Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Faster Algorithms for Max-Product Message-Passing   Maximum A Posteriori inference in graphical models is often solved via
message-passing algorithms, such as the junction-tree algorithm, or loopy
belief-propagation. The exact solution to this problem is well known to be
exponential in the size of the model's maximal cliques after it is
triangulated, while approximate inference is typically exponential in the size
of the model's factors. In this paper, we take advantage of the fact that many
models have maximal cliques that are larger than their constituent factors, and
also of the fact that many factors consist entirely of latent variables (i.e.,
they do not depend on an observation). This is a common case in a wide variety
of applications, including grids, trees, and ring-structured models. In such
cases, we are able to decrease the exponent of complexity for message-passing
by 0.5 for both exact and approximate inference.

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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: atom-ph
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg