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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
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Differential Geometrical Theory of Statistics
Title
Differential Geometrical Theory of Statistics
Authors
Frédéric Barbaresco
Frank Nielsen
Editor
MDPI
Location
Basel
Date
2017
Language
English
License
CC BY-NC-ND 4.0
ISBN
978-3-03842-425-3
Size
17.0 x 24.4 cm
Pages
476
Keywords
Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
Categories
Naturwissenschaften Physik
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Differential Geometrical Theory of Statistics