NDLI: Bayesian compressed sensing: Improving inference

Content Provider	IEEE Xplore Digital Library
Author	Karseras, E. Kin Leung Wei Dai
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK (Karseras, E.; Kin Leung; Wei Dai)
Abstract	In this paper we present a set of theoretical results regarding inference algorithms for hierarchical Bayesian networks. More specifically we focus on a specific type of networks which result in highly sparse models for the input. Bayesian inference in these networks usually is based on optimising a non-convex cost function of the model parameters. We extend previous work done in this field by providing some global performance guarantees regarding this cost function. This is the starting point for redesigning the aforementioned algorithms by employing results from well known sparse reconstruction techniques. This contribution comes in the form of three theorems. The end result is a new view of the Bayesian sparse reconstruction problem.
Starting Page	365
Ending Page	369
File Size	251296
Page Count	5
File Format	PDF
ISBN	9781479910434
DOI	10.1109/ChinaSIP.2013.6625362
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Hierarchical Noise Signal processing algorithms Bayesian Subspace Cost function Inference algorithms Bayes methods Pursuit Compressed sensing
Content Type	Text
Resource Type	Article

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