PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Kayabol K., Kuruoglu E. E., Sankur B., Salerno E., Bedini L. Fast MCMC separation for MRF modelled astrophysical components. In: ICIP 2009 - IEEE 16th International Conference on Image Processing (Cairo, Egypt, 7-10 November 2009). Proceedings, pp. 2769 - 2772. Ahmed Tewfik, Yassin Hasan. IEEE Signal Processing Society, 2009.
 
 
Abstract
(English)
We propose an adaptive Monte Carlo Markov Chain (MCMC) simulation for the Bayesian source separation problem and apply it to the unmixing of astrophysical components. In this method, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and which reduces the computation time significantly (by two orders of magnitude). In addition to this, the parameters of the Markov Random Field (MRF) model are updated via Maximum Likelihood (ML) throughout the iterations.
Subject Astrophysical component separation
Bayesian
Markov Random Fields
Markov Chain Monte Carlo
Langevin Equation
G.1.6 Optimization. Global optimization
J.2 Physical sciences and engineering. Astronomy
I.4.3 Enhancement. Filtering


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