Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Sparsity by NUV-EM, Gaussian Message Passing, and Kalman Smoothing   Normal priors with unknown variance (NUV) have long been known to promote
sparsity and to blend well with parameter learning by expectation maximization
(EM). In this paper, we advocate this approach for linear state space models
for applications such as the estimation of impulsive signals, the detection of
localized events, smoothing with occasional jumps in the state space, and the
detection and removal of outliers. The actual computations boil down to
multivariate-Gaussian message passing algorithms that are closely related to
Kalman smoothing. We give improved tables of Gaussian-message computations from
which such algorithms are easily synthesized, and we point out two preferred
such algorithms.

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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: eess
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin