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Entropy2016,18, 433
Step1
In thepair (F1,gf) theopensubsetU1⊂F1 isdefinedby
x∈U1 if f rank(gf(x))= max
[x∗∈F1] rank(gf(x∗)).
Step2
We iterate this construction. Thenwehaveget afiltrationofM
...⊂Fn⊂Fn−1⊂ ...⊂F1⊂FO=M.
Thisfiltrationhas the followingproperties
(1) Fj−1\Fj is aanalytic submanifoldofM.
(2) gf definesa regularRiemannian foliation in (Fj−1\Fj,gf),
Remark6. Theextrinsicgeometryof submanifolds isaparticularcaseof thegeometryof singular foliation[25].
13.HighlightingConclusions
13.1. Criticisms
InPartBwehaveraisedsomecriticisms.Wehaveconstructedstructuresof statisticalmodels
inflat tori. Anm-dimensionalflat torus isnothomeomorphic toanopensubsetofRm. Thesecond
criticism is the lack of dynamics. Subsequently, the problemofmoduli space is absent from the
classical theory. Thatdeficiency isfilled inbythecharacteristic functor
M=[E,π,M,D,p]→ qM.
Thecurrent theoryrequiresamodel tobe identifiable. Fromtheviewpointof locally trivialfiber
bundles, that requirement isuseless.
13.2. Complexity
In both the theoretical information geometry and the applied information the exponential
models and their generalizations play notable roles. What we call the complexity of a model
[E,π,M,D,p] is its distance from the category of exponential models. Up to today there does
not exist any INVARIANTwhichmeasures how far frombeing an exponential is a givenmodel.
This problemhas a homological nature. Wehave produced a function rb which fills in that lack.
(See theAppendixAbelow).
13.3. KVHomologyandLocalization
Wehave introducedthe theoryofhomologicalmodel.Amongthenotablenotions thatwehave
studied is the localizationofhomological statisticalmodels. It links the theoryofhomologicalmodels
andthecurrent theoryas in [22]. Itmaybe interpretedasa functor fromthe theoryofhomological
models to theclassical theoryofstatisticalmodels.
13.4. TheHomologicalNatureof the InformationGeometry
GM(Ξ,Ω) andHSM(Ξ,Ω) are introduced in this Part B. The category of local statistical
models for (Ξ,Ω) isdenotedbyLM(Ξ,Ω).Ononeside, therightarrowsbelowmeansubcategory.
Thenwehave
LM(Ξ,Ω)→GM(Ξ,Ω)→HSM(Ξ,Ω).
229
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