Web-Books
im Austria-Forum
Austria-Forum
Web-Books
Naturwissenschaften
Physik
Differential Geometrical Theory of Statistics
Seite - 247 -
  • Benutzer
  • Version
    • Vollversion
    • Textversion
  • Sprache
    • Deutsch
    • English - Englisch

Seite - 247 - in Differential Geometrical Theory of Statistics

Bild der Seite - 247 -

Bild der Seite - 247 - in Differential Geometrical Theory of Statistics

Text der Seite - 247 -

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
zurück zum  Buch Differential Geometrical Theory of Statistics"
Differential Geometrical Theory of Statistics
Titel
Differential Geometrical Theory of Statistics
Autoren
FrƩdƩric 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
Schlagwƶrter
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
Web-Books
Bibliothek
Datenschutz
Impressum
Austria-Forum
Austria-Forum
Web-Books
Differential Geometrical Theory of Statistics