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dc.contributor.authorWILSON, SIMONen
dc.date.accessioned2010-08-20T16:01:59Z
dc.date.available2010-08-20T16:01:59Z
dc.date.issued2010en
dc.date.submitted2010en
dc.identifier.citationSimon P. Wilson, Ercan E. Kuruoğlu and Alicia Quirós Carretero, Bayesian factor analysis using Gaussian mixture sources, with applicatin to separation of the cosmic microwave background, Proceedings of the 2010 IAPR Workshop on Cognitive Information Processing,, 2010 IAPR Workshop on Cognitive Information Processing,, Elba, Italy, 2010, 198-202en
dc.identifier.otherYen
dc.descriptionPUBLISHEDen
dc.descriptionElba, Italyen
dc.description.abstractIn this paper a fully Bayesian factor analysis model is developed that assumes a very general model for each factor, namely the Gaussian mixture. We discuss the cases where factors are both independent and dependent. In the statistical literature, factor analysis has been used principally as a dimension reduction technique, with little interest in a priori modelling of the factors, but here the application is source separation where the factors may have a direct interpretation and the usual Gaussian model for a factor may not be appropriate. That is the case for the application that illustrates our work, which is that of identifying different sources of extra-terrestrial microwaves from all-sky images taken at different frequencies. In particular there is interest in separating out the cosmic microwave background (CMB) signal from the other sources. The posterior distribution is computed by Monte Carlo sampling, and the separated sources are estimated as averages of the samples from the posterior distribution. Beyond this, further information can be extracted from the samples if desired, such as: estimates of uncertainty in the separation, like the standard deviation point-wise of the source samples, or functions of interest like the mean of the spectral density of the samples. The ability to do this is one of the principal benefits of the Bayesian approach.en
dc.description.sponsorshipThis work was started under the Network of Excellence MUSCLE, http://www.muscle-noe.org, contract number FP6- 507752, funded by the European Union. Simon Wilson?s work is currently under the STATICA programme, funded by Science Foundation Ireland, contract number IN.1/I1879. Ercan Kuruoglu?s work is partly supported by the Italian Space Agency (ASI) under the contract Planck LFI Phase E2 activity.en
dc.format.extent198-202en
dc.language.isoenen
dc.rightsYen
dc.subjectStatistics and probabilityen
dc.subjectBayesian approachen
dc.titleBayesian factor analysis using Gaussian mixture sources, with applicatin to separation of the cosmic microwave backgrounden
dc.title.alternativeProceedings of the 2010 IAPR Workshop on Cognitive Information Processing,en
dc.title.alternative2010 IAPR Workshop on Cognitive Information Processing,en
dc.typeConference Paperen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/swilsonen
dc.identifier.rssinternalid67835en
dc.subject.TCDThemeSmart & Sustainable Planeten
dc.contributor.sponsorScience Foundation Ireland (SFI)en
dc.identifier.urihttp://hdl.handle.net/2262/40570


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