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TZID:Europe/Paris
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UID:2346@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20180525T140000
DTEND;TZID=Europe/Paris:20180525T150000
DTSTAMP:20180510T120000Z
URL:https://www.i2m.univ-amu.fr/evenements/statistical-model-of-non-gaussi
 an-process-with-wavelet-scattering-moments/
SUMMARY: (...): Statistical model of non-Gaussian process with wavelet scat
 tering moments
DESCRIPTION:: One of the most challenging problems in statistical modeling 
 is to define a minimal set of statistics so as to infer a stochastic model
  from few observational data of the underlying random process. We propose 
 such set of statistics based on the wavelet scattering transform. Our goal
  is to model the non-Gaussianarity and the long-range interaction of the d
 ata\, in particular when there is complex geometry and transient structure
 s at multiple scales such as Turbulence. We follow the maximum entropy pri
 nciple to infer a stochastic model given a set of statistical moment const
 raints. It results in a Gibbs distribution which is common in statistical 
 physics to describe the equilibrium states. In this talk\, I will discuss 
 the current state-of-art methods to model the texture as a stationary and 
 ergodic random process\, including convolutional neural network based appr
 oach. We compare different methods quantitatively by estimating the power 
 spectrum\, and the entropy of the random process. Numerical results on iso
 tropic Turbulence will be presented.http://www.di.ens.fr/~zhang/
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DTSTART:20180325T030000
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