Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Compressive Sensing via Belief Propagation   Compressive sensing (CS) is an emerging field based on the revelation that a
small collection of linear projections of a sparse signal contains enough
information for stable, sub-Nyquist signal acquisition. When a statistical
characterization of the signal is available, Bayesian inference can complement
conventional CS methods based on linear programming or greedy algorithms. We
perform approximate Bayesian inference using belief propagation (BP) decoding,
which represents the CS encoding matrix as a graphical model. Fast computation
is obtained by reducing the size of the graphical model with sparse encoding
matrices. To decode a length-N signal containing K large coefficients, our
CS-BP decoding algorithm uses O(Klog(N)) measurements and O(Nlog^2(N))
computation. Finally, although we focus on a two-state mixture Gaussian model,
CS-BP is easily adapted to other signal models.

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The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
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The categories of this paper are: eess
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph