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Entropy2016,18, 442 If1/2≤α<1, then θ¯=αθi+(1−α)θ′jbelongs to thenaturalparameterspaceMθ. ThereforeAαi,j(I) isboundedandcanbecomputedfromtheCDFof p(x; θ¯)as Aαi,j(I)= c α i (c ′ j) 1−αexp(F ( θ¯ )−αF(θi)−(1−α)F(θ′j))∫ I p ( x; θ¯ ) dx. (41) Theothercaseα> 1 ismoredifficult: if θ¯= αθi+(1−α)θ′j still lies inMθ, thenAαi,j(I) canbe computedbyEquation(41).Otherwisewetry tosolve itbyanumerical integrator. This isnot idealas the integralmaydiverge,orourapproximationmaybe too loose toconclude.Wepoint the reader to [42] andEquations (61)–(69) in [35] for relatedanalysiswithmoredetails. As computingAαi,j(I) only requiresO(1) time, theoverall computational complexity (without considering the envelope computation) isO( ). 4.2.AdaptiveBounds Thissectionderives theshape-dependentboundswhich improve thebasicbounds inSection4.1. Wecanrewriteamixturemodelm(x) inaslab Ir as m(x)=wζ(r)pζ(r)(x) ⎛⎝1+ ∑ i =ζ(r) wipi(x) wζ(r)pζ(r)(x) ⎞⎠ , (42) wherewζ(r)pζ(r)(x) isaweightedcomponent inm(x) servingasa reference.Weonlydiscuss thecase that the reference is chosenas thedominatingcomponent, i.e., ζ(r)= δ(r). However it isworth to note that theproposedboundsdonotdependonthisparticularchoice. Therefore theratio wipi(x) wζ(r)pζ(r)(x) = wi wζ(r) exp (( θi−θζ(r) ) t(x)−F(θi)+F(θζ(r)) ) (43) canbebounded ina sub-rangeof [0,1]byanalyzing theextremevaluesof t(x) in the slab Ir. This canbedonebecause t(x)usually consistsofpolynomial functionswithfinite criticalpointswhich canbesolvedeasily.Correspondingly the function ( 1+∑i =ζ(r) wipi(x) wζ(r)pζ(r)(x) ) in Ir canbeboundedina subrangeof [1,k],denotedas [ωζ(r)(Ir),Ωζ(r)(Ir)].Hence ωζ(r)(Ir)wζ(r)pζ(r)(x)≤m(x)≤Ωζ(r)(Ir)wζ(r)pζ(r)(x). (44) This formsbetterboundsofm(x) thanEquation (32)becauseeachcomponent in the slab Ir is analyzedmore accurately. Therefore,we refine the fundamental boundsofm(x)by replacing the Equations (34)and(35)with cν(r)pν(r)(x) :=ωζ(r)(Ir)wζ(r)pζ(r)(x), cδ(r)pδ(r)(x) :=Ωζ(r)(Ir)wζ(r)pζ(r)(x). (45) (46) Then, the improvedboundsofHα aregivenbyEquations (38)and(39)accordingto theabove replaceddefinitionof cν(r)pν(r)(x)and cδ(r)pδ(r)(x). To evaluate ωζ(r)(Ir) andΩζ(r)(Ir) requires iterating through all components in each slab. Therefore thecomputationalcomplexity is increasedtoO( (k+k′)). 4.3.Variance-ReducedBounds This section further improves the proposed bounds based on variance reduction [43]. Byassumption, α≥ 1/2, thenm(x)αm′(x)1−α ismore similar tom(x) rather thanm′(x). The ratio 299
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
Kategorien
Naturwissenschaften Physik
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Differential Geometrical Theory of Statistics