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Differential Geometrical Theory of Statistics
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Entropy2016,18, 383 LetW˜k (k=1,. . . ,r)bethevectorspaceofW∈Mat(n,nk,R)of the form: W= ⎛⎜⎜⎜⎜⎜⎜⎝ 0n1+···+nk−1,nk Xkk Xk+1,k ... Xrk ⎞⎟⎟⎟⎟⎟⎟⎠ (Xkk= xkkInk, xkk∈R,Xlk∈Vlk, l> k). Clearly, the space W˜k is isomorphic to R⊕∑l>kVlk, which implies dimW˜k = 1+ qk with qk := ∑l>knlk. Gathering orthogonal bases of Vlk’s, we take a basis of W˜k, so that we have an isomorphismW˜k W →w=vect(W)∈R1+qk,where thefirstcomponentw1 ofw isassumedtobe xkk. Letus introducea linearmapφk :ZV→Sym(1+qk,R)definedinsuchawaythat: (WtW|ξ)= twφk(ξ)w (ξ∈ZV, W∈W˜k,w=vect(W)∈R1+qk). (8) It iseasy tosee thatφr(ξ)= ξrr forξ∈ZV. Theorem1. Thedual coneP∗V ⊂ZV ofPVwithrespect to the standard innerproduct isdescribedas: P∗V={ξ∈ZV ; φk(ξ) ispositivedefinite for all k=1,. . . ,r} ={ξ∈ZV ; detφk(ξ)>0 for all k=1,. . . ,r} . (9) Proof. Weshallprove thestatementby inductionontherank r.When r=1,wehaveφ1(ξ)= ξ11 and ξ= ξ11In1. Thus, (9)holds in thiscase. Letusassumethat (9)holdswhentherank issmaller than r. Inparticular, thestatementholds for P∗V′ ⊂ZV′, that is, P∗V′= { ξ′ ∈ZV′ ; φ′k(ξ′) ispositivedefinite forallk=1,. . . ,r−1 } = { ξ′ ∈ZV′ ; detφ′k(ξ′)>0 forallk=1,. . . ,r−1 } , whereφ′k isdefinedsimilarly to (8) forZV′.Ontheotherhand, if: ξ= ( ξ11In1 tV V ξ′ ) (ξ11∈R,V∈W, ξ′ ∈ZV′), (10) weobserve that: φk(ξ)=φ ′ k−1(ξ ′) (k=2,. . . ,r). Therefore, inorder toprove(9) forP∗V ofrank r, it suffices toshowthat: P∗V= { ξ∈ZV ; ξ′ ∈P∗V′ andφ1(ξ) ispositivedefinite } = { ξ∈ZV ; ξ′ ∈P∗V′ and detφ1(ξ)>0 } . (11) Ifq1=0, thenanyelementξ∈ZV isof the form: ξ= ( ξ11In1 ξ′ ) , whichbelongs toPV if andonly ifξ′ ∈PV′ andφ1(ξ)= ξ11>0, so that (11)holds. 238
zurĂźck zum  Buch Differential Geometrical Theory of Statistics"
Differential Geometrical Theory of Statistics
Titel
Differential Geometrical Theory of Statistics
Autoren
FrĂŠdĂŠric 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
SchlagwĂśrter
Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
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Differential Geometrical Theory of Statistics