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Entropy2016,18, 277 Nowassumptions of Theorem3.2.4. from [18] are verified. Thus, using the same lines from theproofof this theorem(invertingall inequalitiessinceweareminimizing insteadofmaximizing), wemayprovethatφ∞ isaglobal infimumof theestimateddivergence, that is Dˆϕ(pφ∞,pφT)≤ Dˆϕ(pφ,pφT), ∀φ∈Φ. Theproblemwith this approach is that it dependsheavily on the fact that the supremumon each step of the algorithm is calculated exactly. This does not happen in general unless function Dˆϕ(pφ,pφT)+βkDψ(φ,φ k) is convexor thatwedisposeofanalgorithmthatcanperfectlysolvenon convexoptimizationproblems (In this case, there isnomeaning inapplyingan iterativeproximal algorithm. We would have used the optimization algorithm directly on the objective function Dˆϕ(pφ,pφT)). Although in our approach, we use a similar assumption to prove the consecutive decreasingofDˆϕ(pφ,pφT),wecanreplacetheinfimumcalculusin(11)bytwothings.Werequireateach stepthatwefindalocal infimumof Dˆϕ(pφ,pφT)+Dψ(φ,φ k)whoseevaluationwithφ → Dˆϕ(pφ,pφT) is less thantheprevious termof thesequenceφk. Ifwecannolongerfindanylocalminimaverifying theclaim, theprocedurestopswithφk+1=φk. Thisensures theavailabilityofall theproofspresented in thispaperwithnochange. 4.2. TheTwo-ComponentGaussianMixture Wesuppose that themodel (pφ)φ∈Φ isamixtureof twogaussiandensities, andthatweareonly interested inestimating themeansμ= (μ1,μ2)∈R2 and theproportionλ∈ [η,1−η]. Theuseof η is toavoidcancellationofanyof the twocomponents, andtokeep thehypothesishi(x|φ)>0 for x= 1,2verified. Wealso suppose that the componentsvariancesare reduced (σi= 1). Themodel takes the form pλ,μ(x)= λ√ 2π e− 1 2(x−μ1)2+ 1−λ√ 2π e− 1 2(x−μ2)2. (17) Here,Φ=[η,1−η]×R2. TheregularizationtermDψ isdefinedby(8)where: hi(1|φ)= λe −12(yi−μ1)2 λe−12(yi−μ1)2+(1−λ)e−12(yi−μ2)2 , hi(2|φ)=1−hi(1|φ). Functions hi are clearly of class C1(int(Φ)), and so doesDψ. Weprove thatΦ0 is closed and bounded,which is sufficient toconclude its compactness, since thespace [η,1−η]×R2 providedwith theeuclideandistance iscomplete. Ifweareusing thedual estimatorof the ϕ−divergencegivenby (2), thenassumptionA0can beverifiedusing themaximumtheoremofBerge [19]. There is still a great difficulty in studying theproperties (closedness or compactness) of the setΦ0. Moreover, all convergenceproperties of thesequenceφk require thecontinuityof theestimatedϕ−divergence Dˆϕ(pφ,pφT)withrespect toφ. Inorder toprove thecontinuityof theestimateddivergence,weneedtoassumethatΦ is compact, i.e., assumethat themeansare includedinanintervalof the form [μmin,μmax].Now,usingTheorem 10.31 from[13],φ → Dˆϕ(pφ,pφT) is continuousanddifferentiablealmosteverywherewithrespect toφ. ThecompactnessassumptionofΦ impliesdirectly thecompactnessofΦ0. Indeed, Φ0 = { φ∈Φ,Dˆϕ(pφ,pφT)≤ Dˆϕ(pφ0,pφT) } = Dˆϕ(pφ,pφT) −1 ( (−∞,Dˆϕ(pφ0,pφT)] ) . Φ0 is then the inverse imagebyacontinuous functionofaclosedset, so it is closed inΦ. Hence, it is compact. 263
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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