Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating and Sampling Orbits for Lifted Probabilistic Inference   A key goal in the design of probabilistic inference algorithms is identifying
and exploiting properties of the distribution that make inference tractable.
Lifted inference algorithms identify symmetry as a property that enables
efficient inference and seek to scale with the degree of symmetry of a
probability model. A limitation of existing exact lifted inference techniques
is that they do not apply to non-relational representations like factor graphs.
In this work we provide the first example of an exact lifted inference
algorithm for arbitrary discrete factor graphs. In addition we describe a
lifted Markov-Chain Monte-Carlo algorithm that provably mixes rapidly in the
degree of symmetry of the distribution.

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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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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: hep-th
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The categories of this paper are: nucl-th
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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: patt-sol
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The categories of this paper are: alg-geom