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dc.contributor.authorTesfamicael, Solomon Abedom
dc.date.accessioned2018-03-22T13:15:01Z
dc.date.available2018-03-22T13:15:01Z
dc.date.created2017-11-07T10:37:05Z
dc.date.issued2017
dc.identifier.isbn978-953-51-3578-4
dc.identifier.urihttp://hdl.handle.net/11250/2491748
dc.description.abstractThis chapter provides the use of Bayesian inference in compressive sensing (CS), a method in signal processing. Among the recovery methods used in CS literature, the convex relaxation methods are reformulated again using the Bayesian framework and this method is applied in different CS applications such as magnetic resonance imaging (MRI), remote sensing, and wireless communication systems, specifically on multiple-input multiple-output (MIMO) systems. The robustness of Bayesian method in incorporating prior information like sparse and structure among the sparse entries is shown in this chapter.nb_NO
dc.language.isoengnb_NO
dc.publisherInTechnb_NO
dc.relation.ispartofBayesian Inference
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleBayesian Inference and Compressed Sensingnb_NO
dc.typeChapternb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber257-278nb_NO
dc.identifier.doi10.5772/intechopen.70308
dc.identifier.cristin1511723
dc.description.localcode© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.nb_NO
cristin.unitcode194,67,80,0
cristin.unitnameInstitutt for lærerutdanning
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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