Bayesian factor analysis using Gaussian mixture sources, with applicatin to separation of the cosmic microwave background
Citation:
Simon 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, 198-202Download Item:
Abstract:
In 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.
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Grant Number
Science Foundation Ireland (SFI)
Author's Homepage:
http://people.tcd.ie/swilsonDescription:
PUBLISHEDElba, Italy
Author: WILSON, SIMON
Other Titles:
Proceedings of the 2010 IAPR Workshop on Cognitive Information Processing,2010 IAPR Workshop on Cognitive Information Processing,
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Conference PaperCollections
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Full text availableKeywords:
Statistics and probability, Bayesian approachSubject (TCD):
Smart & Sustainable PlanetMetadata
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