Fast MCMC separation for MRF modelled astrophysical components
Kayabol K., Kuruoglu E. E., Sankur B., Salerno E., Bedini L.
Astrophysical component separation Bayesian Markov Random Fields Markov Chain Monte Carlo Langevin Equation
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.
Source: IEEE 16th International Conference on Image Processing, pp. 2769–2772, Cairo, Egypt, 7-10 November 2009
Publisher: Institute of Electrical and Electronic Engineers ;, Red Hook, NY , Stati Uniti d'America
Back to previous page
@inproceedings{oai:it.cnr:prodotti:91982,
title = {Fast MCMC separation for MRF modelled astrophysical components},
author = {Kayabol K. and Kuruoglu E. E. and Sankur B. and Salerno E. and Bedini L.},
publisher = {Institute of Electrical and Electronic Engineers ;, Red Hook, NY , Stati Uniti d'America},
booktitle = {IEEE 16th International Conference on Image Processing, pp. 2769–2772, Cairo, Egypt, 7-10 November 2009},
year = {2009}
}
0000-0003-0053-2800