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

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

2011-01-07

arxiv.org/abs/1101.1396v1

IEEE Transactions on Signal Processing, vol. 19, issue 9, 2010, pp. 2357-2368

Astronomy and Astrophysics

Astrophysics

Instrumentation and Methods for Astrophysics

12 pages, 6 figures

Scientific paper

10.1109/TIP.2010.2048613

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.

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