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Entropy2017,19, 7 Sinceadifferentialofapower function isalsoapower function,wecangiveacharacterization forescortdistributions. Proposition1. Suppose that Sq is a q-exponential family defined by (1). Then the n-th escort distribution is givenby the n-th differential of q-exponential function. That is, by settingu(t) = (expq t) ′,wehave the following formula: pq(x;θ) = expq ( n ∑ i=1 θiFi(x)−ψ(θ) ) , Pq(x;θ)=Pq,(1)(x;θ) = u ( n ∑ i=1 θiFi(x)−ψ(θ) ) , P˜q(x;θ)=Pq,(2)(x;θ) = u ′ ( n ∑ i=1 θiFi(x)−ψ(θ) ) , ... ... Pq,(n)(x;θ) = u (n−1) ( n ∑ i=1 θiFi(x)−ψ(θ) ) , ... ... Proof. Sinceaq-exponential function isexpq(x)=(1+(1−q))1/(1−q), itsdifferential isgivenby u(x)= 1−q 1−q(1+(1−q)x) 1 1−q−1=(1+(1−q)x) q 1−q ={expq x}q. Therefore,weobtainPq(x;θ)=u ( ∑ni=1θ iFi(x)−ψ(θ) ) . Byinduction, then-thdifferentialofu(x)coincideswiththen-thescortdistributionPq,(n),whichis givenbyEquation(5). 4. StatisticalManifoldsandTheirGeneralizedConformalStructures In this section,weus reviewthegeometryof statisticalmanifolds. Formoredetails about the geometryofstatisticalmanifolds, see [18,19]. Let (S,g)beaRiemannianmanifoldand∇beatorsion-freeaffineconnectiononS.Wesaythat thetriplet (S,∇,g) isa statisticalmanifold if∇g is totallysymmetric. In thiscase,wecandefineatotally symmetric (0,3)-tensorfieldby C(X,Y,Z) :=(∇Xg)(Y,Z) = Xg(Y,Z)−g(∇XY,Z)−g(Y,∇XZ), where X,Y and Z are arbitrary vector fields on S. The tensor field C is called a cubic form or an Amari–Chentsov tensorfield. Thenotionofstatisticalmanifoldwas introducedbyLauritzen[20].Hecalled the triplet (S,g,C) a statisticalmanifold. In this paper, the definition is followed toKurose [18]. Though these two definitions aredifferent, theother statisticalmanifold structure canbeobtained fromagivenone, However, themotivationfor thenotionofconformalequivalenceusing (S,g,C) isdifferent fromthat oneusing (S,∇,g),whichwewilldiscuss in the latterpartof this section. Foragivenstatisticalmanifold (S,∇,g),wecandefineanother torsion-freeaffineconnection∇∗ onSby Xg(Y,Z)= g(∇XY,Z)+g(Y,∇∗XZ). 316
zurĂźck zum  Buch Differential Geometrical Theory of Statistics"
Differential Geometrical Theory of Statistics
Titel
Differential Geometrical Theory of Statistics
Autoren
FrĂŠdĂŠric 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
SchlagwĂśrter
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
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Differential Geometrical Theory of Statistics