Seite - 247 - in Differential Geometrical Theory of Statistics
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Entropy2016,18, 383
Proposition1. Let s∈Rr>0. Forξ∈P∗V, onehas:
Δ−s(Js(ξ))−1=( r
∏
k=1 sskk )δ−s(ξ). (45)
Proof. Weprove the statementby inductionon the rank. When r= 1, theequality (45) isverified
directly. Indeed, the left-handsideof (45) is computedas ( s1ξ11) s1 = ss11 ξ −s1
11 .
When r>1,assumethat (45)holds forasystemofrank r−1.Wededuce from(31), (33), (43), (44)
andthe inductionhypothesis that (45)holds forξ∈P∗V∩Z0V. Therefore, (45)holds forallξ∈P∗V by
(30), (34)and(42).
Ingeneral, foranon-zerofunction f, thefunction 1f◦(∇log f)−1 iscalledthemultiplicativeLegendre
transformof f. Thanks toTheorem5andProposition 1,we see that themultiplicative Legendre
transformofΔ−s(x) isequal toδ−s(−ξ)on−P∗V uptoconstantmultiple.Asacorollary,wearriveat
the followingresult.
Theorem6. TheFenchel–Legendre transformof the convex function logΔ−s onPV is equal to the function
logδ−s(−ξ)ofξ∈−P∗uptoconstantaddition.
6.ApplicationtoStatisticsandOptimization
Take s∈Rr forwhich sk> qk/2 (k=1,. . . ,r).WedefineameasureρVs onP∗V by:
ρVs (dξ) :=C−1V ΓV(s) −1δVs (ξ)ϕV(ξ)dξ (ξ∈P∗V). (46)
Theorem4states that: ∫
P∗V e−(x|ξ)ρVs (dξ)=ΔV−s(x) (x∈PV).
Then, we obtain the natural exponential family generated by ρVs , that is a family {μVs,x}x∈PV of
probabilitymeasuresonP∗V givenby:
μVs,x(dξ) :=ΔVs (x)e−(x|ξ)ρVs (dξ).
In particular, when s = (n1α,n2α, . . . ,nrα) for sufficiently large α, we have μVs,x(dξ) =
(detx)αe−(x|ξ)ρVs (dξ).WecallμVs,x theWishartdistributionsonP∗V ingeneral.
Fromasampleξ0∈P∗V, letusestimate theparameterx∈PV insuchawaythat the likelihood
functionΔVs (x)e−(x|ξ) attains itsmaximumat theestimatorx0. Then,wehavethe likelihoodequation
ξ0=IVs (x0),whereasTheorem5givesauniquesolutionbyx0=JVs (ξ0).
Thesameargument leadsus to the followingresult in semidefiniteprogramming. Forafixed
ξ0∈P∗V andα>0,auniquesolutionx0 of theminimizationproblemof (x|ξ0)−αlogdetx subject to
x∈PV=ZV∩Pn isgivenbyx0=JVs (ξ0),where s=(n1α, . . . ,nrα).Note thatJVs isarationalmap
becauseδVs isaproductofpowersof rational functions.
7. SpecialCases
7.1.MatrixRealizationofHomogeneousCones
Letusassumethat thesystemV={Vlk}1≤k<l≤r satisfiesnotonly theconditions (V1)and(V2),
butalso the following:
(V3)A∈Vlk,B∈Vkj⇒AB∈Vlj (1≤ j< k< l≤ r).
247
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