Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms
This addresses the problem of local maxima in deterministic methods for Bayesian network learning, offering a stochastic alternative for researchers in machine learning and statistics.
The paper tackles learning Bayesian networks from incomplete data, a problem with a huge, multimodal solution space, by introducing stochastic search algorithms including a new algorithm and adaptive mutation operator, and shows they produce accurate results.
This paper describes stochastic search approaches, including a new stochastic algorithm and an adaptive mutation operator, for learning Bayesian networks from incomplete data. This problem is characterized by a huge solution space with a highly multimodal landscape. State-of-the-art approaches all involve using deterministic approaches such as the expectation-maximization algorithm. These approaches are guaranteed to find local maxima, but do not explore the landscape for other modes. Our approach evolves structure and the missing data. We compare our stochastic algorithms and show they all produce accurate results.