Web-Books
im Austria-Forum
Austria-Forum
Web-Books
Naturwissenschaften
Physik
Differential Geometrical Theory of Statistics
Seite - 442 -
  • Benutzer
  • Version
    • Vollversion
    • Textversion
  • Sprache
    • Deutsch
    • English - Englisch

Seite - 442 - in Differential Geometrical Theory of Statistics

Bild der Seite - 442 -

Bild der Seite - 442 - in Differential Geometrical Theory of Statistics

Text der Seite - 442 -

Entropy2016,18, 110 In thismodel,weonlyconsiderbinaryparameterswithvalues±1 (here identifiedwith letters0 or1 inF2)andweignoreparameters inaneutralizedstate following implicationsacrossparameters, as in thedatasetsof [3,4]. Theentailmentofparameters, that is, thephenomenonbywhichaparticular valueofoneparameter (butnot thecomplementaryvalue) rendersanotherparameter irrelevant,was addressed ingreaterdetail in [22].Wefirstdiscussaversionofourcodingtheorymodel thatdoesnot incorporateentailment.Wewill thencomment inSection2.7belowonhowthemodelcanbemodified to incorporate thisphenomenon. The idea thatnatural languagescanbedescribed,at the levelof their coregrammatical structures, in termsofastringofbinarycharacters (codewords)wasalreadyusedextensively in [23]. 2.1. CodeParameters In the theory of error-correcting codes, one assigns two main parameters to a code C, the transmission rate and the relative minimum distance. More precisely, a binary code C ⊂ Fn2 is an [n,k,d]2-code if thenumberofcodewords is#C=2k, that is, k= log2#C, (1) wherekneednotbean integer,andtheminimalHammingdistancebetweencodewords is d= min L1 =L2∈C dH(L1,L2), (2) where theHammingdistance isgivenby dH(L1,L2)= n ∑ i=1 |xi−yi|, forL1=(xi)ni=1 andL2=(yi) n i=1 inC. The transmissionrateof thecodeC isgivenby R= k n . (3) OnedenotesbyδH(L1,L2) therelativeHammingdistance δH(L1,L2)= 1 n n ∑ i=1 |xi−yi|, andonedefines therelativeminimumdistanceof thecodeCas δ= d n = min L1 =L2∈C δH(L1,L2). (4) Incodingtheory,onewouldlike toconstructcodes thatsimultaneouslyoptimizebothparameters (δ,R): a largervalueofR representsa faster transmissionrate (betterencoding),anda largervalueof δ represents the fact thatcodewordsaresufficientlysparse in theambientspaceFn2 (betterdecoding, withbettererror-correctingcapability). Constraintsonthisoptimizationproblemareexpressed in the formofbounds in thespaceof (δ,R)parameters, see [12,13]. Inoursetting, theRparametermeasures theratiobetweenthe logarithmicsizeof thenumberof languagesencompassingthegivenfamilyandthetotalnumberofparameters,orequivalentlyhow densely thegiven languagefamily is in theambientconfigurationspaceFn2 ofparameterpossibilities. The parameter δ is theminimum, over all pairs of languages in the given family, of the relative Hammingdistanceusedin thePCMmethodof [3,4]. 442
zurück zum  Buch Differential Geometrical Theory of Statistics"
Differential Geometrical Theory of Statistics
Titel
Differential Geometrical Theory of Statistics
Autoren
Frédéric Barbaresco
Frank Nielsen
Herausgeber
MDPI
Ort
Basel
Datum
2017
Sprache
englisch
Lizenz
CC BY-NC-ND 4.0
ISBN
978-3-03842-425-3
Abmessungen
17.0 x 24.4 cm
Seiten
476
Schlagwörter
Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
Kategorien
Naturwissenschaften Physik
Web-Books
Bibliothek
Datenschutz
Impressum
Austria-Forum
Austria-Forum
Web-Books
Differential Geometrical Theory of Statistics