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Entropy2016,18, 425 Therefore, the metric on HFM can be extended to a Riemannian metric on FM. Such metrics incorporate theanisotropicallyweightedmetriconHFM,however,allowingverticalvariationsand thus thatcovariancescanchangeunrestricted. WhenM isRiemannian, themetricgFM is inadditionrelatedtotheSasaki–MokmetriconFM [18] thatextends theSasakimetriconTM.As for theaboveRiemannianmetriconFM, theSasaki–Mok metricallowspaths inFM tohavederivatives in thevertical spaceVFM.OnHFM, theRiemannian metric gR is here lifted to themetric gSM = (vu,wu) = gR(π∗(vu),π∗(wu)) (i.e., themetric is not anisotropicallyweighted). The lineelement is in thiscaseds2= gijdxidxj+XÎČαgijDαiD ÎČj. GeodesicsforgSMareliftsofRiemanniangeodesicsforgRonM, incontrasttothesub-Riemannian normalgeodesics forgFMwhichwewill characterizebelow.Thefamilyofcurvesarisingasprojections to M of normal geodesics for gFM includesRiemanniangeodesics for gR (and thusprojections of geodesics forgSM),but the family is ingeneral larger thangeodesics forgR. 4.ConstrainedEvolutions Extremalpaths for (5) canbe interpretedasmostprobablepaths for thedrivingprocessWtwhen u0 deïŹnesananisotropicdiffusion. This iscaptured in the followingdeïŹnition[3]: DeïŹnition1. Amostprobablepath for thedrivingprocess (MPP) fromx=π(u0)∈Mtoy∈Misasmooth pathxt : [0,1]→Mwithx0= xandx1= ysuch that its anti-developmentϕ−1u0 (xt) is amostprobablepath forWt; i.e., xt∈ argminσ,σ0=x,σ1=y ∫ 1 0 −L Rd(ϕ −1 u0 (σt), d dtϕ −1 u0 (σt))dt withL Rd being theOnsager–Machlup function for theprocessWt onR d [22]. The deïŹnition uses the one-to-one relation between W(Rd) and W(M) provided by ϕu0 to characterize the paths using the Rd Onsager–Machlup function L Rd. When M is Riemannian with metric gR, the Onsager–Machlup function for a g-Brownian motion on M is L(xt, x˙t) = −12‖x˙t‖2gR + 112SgR(xt)with SgR denoting the scalar curvature. This curvature term vanishes on Rd, andthereforeL Rd(Îłt,γ˙t)=−12‖γ˙t‖2 foracurveÎłt∈Rd. Bypullingxt∈Mback toRdusingϕ−1u0 , theconstructionremoves the 112SgR(xt) scalarcurvature correctiontermpresent inthenon-EuclideanOnsager–Machlupfunction. It therebyprovidesarelation betweengeodesicenergyandmostprobablepaths for thedrivingprocess. This is contained in the followingcharacterizationofmostprobablepaths for thedrivingprocess as extremalpathsof the sub-Riemanniandistance [3] that followsfromtheEuclideanspaceOnsager–Machluptheorem[22]. Theorem1 ([3]). LetQ(u0)denote theprincipal sub-bundleofFMofpointsz∈FMreachable fromu0∈FM byhorizontalpaths. Suppose theHörmander condition is satisïŹedonQ(u0), and thatQ(u0)has compactïŹbers. Then,most probable paths fromx0 to y∈Mfor the driving process of Xt exist, and they are projections of sub-Riemanniangeodesics inFMminimizing the sub-Riemanniandistance fromu0 toπ−1(y). Below,wewillderiveevolutionequations for thesetofsuchextremalpaths thatcorrespondto normalsub-Riemanniangeodesics. 4.1.NormalGeodesics for gFM Connectedto themetricgFM is theHamiltonian H(z)= 1 2 (z|gFM(z)) (12) 412
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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