On a Non-Local Search Strategy for Learning in Bayesian Networks
نویسنده
چکیده
In this paper, we propose a learning algorithm which is non-local in the sense that subsequent Bayesian network structures might differ from each other by more than one edge in the learning process. This is achieved by optimizing the orientations of all edges in the graph simultaneously in a heuristic way. As a benefit, the algorithm is quite robust against problems entailed by Markov equivalent Bayesian network structures. Its performance is demonstrated for artificial and real-world data.
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