Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Bayesian Networks with Incomplete Data by Augmentation   We present new algorithms for learning Bayesian networks from data with
missing values using a data augmentation approach. An exact Bayesian network
learning algorithm is obtained by recasting the problem into a standard
Bayesian network learning problem without missing data. To the best of our
knowledge, this is the first exact algorithm for this problem. As expected, the
exact algorithm does not scale to large domains. We build on the exact method
to create an approximate algorithm using a hill-climbing technique. This
algorithm scales to large domains so long as a suitable standard structure
learning method for complete data is available. We perform a wide range of
experiments to demonstrate the benefits of learning Bayesian networks with such
new approach.

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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph