Seite - 399 - in Differential Geometrical Theory of Statistics
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Text der Seite - 399 -
Entropy2016,9, 337
derivativeγ′ij = γ ′
i(ti) is available,most of the time throughanumerical approximation. Givena
quadrature formulaon [0,1]withpoints tj, j=1,. . . ,mandweightswj, j=1,. . . ,m, thedensitymay
beapproximatedatx∈R2 by:
d˜(x)= 1
∑Ni=1 li N
∑
i=1 m
∑
j=1 wjK (‖x−γij‖)‖γ′ij‖. (28)
where the lengths li, i=1,. . . ,Narealsoobtainedwith thesamequadraturerule:
li= m
∑
j=1 wj‖γ′ij‖.
Whenγ′ij is computedinanumericalway, itmaybeexpressedas:
γ′ij= m
∑
k=1 w˜jkγi,k.
wheretheweights w˜jkareoftenobtainedthroughtheapplicationoftheLagrangeinterpolationformula
toensureexactnessonpolynomialsuptoagivendegree. Inamorecompact form, it canbewritten in
matrix formas: ⎛⎜⎜⎝ γ′i1
...
γ′iq ⎞⎟⎟⎠= W˜ ⎛⎜⎝ γi1...
γiq ⎞⎟⎠
where thematrix W˜ has as entries theweights w˜jk. The cost of evaluating d˜ at a single point is
in o(Nm),with thekernel evaluationbeingdominant. Whendealingwithpoints inR2 orR3 and
compactly-supportedkernels, asimple trickgreatlyreduces the timeneededtocompute d˜. Firstof
all, thedomainof interest isdiscretizedonanevenly-spacedgrid, so that thepointsof evaluation
of the density d˜ are its vertices xij, i = 1,. . . ,nx, j = 1,. . . ,ny. The grid step δx (resp. δy) in the
first (resp. second) coordinate is thedifferencebetweenany twoadjacentvertices δx = xi+1,j−xi,j
(resp.δy= xi,j+1−xi,j (mostof the time,δx= δy). Since theexpression(28) is linear, thecomputation
canbeperformedbyaccumulatingvaluesK(‖xkl−γij‖)‖γ′ij‖ for afixed couple (i, j), where only
the points xkl close enough to γij are considered. In fact, the evaluation can bewritten as a 2D
discreteconvolution:
d˜(xkl)= ∑
i=1,...,N,j=1,...,m wjK(‖xkl−γij‖)‖γ′ij‖. (29)
When the support ofK is small compared to the overall spatial domain,much computation
is saved using this procedure. Furthermore, it can be thought of as 2D filtering, so that highly
efficientalgorithmscomingfromthefieldof imageprocessingcanbeapplied: inparticular, computing
thedensityonagraphicsprocessingunit (GPU) is straightforwardandallowsone todecrease the
computational timebyat leasta factorof ten.Whendealingwith thescaledarclength, thederivative
termisnotpresent,andafactorof li appears in fromof the integral. Thediscreteversionbecomes:
d˜(xkl)= ∑
i=1,...,N,j=1,...,m liwjK(‖xkl−γij‖) (30)
whereγij=γi(ηj),ηjbeing incorrespondencewith tj. Pleasenote that thequadratureweightsmust
be adapted to the abscissa ηj, j= 1,. . . ,m andnot to the tj, j= 1,. . . ,m. Therefore, it is advisable
to resample the curves so that thepoints ηj, j= 1,. . . ,m are, for example, evenly spacedorof the
Gauss–Lobatto form.Theformerwaschosenfor theexperimentsdueto itseaseof implementation,
399
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