Seite - 195 - in Differential Geometrical Theory of Statistics
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Text der Seite - 195 -
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
Differential Geometrical Theory of Statistics
- Titel
- Differential Geometrical Theory of Statistics
- Autoren
- Frédéric Barbaresco
- Frank Nielsen
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2017
- Sprache
- englisch
- Lizenz
- CC BY-NC-ND 4.0
- ISBN
- 978-3-03842-425-3
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 476
- Schlagwörter
- Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
- Kategorien
- Naturwissenschaften Physik