http://www.cnr.it/ontology/cnr/individuo/prodotto/ID207249
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
- 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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