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2010

Adaptive Langevin Sampler for Separation of t-Distribution Modelled Astrophysical Maps

8 years 5 months ago
Adaptive Langevin Sampler for Separation of t-Distribution Modelled Astrophysical Maps
We propose to model the image differentials of astrophysical source maps by Student's t-distribution and to use them in the Bayesian source separation method as priors. We introduce an efficient Markov Chain Monte Carlo (MCMC) sampling scheme to unmix the astrophysical sources and describe the derivation details. In this scheme, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and reduces the computation time significantly (by two orders of magnitude). In addition, Student's t-distribution parameters are updated throughout the iterations. The results on astrophysical source separation are assessed with two performance criteria defined in the pixel and the frequency domains.
Koray Kayabol, Ercan E. Kuruoglu, José Luis
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TIP
Authors Koray Kayabol, Ercan E. Kuruoglu, José Luis Sanz, Bülent Sankur, Emanuele Salerno, Diego Herranz
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