Seite - 356 - in Differential Geometrical Theory of Statistics
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Entropy2016,18, 396
Theresultsarepartlyextractedfromtheconferencepaper [17].Dataused in theexperimental tests
are radarobservations fromTHALESX-bandRadar, recordedduring2014field trials campaignat
ToulouseBlagnacAirport forEuropeanFP7UFOstudy (Ultra-FastwindsensOrs forwake-vortex
hazardsmitigation) (see [29,30]).DataarerepresentativeofTurbulentatmospheremonitoredbyradar.
Figure2 illustrates thedensityestimationofsixcoefficientsonthePoincaréunitdiskunderarainy
environment. Thedensitiesareindividuallyre-scaledforvisualizationpurposes. Foreachenvironment,
thedataset is composedof120draws. Thedensitiesof thecoefficientsΩk are representativeof the
background.This informationonthebackgroundisexpectedtoeasethedetectionof interestingtargets.
Ω1 Ω2 Ω3
Ω4 Ω5 Ω6
Figure2.Estimationof thedensityof sixcoefficientsΩkunderrainyconditions. Theexpressionof the
usedkernel isK(x)= 3π(1−x2)21x<1.Densitiesarerescaledforvisualpurposes.
356
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