Bayesian Network Induction via Local Neighborhoods

In recent years, Bayesian networks have become highly successful tool for di(cid:173) agnosis, analysis, and decision making in real-world domains. We present an efficient algorithm for learning Bayes networks from data. In contrast to the majority of work, which typically uses hill-climbing approaches that may produce dense and causally incorrect nets, our approach yields much more compact causal networks by heeding independencies in the data. Compact causal networks facilitate fast in(cid:173) ference 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.