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This paper describes stochastic search approaches, including a new stochasticalgorithm and an adaptive mutation operator, for learning Bayesian networksfrom incomplete data. This problem is characterized by a huge solution spacewith a highly multimodal landscape. State-of-the-art approaches all involveusing deterministic approaches such as the expectation-maximization algorithm.These approaches are guaranteed to find local maxima, but do not explore thelandscape for other modes. Our approach evolves structure and the missing data.We compare our stochastic algorithms and show they all produce accurateresults.

Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms