【摘要】 基于模型选择的 Bayesian网络 ( BN)结构学习是 NP难的可行解搜索过程 .针对现有算法在复杂系统求解中时间效率低的问题 ,提出了一种新的基于最小描述长度 ( minimal description length)理论的结构学习算法 I-B &B-MDL .这种算法将独立性测度与预测估计相结合 ,在学习过程中引入小计算量的独立性测试为 MDL搜索提供启发性知识 ,限制可行解搜索空间 ,从而加速问题求解过程 .针对新算法讨论了改进策略对求解精度的影响 ,并结合算例分析了独立性测试的阶数选择问题 .通过对一实际问题进行验证表明 ,在保证结果精度的前提下 ,新算法在时间性能上比仅基于预测估计的 B & B-MDL 有较大改进更多还原

【Abstract】 Bayesian network structure learning based on model selection is an NP-hard problem. And none of the presented algorithms is perfectly successful in solving the problem of searching efficiency, especially for complex system learning. Presented in this paper is a new independent-MDL-based approach to learn Bayesian network structures. The proposed algorithm limits the searching space by using a set of lower order independence tests, thus executing the MDL-based searching algorithm B & B-MDL to obtain the final graph. The precision analysis of algorithm is presented. And the problem of parameter design is also concerned. The result of the experiment shows that the new algorithm I-B & B-MDL is more efficient in time consumption than B & B-MDL algorithm.更多还原