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Entropy2016,18, 407
Usingexpression(25) tocalculate thederivatives in (27),wecanexpress
Γijk=E′′θ [∂2ϕ−1(pθ)
∂θi∂θj ∂ϕ−1(pθ)
∂θk ]
+ 1
2 E′′′θ [∂ϕ−1(pθ)
∂θi ∂ϕ−1(pθ)
∂θj ∂ϕ−1(pθ)
∂θk ]
− 1
2 E′′θ [∂ϕ−1(pθ)
∂θi ∂ϕ−1(pθ)
∂θk ]
E′′θ [
u0 ∂ϕ−1(pθ)
∂θj ]
− 1
2 E′′θ [∂ϕ−1(pθ)
∂θj ∂ϕ−1(pθ)
∂θk ]
E′′θ [
u0 ∂ϕ−1(pθ)
∂θi ]
+ 1
2 E′′θ [∂ϕ−1(pθ)
∂θi ∂ϕ−1(pθ)
∂θj ]
E′′θ [
u0 ∂ϕ−1(pθ)
∂θk ]
.
Aswewill showlater, theLevi–Civitaconnection∇corresponds to theconnectionderivedfrom
thedivergenceD(α)ϕ (· ‖ ·)withα=0.
4.2. ϕ-Families
Let c: T → R be a measurable function for which p = ϕ(c) is a probability density inPμ.
Fixmeasurable functionsu1, . . . ,un : T→R. A (parametric) ϕ-familyFp= {pθ : θ∈Θ}, centeredat
p=ϕ(c), isasetofprobabilitydistributions inPμ,whosememberscanbewritten in the form
pθ :=ϕ (
c+ n
∑
i=1 θiui−ψ(θ)u0 )
, foreachθ=(θi)∈Θ, (28)
whereψ:Θ→ [0,∞) is anormalizing function,which is introduced so that expression (28)defines
aprobabilitydistributionbelongingtoPμ.
The functionsu1, . . . ,un arenotarbitrary. Theyarechosentosatisfy the followingassumptions:
(i) u0,u1, . . . ,un are linearly independent,
(ii) ∫
Tuiϕ ′(c)dμ=0,and
(iii) thereexists ε>0suchthat ∫
T ϕ(c+λui)dμ<∞, forallλ∈ (−ε,ε).
Moreover, thedomainΘ⊆Rn isdefinedas thesetofallvectorsθ=(θi) forwhich
∫
T ϕ (
c+λ n
∑
i=1 θiui )
dμ<∞, for someλ>1.
Condition(i) implies that themappingdefinedby(28) isone-to-one.Assumption(ii)makesofψ
anon-negative function. Indeed,bytheconvexityofϕ(·), alongwith (ii),wecanwrite
∫
T ϕ(c)dμ= ∫
T [
ϕ(c)+ ( n
∑
i=1 θiui )
ϕ′(c) ]
dμ≤ ∫
T ϕ (
c+ n
∑
i=1 θiui )
dμ,
which impliesψ(θ)≥0. Bycondition(iii), thedomainΘ isanopenneighborhoodof theorigin. If the
setT isfinite, condition(iii) isalwayssatisfied.Onecanshowthat thedomainΘ isopenandconvex.
Moreover, the normalizing function ψ is also convex (or strictly convex if ϕ(·) is strictly convex).
Conditions (ii) and(iii) alsoappears in thedefinitionofnon-parametricϕ-families. For furtherdetails,
werefer to [11,12].
Inaϕ-familyFp, thematrix (gij)givenby(22)or (25)canbeexpressedas theHessianofψ. Ifϕ(·)
is strictlyconvex, then(gij) ispositivedefinite. From
∂ϕ−1(pθ)
∂θi =ui− ∂ψ
∂θi , −∂ 2ϕ−1(pθ)
∂θi∂θj =− ∂ 2ψ
∂θi∂θj ,
it followsthatgij= ∂2ψ
∂θi∂θj .
Thenext tworesults showhowthegeneralizationofRényidivergenceandtheϕ-divergenceare
relatedto thenormalizingfunction inϕ-families.
280
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