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Entropy2016,18, 433 whosedomain is a convexopensubsetU.Wewrite thematrixofFisher informationg in thebasis{ ∂ ∂θj } , namely g=∑gijdθidθj. Here gij=g( ∂ ∂θi , ∂ ∂θj ). Theassumption δKVg=0 is equivalent to the system ∂gij ∂θk − ∂gkj ∂θi =0 for all i, j,k. Weuseanotationwhich isused in [52].Weconsider thedifferential1-forms hj=∑ i gijdθi. Everydifferential1-formhj isadeRhamcocycle. BytheLemmaofPoincaré theconvexopensetUsupports smooth functionsφj, j=: 1,...,mwhichhave the followingproperty dφj=hj. Weput ω=∑ j φjdθj. Thenwehave (δKVω)( ∂ ∂θi , ∂ ∂θj )=gij. Thus thedifferential1-form∑jφjdθj is deRhamclosed. SinceU is convex it supports a local smooth functionΨ such that dΨ=∑φjdθj. Soweget g( ∂ ∂θi , ∂ ∂θj )= ∂2ψ ∂θi∂θj . Tocontinuewefixθ0∈Uandweconsider the function θ→ a(θ) which isdefined inUby a(θ)= ∫ Ξ P(θ0,ξ)[ψ(θ)+ log(P(θ,ξ))]dξ. Nowrecall that the integration ∫ Ξ commuteswith thedifferentiation d dθ. Therefore,∀i, j≤dim(Θ)onehas ∂2a ∂θi∂θj (θ)= ∫ Ξ P(θ0,ξ) ∂2(ψ+ log(P)) ∂θi∂θj (θ,ξ)dξ. 195
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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