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

Autor:	Kayabol, K., Kuruoglu, E. E., Sanz, J. L., Sankur, B., Salerno, E., Herranz, D.
Rok vydání:	2011
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - Cosmology and Nongalactic Astrophysics
Zdroj:	IEEE Transactions on Signal Processing, vol. 19, issue 9, 2010, pp. 2357-2368
Druh dokumentu:	Working Paper
DOI:	10.1109/TIP.2010.2048613
Popis:	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. Comment: 12 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1101.1396 Zobrazit plný text záznamu View this record from Arxiv