Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Bayesian Networks with Restricted Causal Interactions   A major problem for the learning of Bayesian networks (BNs) is the
exponential number of parameters needed for conditional probability tables.
Recent research reduces this complexity by modeling local structure in the
probability tables. We examine the use of log-linear local models. While
log-linear models in this context are not new (Whittaker, 1990; Buntine, 1991;
Neal, 1992; Heckerman and Meek, 1997), for structure learning they are
generally subsumed under a naive Bayes model. We describe an alternative
interpretation, and use a Minimum Message Length (MML) (Wallace, 1987) metric
for structure learning of networks exhibiting causal independence, which we
term first-order networks (FONs). We also investigate local model selection on
a node-by-node basis.

2
The categories of this paper are: math
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph