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Abstract:
In recent years, Bayes networks have become highly successful
tool for diagnosis, analysis, and decision making in real-world
domains. We present an efficient and robust algorithm for learning
Bayes networks from data. Our approach constructs Bayes networks by
first identifying each node's Markov blankets, then connecting
nodes in a consistent way. In contrast to the majority of work,
which typically uses hill-climbing approaches that may produce
dense nets, our approach yields much more compact networks by
heeding independencies in the data. Compact networks facilitate
fast inference and are also easier to understand. We prove that
under mild assumptions, our approach requires time polynomial in
the size of the data and the number of nodes. A Monte Carlo
variant, also presented here, yields comparable results at much
higher speeds.
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