Joint Bayesian separation and restoration of CMB from convolutional mixtures (Rapporti tecnici/preprint/working paper)

Type
Label
  • Joint Bayesian separation and restoration of CMB from convolutional mixtures (Rapporti tecnici/preprint/working paper) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Alternative label
  • Kayabol Koray, Sanz Jose Luis, Herranz Diego, Kuruoglu Ercan Engin, Salerno Emanuele (2011)
    Joint Bayesian separation and restoration of CMB from convolutional mixtures
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Kayabol Koray, Sanz Jose Luis, Herranz Diego, Kuruoglu Ercan Engin, Salerno Emanuele (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Progetto Cosmologia e Fisica Fondamentale Acronimo COFIS Tipo Progetto NC (literal)
Note
  • uMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR ISTI, Instituto de Fisica de Cantabria, Santander (literal)
Titolo
  • Joint Bayesian separation and restoration of CMB from convolutional mixtures (literal)
Abstract
  • We propose a Bayesian approach to joint source separation and restoration for astrophysical diff use sources. We constitute a prior statistical model for the source images by using their gradient maps. We assume a t-distribution for the gradient maps in di fferent directions, because it is able to fit both smooth and sparse data. A Monte Carlo technique, called Langevin sampler, is used to estimate the source images and all the model parameters are estimated by using deterministic techniques. (literal)
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