objects, in contrast to thedeformable objects below the asymptotic bound. In termsofproperties of the distribution of syntactic parameterswithin a set of languages, this different nature of the associatedcodecanbeseenasameasureof thedegreeof“deformability”of theparameterdistribution: in languages that belong to the samehistorical linguistic families, the parameter distributionhas evolvedhistoricallyalongwith thedevelopmentof the family’sphylogenetic tree,andoneexpects that correspondingly the codeparameterswill indicate a higher degree of “deformability” of the correspondingcode. Ifagroupof languages ischosenthatbelongtoverydifferenthistorical families, on thecontrary,oneexpects that thedistributionofsyntacticparameterswillnotnecessarily leadany longer toacodethathas thesamekindofdeformabilityproperty: codepointsabovetheasymptotic boundmayberealizablebythis typeof languagegroups. There is no similar interpretation for the position of the code point with respect to the Gilbert–Varshamovline.Aninterpretationof thatpositioncanbesought in termsofShannonentropy, aswediscussbelow. Summarizing: themainconceptualdistinctionbetweentheGilbert–Varshamov line and the asymptotic bound is that the GV line represents only a statistical phenomenon, as wereviewbelow,while theasymptoticboundrepresents a true separationbetween twoclassesof structurallydifferentcodes, in thesenseexplainedabove. 448
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Differential Geometrical Theory of Statistics
Title
Differential Geometrical Theory of Statistics
Authors
Frédéric Barbaresco
Frank Nielsen
Editor
MDPI
Location
Basel
Date
2017
Language
English
License
CC BY-NC-ND 4.0
ISBN
978-3-03842-425-3
Size
17.0 x 24.4 cm
Pages
476
Keywords
Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
Categories
Naturwissenschaften Physik
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