Web-Books
in the Austria-Forum
Austria-Forum
Web-Books
Naturwissenschaften
Physik
Differential Geometrical Theory of Statistics
Page - 322 -
  • User
  • Version
    • full version
    • text only version
  • Language
    • Deutsch - German
    • English

Page - 322 - in Differential Geometrical Theory of Statistics

Image of the Page - 322 -

Image of the Page - 322 - in Differential Geometrical Theory of Statistics

Text of the Page - 322 -

Entropy2017,19, 7 ThemetricgN is conformallyequivalent togMwithconformal factorZq(pθ)= ∫ Ω{p(x;θ)}qdx. That is, gN(θ)=Zq(pθ)gM(θ). (Seealso [6]). Naudtsgavea furthergeneralizationofFishermetric andheshowedaCramér–Raotypeboundtheorem[5]. 6.ConcludingRemarks In thispaper,we introducedasequenceofescortdistributions. Thenwegaverepresentationsof Riemannianmetricsandcubic formsfromaviewpointof thesequenceofescortdistributions. Inparticular,wecandefinethefollowing(0,2)-tensorfieldsonaq-exponential family. Forpθ ∈Sq, setηi= ∂iψ(θ). (1) Fromthestandardexpectation,weobtain g(0)ij (θ) := Gij(θ) := ∫ Ω (∂i lnq pθ)(∂j lnq pθ)pθdx = Ep[(Fi(x)−ηi)(Fj(x)−ηj)]. The tensorG isacovariancematrix.However,Gmaynotbe important inanomalousstatistics. (2) Fromtheq-expectation,weobtain g(1)ij (θ) := g M ij (θ) = ∫ Ω (∂i lnq pθ)(∂j lnq pθ){pθ}qdx = ∫ Ω (∂i lnq pθ)(∂j lnq pθ)Pq(x;θ)dx = Eq,p[(Fi(x)−ηi)(Fj(x)−ηj)]. TheRiemannianmetricgM isaHessianmetric, andit is inducedfromtheβ-divergence (20). (3) Fromtheexpectationwithrespect to thesecondescortdistribution,weobtain g(2)ij (θ) := g F ij(θ) = ∫ Ω (∂i lnq pθ)(∂j lnq pθ){pθ}2q−1dx = 1 q ∫ Ω (∂i lnq pθ)(∂j lnq pθ)P˜q(x;θ)dx = 1 q Eq,(2),p[(Fi(x)−ηi)(Fj(x)−ηj)]. gqij(θ) = Zq(p) q gFij. TheRiemannianmetric gF is a Fishermetric. Hence gF is invariant to the choiceof reference measureonΩ, but it isnotaHessianmetric. Inaddition, gF is inducedfromtheα-divergence (14). TheconformalRiemannianmetricgq isaq-Fishermetric. It isaHessianmetric, and it is inducedfrom anormalizedTsallis relativeentropy(15). WemaydefineaRiemannianmetricandacubic formfromhigherorderescortexpectations: g(n)ij (θ) := ∫ Ω (∂i lnq pθ)(∂j lnq pθ)Pq,(n)(x;θ)dx, C(n)ij (θ) := ∫ Ω (∂i lnq pθ)(∂j lnq pθ)(∂k lnq pθ)Pq,(n+1)(x;θ)dx. Thenweobtainasequenceofstatisticalmanifoldstructures. (Sq,g(1),C(1)) → (Sq,g(2),C(2)) → ··· → (Sq,g(n),C(n)) → ··· However, the geometric meaning of this sequence is not clear at this moment. Elucidating geometricpropertiesof this sequence isa futureproblem. 322
back to the  book Differential Geometrical Theory of Statistics"
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
Web-Books
Library
Privacy
Imprint
Austria-Forum
Austria-Forum
Web-Books
Differential Geometrical Theory of Statistics