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Entropy2016,18, 383 Proposition1. Let s∈Rr>0. Forξ∈Pāˆ—V, onehas: Ī”āˆ’s(Js(ξ))āˆ’1=( r āˆ k=1 sskk )Ī“āˆ’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 āˆ’s1 11 . When r>1,assumethat (45)holds forasystemofrank rāˆ’1.Wededuce from(31), (33), (43), (44) andthe inductionhypothesis that (45)holds forξ∈Pāˆ—V∩Z0V. Therefore, (45)holds forallξ∈Pāˆ—V by (30), (34)and(42). Ingeneral, foranon-zerofunction f, thefunction 1fā—¦(āˆ‡log f)āˆ’1 iscalledthemultiplicativeLegendre transformof f. Thanks toTheorem5andProposition 1,we see that themultiplicative Legendre transformofĪ”āˆ’s(x) isequal toĪ“āˆ’s(āˆ’Ī¾)onāˆ’Pāˆ—V uptoconstantmultiple.Asacorollary,wearriveat the followingresult. Theorem6. TheFenchel–Legendre transformof the convex function logĪ”āˆ’s onPV is equal to the function logĪ“āˆ’s(āˆ’Ī¾)ofĪ¾āˆˆāˆ’Pāˆ—uptoconstantaddition. 6.ApplicationtoStatisticsandOptimization Take s∈Rr forwhich sk> qk/2 (k=1,. . . ,r).WedefineameasureρVs onPāˆ—V by: ρVs (dξ) :=Cāˆ’1V Ī“V(s) āˆ’1Ī“Vs (ξ)Ļ•V(ξ)dξ (ξ∈Pāˆ—V). (46) Theorem4states that: ∫ Pāˆ—V eāˆ’(x|ξ)ρVs (dξ)=Ī”Vāˆ’s(x) (x∈PV). Then, we obtain the natural exponential family generated by ρVs , that is a family {μVs,x}x∈PV of probabilitymeasuresonPāˆ—V givenby: μVs,x(dξ) :=Ī”Vs (x)eāˆ’(x|ξ)ρVs (dξ). In particular, when s = (n1α,n2α, . . . ,nrα) for sufficiently large α, we have μVs,x(dξ) = (detx)αeāˆ’(x|ξ)ρVs (dξ).WecallμVs,x theWishartdistributionsonPāˆ—V ingeneral. Fromasampleξ0∈Pāˆ—V, letusestimate theparameterx∈PV insuchawaythat the likelihood functionĪ”Vs (x)eāˆ’(x|ξ) attains itsmaximumat theestimatorx0. Then,wehavethe likelihoodequation ξ0=IVs (x0),whereasTheorem5givesauniquesolutionbyx0=JVs (ξ0). Thesameargument leadsus to the followingresult in semidefiniteprogramming. Forafixed ξ0∈Pāˆ—V andα>0,auniquesolutionx0 of theminimizationproblemof (x|ξ0)āˆ’Ī±logdetx subject to x∈PV=ZV∩Pn isgivenbyx0=JVs (ξ0),where s=(n1α, . . . ,nrα).Note thatJVs isarationalmap becauseĪ“Vs 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
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Differential Geometrical Theory of Statistics
Title
Differential Geometrical Theory of Statistics
Authors
FrƩdƩric 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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