Fast MCMC separation for MRF modelled astrophysical components

Author

Kayabol, Koray ; Kuruoglu, Ercan E. ; Sankur, Bulent ; Salerno, Emanuele ; Bedini, Luigi

Author_Institution

ISTI, CNR, Pisa, Italy

fYear

2009

fDate

7-10 Nov. 2009

Firstpage

2769

Lastpage

2772

Abstract

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.

Keywords

Bayes methods; Markov processes; Monte Carlo methods; astronomical techniques; source separation; Bayesian source separation problem; Langevin stochastic equation; MRF model; Markov Random Field; Maximum Likelihood approximation; Monte Carlo Markov Chain simulation; fast MCMC separation; unmixing; Bayesian methods; Computational modeling; Convergence; Equations; Monte Carlo methods; Physics; Sampling methods; Smoothing methods; Source separation; Stochastic processes; Astrophysical Component Separation; Bayesian; Langevin equation; MCMC; MRF;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing (ICIP), 2009 16th IEEE International Conference on

Conference_Location

Cairo

ISSN

1522-4880

Print_ISBN

978-1-4244-5653-6

Electronic_ISBN

1522-4880

Type

conf

DOI

10.1109/ICIP.2009.5414190

Filename

5414190