Journal article  Open Access

Adaptive langevin sampler for separation of t-distribution modelled astrophysical maps

Kayabol K., Kuruoglu E. E., Sanz J. L., Sankur B., Salerno E., Herranz D.

Langevin stochastic equation  Instrumentation and Methods for Astrophysics (astro-ph.IM)  Bayesian source separation  T-distribution  FOS: Physical sciences  Computer Graphics and Computer-Aided Design  Markov Random fields  Student's t-distribution  Image analysis  Software  Cosmology and Nongalactic Astrophysics (astro-ph.CO)  Astrophysics - Instrumentation and Methods for Astrophysics  Astrophysics - Cosmology and Nongalactic Astrophysics 

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.

Source: IEEE transactions on image processing 19 (2010): 2357–2368. doi:10.1109/TIP.2010.2048613

Publisher: Institute of Electrical and Electronics Engineers,, New York, NY , Stati Uniti d'America

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BibTeX entry
	title = {Adaptive langevin sampler for separation of t-distribution modelled astrophysical maps},
	author = {Kayabol K. and Kuruoglu E.  E. and Sanz J.  L. and Sankur B. and Salerno E. and Herranz D.},
	publisher = {Institute of Electrical and Electronics Engineers,, New York, NY , Stati Uniti d'America},
	doi = {10.1109/tip.2010.2048613 and 10.48550/arxiv.1101.1396},
	journal = {IEEE transactions on image processing},
	volume = {19},
	pages = {2357–2368},
	year = {2010}