Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automated Construction of Sparse Bayesian Networks from Unstructured
  Probabilistic Models and Domain Information   An algorithm for automated construction of a sparse Bayesian network given an
unstructured probabilistic model and causal domain information from an expert
has been developed and implemented. The goal is to obtain a network that
explicitly reveals as much information regarding conditional independence as
possible. The network is built incrementally adding one node at a time. The
expert's information and a greedy heuristic that tries to keep the number of
arcs added at each step to a minimum are used to guide the search for the next
node to add. The probabilistic model is a predicate that can answer queries
about independencies in the domain. In practice the model can be implemented in
various ways. For example, the model could be a statistical independence test
operating on empirical data or a deductive prover operating on a set of
independence statements about the domain.

5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat