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Entropy2016,18, 433 Accordingtoourconstructiononehas the followingfunctor qΨ= qp2◦ψ∗. This functorqΨ is theHessianfunctorof themodel MΨ=[E1,π,M1,D1,p2◦Ψ]. FurtherΨ×ψ isan isomorphismofMΨontoM2. Letusprove thatassertion(2) impliesassertion(1). Byof thedefinitionofmorphismofmodels, thepairΨ×ψ isan isomorphismofM1 ontoM2 if andonly if p2◦Ψ= p1. Hereweset theexplicit formulas. Let∇∈LC(M1). ForallvectorfieldsX,Y inM2wehave X ·(Y · log(p1))−∇XY · log(p1)=X ·(Y · log(p2◦Ψ))−∇XY · log(p2◦Ψ) =ψ∗(X) ·(ψ∗(Y) · log(p2))−ψ∗(∇XY) · log(p2). nowweobserve that ψ∗(∇XY)= [ψ∗(∇)][ψ∗(X)]ψ∗(Y). Therefore (2) impies theequality ψ∗[q[p2](ψ∗(∇))]= q[p2◦Ψ](∇)= qp1. Thisshowsthe implication (2)→ (1). Letusprove thatassertion(1) impliesassertion(2). Nowweassumethat that (1)holds,viz q[p2◦Ψ] = qp1. Then bothM1 andMΨ have the sameHessian functor. By the virtue of Lemma8 abovewe deduce that p2◦Ψ= p1. Thisends thedemonstration. Reminder. (i) Objects ofGM(Γ,Ξ)arequintuplets M=[E,π,M,D,p]. Theyare called statisticalmodels for themeasurable set (Ξ,Ω). (ii) Objects ofFB(MSE)are functors [E,p]→ [M,D]. Theyare calledMSE-fibrations. 220
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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