Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scaling up Probabilistic Inference in Linear and Non-Linear Hybrid
  Domains by Leveraging Knowledge Compilation   Weighted model integration (WMI) extends weighted model counting (WMC) in
providing a computational abstraction for probabilistic inference in mixed
discrete-continuous domains. WMC has emerged as an assembly language for
state-of-the-art reasoning in Bayesian networks, factor graphs, probabilistic
programs and probabilistic databases. In this regard, WMI shows immense promise
to be much more widely applicable, especially as many real-world applications
involve attribute and feature spaces that are continuous and mixed.
Nonetheless, state-of-the-art tools for WMI are limited and less mature than
their propositional counterparts. In this work, we propose a new implementation
regime that leverages propositional knowledge compilation for scaling up
inference. In particular, we use sentential decision diagrams, a tractable
representation of Boolean functions, as the underlying model counting and model
enumeration scheme. Our regime performs competitively to state-of-the-art WMI
systems but is also shown to handle a specific class of non-linear constraints
over non-linear potentials.

5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph