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- A new method of learning Bayesian networks is presented, which can effectively combine expert knowledge and data. 摘要结合专家知识和数据进行贝叶斯网络学习。
- Presented an efficient hybrid heuristic SGS-EM-PACOB algorithm for learning Bayesian network with mi-ssing values. 建立了具有数据缺失训练集下学习贝叶斯网的一种混合启发方法:SGS-EM-PACOB算法。
- Learning Bayesian Network Structure 贝叶斯网络结构学习分析
- An approach to learning Bayesian networks from small data set 一种基于小数据集的贝叶斯网络学习方法
- Learning Bayesian Network Classifiers Restricted by Class Variable 基于类约束的贝叶斯网络分类器学习
- Improve MDL Principle Used for Learning Bayesian Network Structure 改进学习贝叶斯网络结构的MDL准则
- Learning Bayesian Networks 学习贝叶斯网
- Learning bayesian network 学习贝叶斯网
- At present the Bayesian networks is applied widely in each field. 摘要目前贝叶斯网络在各种领域得到了广泛的应用。
- NIPS Workshop on Learning in bayesian networks and other Graphical Models - 1995. 易易工作室-各种在线工具，站长网志，以及多个应用项目。
- An optimal algorithm for dynamic Bayesian networks (DBN) based on Bayesian optimal algorithm (BOA) is developed for learning and constructing DBN structure. 摘要针对动态贝叶斯网络(DBN)结构学习问题，提出了一种基于贝叶斯优化(BOA)的DBN结构寻优算法。
- Decision tree, neural networks and Bayesian networks are the main tools of KDD. 决策树、神经网络、Bayesian网络等是当前知识发现的重要工具。
- This method can avoid the problems of depending on a large number of data with high quality in existing Bayesian network learning. 该方法可避免现有的贝叶斯网络学习过干依赖数据、对数据的数量和质量要求过高等问题。
- A Bayesian network for overtraining was constructed under this system. 重点讨论了系统的网络数字化、网络学习、网络推理等关键问题。
- The Design and Implementation of Intelligent Program Selection Based on Bayesian Networks P. 基于贝叶斯网络模型的智能节目选择的设计与实现。
- With flexible inference mechanisms,Bayesian networks can describe inherent relations of the system in depth. 贝叶斯网络具有灵活的推理机制,能深刻地揭示系统内在的机理。
- A risk evaluation model in software project investment based on Bayesian Networks（BNs） is presented in this paper. 摘要 提出了一种基于贝叶斯网络的软件项目投资风险评价模型。
- Finally,the process of how to use the Bayesian networks for situation assessment was showed by an example. 最后,给出一个具体的实例,演示了使用贝叶斯网络进行态势估计的过程。
- Finally, the converting of fault tree to Bayesian networks is illustrated according to a instance, and their reasoning functions are compared. 最后通过一个实例说明了故障树向贝叶斯网络的转化过程，并对二者的推理功能进行了比较。
- Markov network is an undirected graph, while Bayesian network is a directed acyclic graph. Markov网是一个无向图 ;而 Bayesian网是一个有向无环图 .
