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Entropy2017,19, 7
Theorem 2 (cf. [10,24]). For a q-exponential family Sq, two statistical manifolds (Sq,gF,∇(2q−1)) and
(Sq,g,∇q(e))are1-conformally equivalent. Inparticular, an invariant statisticalmanifold (Sq,gF,∇(2q−1)) is
1-conformallyflat. Riemannianmetrics andcubic formshave the followingrelations:
gqij(θ) = q
Zq(p) gFij(θ), (16)
Cqijk(θ) = q
Zq(p) (2q−1)CFijk(θ)
− q
Zq(p) {
gFij∂k lnZq(p)+g F
jk(θ)∂i lnZq(p)+g F
ki(θ)∂j lnZq(p)
}
. (17)
Proof. The results were essentially obtained in [10]. However, we give a simpler proof for
Equations (16)and(17).Thekeyideaisasequenceofescortdistributionsandtheescortrepresentations
ofgF andCF inTheorem1.
FromEquation(10),wedirectlyobtain theconformalequivalencerelation(16)usingtheescort
representationofgF in (12).
Bydifferentiating(9)andtakinganintegration,weobtain
0 = ∫
Ω u′′ (
∑θlFl(x)−ψ(θ) )
(Fi(x)−∂iψ(θ))(Fj(x)−∂jψ(θ))(Fk(x)−∂kψ(θ))dx
− ∫
Ω u′ (
∑θlFl(x)−ψ(θ) )
(Fk(x)−∂kψ(θ))∂i∂jψ(θ)dx
− ∫
Ω u′ (
∑θlFl(x)−ψ(θ) )
(Fi(x)−∂iψ(θ))∂j∂kψ(θ)dx
− ∫
Ω u′ (
∑θlFl(x)−ψ(θ) )
(Fj(x)−∂jψ(θ))∂k∂iψ(θ)dx
− ∫
Ω u (
∑θlFl(x)−ψ(θ) )
∂i∂j∂kψ(θ)dx.
SinceZq(p)= ∫
ΩPq(x;θ)dx,wehave
∂iZq(p)= ∂i ∫
Ω Pq(x;θ)dx= ∫
Ω ∂iPq(x;θ)dx= ∫
Ω P˜q(x;θ)(Fi(x)−∂iψ(θ))dx.
Fromtheescort representationofCF in (13), andProposition1,weobtainEquation (17) since
gqij(θ)= ∂i∂jψ(θ)andC q
ijk(θ)= ∂i∂j∂kψ(θ).
Weremarkthat thecubic formof (Sq,gF,∇(2q−1)) isnotCF but (2q−1)CF.
The difference of a α-divergence and a q-relative entropy is only the normalization q/Zq(p).
This implies thatanormalizationforprobabilitydensity imposesageneralizedconformalchangefora
statisticalmodel.
In thenextpartof this section, letusconsideranotherstatisticalmanifoldonSq (cf. [6,17,26]).
Recall thataFishermetricgF has the followingrepresentation:
gFij(θ)= ∫
Ω (∂i lnpθ)(∂jpθ)dx.
In information geometry, ∂i lnpθ is called an e-representation (exponential representation) of pθ,
and∂jpθ is calledam-representation (mixture representation). Intuitively,∂i lnpθ and∂jpθ areregarded
as tangentvectorsonastatisticalmodel.HenceaFishermetric is regardedasaL2-innerproductof
e- andm-representations.
Letusgeneralize e-andm-representations foraq-exponential family. For pθ ∈Sq,wecall∂i lnq pθ
aq-score function. ThenwedefineaRiemannianmetricgM by
gMij (θ)= ∫
Ω (∂i lnq pθ)(∂jpθ)dx. (18)
320
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