  Learning a Bayesian network structure from data is an NP-hard problem and
thus exact algorithms are feasible only for small data sets. Therefore, network
structures for larger networks are usually learned with various heuristics.
Another approach to scaling up the structure learning is local learning. In
local learning, the modeler has one or more target variables that are of
special interest; he wants to learn the structure near the target variables and
is not interested in the rest of the variables. In this paper, we present a
score-based local learning algorithm called SLL. We conjecture that our
algorithm is theoretically sound in the sense that it is optimal in the limit
of large sample size. Empirical results suggest that SLL is competitive when
compared to the constraint-based HITON algorithm. We also study the prospects
of constructing the network structure for the whole node set based on local
results by presenting two algorithms and comparing them to several heuristics.
