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The recently introduced theory of compressed sensing (CS) enables the reconstruction of sparse signals from a small set of linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist rate samples. However, despite the intense focus on the reconstruction of signals, many signal processing problems do not require a full reconstruction of the signal and little attention has been paid to doing inference in the CS domain. In this paper we showdoi:10.1109/icsmc.2011.6084184 dblp:conf/smc/TuckerK11 fatcat:xipag7anibczdn4voldkqv4hpy