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Entropy2016,18, 433 AComment. Themapping M→ qM is a global geometrical invariant in the sense of Erlangen. In otherwords it is an invariant of the Γ-geometry in [E,π,M,D,p]. Ouraimis todemonstrate that M→ qM isacharacteristic invariant in thecategoryGM(Ξ,Ω). Inotherwords the isomorphismclassof the model M=[E,π,(M,D),p] isencodedbythe functor ∇→ qM[∇]. Thefirst step is the following lemma. Lemma7. In the sameobject [E,π,M,D]weconsider twostatisticalmodels M1=[E,π,M,D,p1], M2=[E,π,M,D,p2]. The followingassertionsare equivalent (1) qM1 = qM2, (2) p1= p2. Proof. Wework in thedomainofa local trivializationof [E,π,M,D]. By thevirtueofLemma6above weknowthat qp1 = qp2 if andonly if p1(x,ξ)=λ(ξ)p2(x,ξ) withλ∈RΞ+. Sinceboth p1 and p2 areΓ-equivariant the function Ξ ξ→λ(ξ) isΓ-invariant too.Nowwetakeintoaccount that thenaturalactionofΓ inΞ is transitive. Thereforethe Γ-equivariant functionλ(ξ) isaconstant function. Therefore p1(x,ξ)=λp2(x,ξ) Theoperationof integrationalongafiberofπyields λ=1 Thisends theproof. Weconsider twom-dimensional statisticalmodels for (Ξ,Ω), namely Mj=[Ej,πj,Mj,Dj,pj], j :=1,2. 218
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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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Differential Geometrical Theory of Statistics