The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas...The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraint- based, and search-and-score techniques in a principled and ef- fective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.展开更多
针对复杂系统设计过程缺乏早期可靠性评估的问题,提出一种基于模型的系统工程方法支持复杂系统的设计及可靠性评估。结合复杂系统的研发特点,提出一种基于“使命、运行、功能、逻辑、物理、可靠性”的基于模型的系统工程(model-based sy...针对复杂系统设计过程缺乏早期可靠性评估的问题,提出一种基于模型的系统工程方法支持复杂系统的设计及可靠性评估。结合复杂系统的研发特点,提出一种基于“使命、运行、功能、逻辑、物理、可靠性”的基于模型的系统工程(model-based systems engineering, MBSE)建模方法,支持复杂系统设计和可靠性评估;利用基于“图、对象、属性、点、关系、角色”的系统建模语言KARMA对上述过程进行统一表达;通过KARMA的代码生成功能实现图模型到计算模型的映射,完成复杂系统可靠性的评估;将方法应用于液压系统案例,结果表明所提方法对于复杂系统设计和评估具备有效性。展开更多
基金supported by the National Natural Science Fundation of China (6097408261075055)the Fundamental Research Funds for the Central Universities (K50510700004)
文摘The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraint- based, and search-and-score techniques in a principled and ef- fective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.
文摘针对复杂系统设计过程缺乏早期可靠性评估的问题,提出一种基于模型的系统工程方法支持复杂系统的设计及可靠性评估。结合复杂系统的研发特点,提出一种基于“使命、运行、功能、逻辑、物理、可靠性”的基于模型的系统工程(model-based systems engineering, MBSE)建模方法,支持复杂系统设计和可靠性评估;利用基于“图、对象、属性、点、关系、角色”的系统建模语言KARMA对上述过程进行统一表达;通过KARMA的代码生成功能实现图模型到计算模型的映射,完成复杂系统可靠性的评估;将方法应用于液压系统案例,结果表明所提方法对于复杂系统设计和评估具备有效性。
