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Entropy2016,18, 396 computationalcomplexity.Unfortunately, eigenfunctionsof theLaplacianoperatorareknownonDn butnotoncompactsub-domains. This iswhythestudyof thismethodhasnotbeendeepened. 3.3. Kernels LetK :R+→R+beamapwhichverifies the followingproperties: (i) ∫ RdK(||x||)dx=1; (ii) ∫ Rd xK(||x||)dx=0; (iii) K(x>1)=0; (iv) sup(K(x))=K(0). Let p ∈ Dn. Generally, given apoint p on aRiemannianmanifold, expp defines an injective applicationonlyonaneighborhoodof0. On theSiegel space, expp is injectiveon thewhole space. Whenthe tangentspaceTpDn isendowedwith the local scalarproduct, ||u||= d(p,expp(u)), where ||.|| is theEuclideandistanceassociatedwiththelocalscalarproductandd(., .) is theRiemannian distance. The correspondingLebesguemeasureonTpDn is noted Lebp. Let exp∗p(Lebp)denote the push-forwardmeasureofLeppby expp. The functionθpdefinedby: θp : q → θp(q)= dvoldexp∗p(Lebp) (q) (6) is thedensityof theRiemannianmeasureonDnwithrespect to theLebesguemeasureLebp after the identificationofDn andTpDn inducedby expp (seeFigure1). Figure1.M isaRiemannianmanifold,andTxM is its tangentspaceatx. Theexponentialapplication inducesavolumechangeθxbetweenTxMandM. GivenKandapositiveradius r, theestimatorof f proposedby[1] isdefinedby: fˆk= 1 k∑i 1 rn 1 θxi(x) K ( d(x,xi) r ) . (7) Thecorrective factor θxi(x) −1 isnecessary since thekernelKoriginally integrates toonewith respect to theLebesguemeasureandnotwithrespect to theRiemannianmeasure. It canbenoticed that thisestimator is theusualkernelestimator in thecaseofEuclideanspace.Whenthecurvatureof thespace isnegative,which is thecaseof theSiegel space, thedistributionplacedovereachsample xihasxi as intrinsicmean. Thefollowingtheoremprovidesconvergencerateof theestimator. It isa directadaptationofTheorem3.1of [1]. Theorem1. Let (M,g) be aRiemannianmanifold of dimensionn andμ itsRiemannianvolumemeasure. LetXbearandomvariable taking itsvalues inacompact subsetCof (M,g). Let0< r≤ rinj,where rinj is the infimumof the injectivity radiusonC.Assumethe lawofXhasa twicedifferentiabledensity f with respect to 352
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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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