Fast MCMC separation for MRF modelled astrophysical components

Autor:	Kayabol K., Kuruoglu E. E., Sankur B., Salerno E., Bedini L.
Jazyk:	angličtina
Rok vydání:	2009
Předmět:	Markov Random Fields Langevin Equation Markov Chain Monte Carlo Bayesian Statistics::Computation Astrophysical component separation
Zdroj:	IEEE 16th International Conference on Image Processing, pp. 2769–2772, Cairo, Egypt, 7-10 November 2009 info:cnr-pdr/source/autori:Kayabol K.; Kuruoglu E. E.; Sankur B.; Salerno E.; Bedini L./congresso_nome:IEEE 16th International Conference on Image Processing/congresso_luogo:Cairo, Egypt/congresso_data:7-10 November 2009/anno:2009/pagina_da:2769/pagina_a:2772/intervallo_pagine:2769–2772
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::53f2337bb3c2bb01aeaf802040a9673b http://www.cnr.it/prodotto/i/91982 Zobrazit plný text záznamu