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Differential Geometrical Theory of Statistics
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Entropy2016,18, 442 4.1. BasicBounds Forapairofgivenm(x)andm′(x),weonlyneedtoderiveboundsofHα(m :m′) inEquation(31) so thatLα(m :m′)≤Hα(m :m′)≤Uα(m :m′). Then theα-divergenceDα(m :m′) canbebounded byalinear transformationof therange [Lα(m :m′),Uα(m :m′)]. In thefollowingwealwaysassume without lossofgeneralityα≥1/2.OtherwisewecanboundDα(m :m′)byconsideringequivalently theboundsofD1−α(m′ :m). Recall that ineachelementaryslab Ir,wehave max { kw (r)p (r)(x),wδ(r)pδ(r)(x) } ≤m(x)≤ kwδ(r)pδ(r)(x). (32) Notice thatkw (r)p (r)(x),wδ(r)pδ(r)(x), andkwδ(r)pδ(r)(x)areall singlecomponentdistributions up toascalingcoefficient. Thegeneral thinking is toboundthemulti-componentmixturem(x)by singlecomponentdistributions ineachelementary interval, so that the integral inEquation(31)canbe computedinapiecewisemanner. For theconvenienceofnotation,werewriteEquation(32)as cν(r)pν(r)(x)≤m(x)≤ cδ(r)pδ(r)(x), (33) where cν(r)pν(r)(x) := kw (r)p (r)(x) or wδ(r)pδ(r)(x), cδ(r)pδ(r)(x) := kwδ(r)pδ(r)(x). (34) (35) If1/2≤α<1, thenbothxα andx1−α aremonotonically increasingonR+. Thereforewehave Aαν(r),ν′(r)(Ir)≤ ∫ Ir m(x)αm′(x)1−αdx≤Aαδ(r),δ′(r)(Ir), (36) where Aαi,j(I)= ∫ I (cipi(x)) α ( c′jp ′ j(x) )1−α dx, (37) and Idenotesan interval I=(a,b)⊂R. Theothercaseα>1 is similarbynoting thatxα andx1−α are monotonically increasinganddecreasingonR+, respectively. Inconclusion,weobtain the following boundsofHα(m :m′): If 1/2≤α<1, Lα(m :m′)= ∑ r=1 Aαν(r),ν′(r)(Ir), Uα(m :m ′)= ∑ r=1 Aαδ(r),δ′(r)(Ir); (38) ifα>1, Lα(m :m′)= ∑ r=1 Aαν(r),δ′(r)(Ir), Uα(m :m ′)= ∑ r=1 Aαδ(r),ν′(r)(Ir). (39) The remaining problem is to compute the definite integral Aαi,j(I) in the above equations. Here we assume all mixture components are in the same exponential family so that pi(x)= p(x;θi)= h(x)exp ( θ i t(x)−F(θi) ) ,whereh(x) isabasemeasure, t(x) isavectorofsufficient statistics, andthe functionF isknownas thecumulantgeneratingfunction. Then it is straightforward fromEquation(37) that Aαi,j(I)= c α i (c ′ j) 1−α ∫ I h(x)exp (( αθi+(1−α)θ′j ) t(x)−αF(θi)−(1−α)F(θ′j) ) dx. (40) 298
zurück zum  Buch Differential Geometrical Theory of Statistics"
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
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Differential Geometrical Theory of Statistics