On the Optimal K-term Approximation of a Sparse Parameter Vector MMSE Estimate
2009 (English)In: Proceedings of the 2009 IEEE Workshop on Statistical Signal Processing (SSP'09), IEEE , 2009, 245-248 p.Conference paper (Refereed)
This paper considers approximations of marginalization sums thatarise in Bayesian inference problems. Optimal approximations ofsuch marginalization sums, using a fixed number of terms, are analyzedfor a simple model. The model under study is motivated byrecent studies of linear regression problems with sparse parametervectors, and of the problem of discriminating signal-plus-noise samplesfrom noise-only samples. It is shown that for the model understudy, if only one term is retained in the marginalization sum, thenthis term should be the one with the largest a posteriori probability.By contrast, if more than one (but not all) terms are to be retained,then these should generally not be the ones corresponding tothe components with largest a posteriori probabilities.
Place, publisher, year, edition, pages
IEEE , 2009. 245-248 p.
MMSE estimation, Bayesian inference, marginalization
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-25591DOI: 10.1109/SSP.2009.5278594ISI: 000274988800062ISBN: 978-1-4244-2709-3OAI: oai:DiVA.org:liu-25591DiVA: diva2:246025
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Erik Axell, Erik G. Larsson and Jan-Åke Larsson, On the Optimal K-term Approximation of a Sparse Parameter Vector MMSE Estimate, 2009, Proceedings of the 2009 IEEE Workshop on Statistical Signal Processing (SSP'09), 245-248.