Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Variational Inference Framework for Inverse Problems   A framework is presented for fitting inverse problem models via variational
Bayes approximations. This methodology guarantees flexibility to statistical
model specification for a broad range of applications, good accuracy
performances and reduced model fitting times, when compared with standard
Markov chain Monte Carlo methods. The message passing and factor graph fragment
approach to variational Bayes we describe facilitates streamlined
implementation of approximate inference algorithms and forms the basis to
software development. Such approach allows for supple inclusion of numerous
response distributions and penalizations into the inverse problem model. Even
though our work is circumscribed to one- and two-dimensional response
variables, we lay down an infrastructure where efficient algorithm updates
based on nullifying weak interactions between variables can also be derived for
inverse problems in higher dimensions. Image processing applications motivated
by biomedical and archaeological problems are included as illustrations.

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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: math
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci