Seite - 412 - in Differential Geometrical Theory of Statistics
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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 inadditionrelatedtotheSasakiâMokmetriconFM [18]
thatextends theSasakimetriconTM.As for theaboveRiemannianmetriconFM, theSasakiâMok
metricallowspaths inFM tohavederivatives in thevertical spaceVFM.OnHFM, theRiemannian
metric gR is here lifted to themetric gSM = (vu,wu) = gR(Ïâ(vu),Ïâ(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 deïŹnesananisotropicdiffusion. This iscaptured in the followingdeïŹnition[3]:
DeïŹnition1. Amostprobablepath for thedrivingprocess (MPP) fromx=Ï(u0)âMtoyâMisasmooth
pathxt : [0,1]âMwithx0= xandx1= ysuch that its anti-developmentÏâ1u0 (xt) is amostprobablepath
forWt; i.e.,
xtâ argminÏ,Ï0=x,Ï1=y â« 1
0 âL Rd(Ï â1
u0 (Ït), d
dtÏ â1
u0 (Ït))dt
withL Rd being theOnsagerâMachlup function for theprocessWt onR d [22].
The deïŹnition uses the one-to-one relation between W(Rd) and W(M) provided by Ïu0 to
characterize the paths using the Rd OnsagerâMachlup function L Rd. When M is Riemannian
with metric gR, the OnsagerâMachlup function for a g-Brownian motion on M is L(xt, xËt) =
â12âxËtâ2gR + 112SgR(xt)with SgR denoting the scalar curvature. This curvature term vanishes on
Rd, andthereforeL Rd(Îłt,ÎłËt)=â12âÎłËtâ2 foracurveÎłtâRd.
BypullingxtâMback toRdusingÏâ1u0 , theconstructionremoves the 112SgR(xt) scalarcurvature
correctiontermpresent inthenon-EuclideanOnsagerâMachlupfunction. It therebyprovidesarelation
betweengeodesicenergyandmostprobablepaths for thedrivingprocess. This is contained in the
followingcharacterizationofmostprobablepaths for thedrivingprocess as extremalpathsof the
sub-Riemanniandistance [3] that followsfromtheEuclideanspaceOnsagerâMachluptheorem[22].
Theorem1 ([3]). LetQ(u0)denote theprincipal sub-bundleofFMofpointszâFMreachable fromu0âFM
byhorizontalpaths. Suppose theHörmander condition is satisïŹedonQ(u0), and thatQ(u0)has compactïŹbers.
Then,most probable paths fromx0 to yâMfor the driving process of Xt exist, and they are projections of
sub-Riemanniangeodesics inFMminimizing the sub-Riemanniandistance fromu0 toÏâ1(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
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