动态贝叶斯网络(Dynamic Bayesian Networks,DBN)是能够对时序数据进行处理的贝叶斯网络,它适用于动态系统的学习和推理,是一种具有很大潜力的数据挖掘技术...
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语音辨識中常用的隐藏式马可夫模型（Hidden Markov Model, HMM）是动态贝式模型（Dynamic Bayesian Network, DBN）的一 个特例。
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discrete dynamic Bayesian network 离散动态贝叶斯网络
Hybrid Dynamic Bayesian Network 混合动态贝叶斯网络
dynamic bayesian network dbn 动态贝叶斯网络 ; 动态贝叶斯网
Hierarchical durational-state dynamic Bayesian network 提出了一种分层且带驻留时间状态的动态贝叶斯网络
continuous dynamic bayesian network 连续动态贝叶斯网络
dynamic bayesian network en-dbn 动态贝叶斯网
Structure Varied Dynamic Bayesian Network 变结构DBN
Hierarchical Multi-obsevation Dynamic Bayesian Network 多观察值层次动态贝叶斯网络模型
In the recognition of dynamic system, different structures of Dynamic Bayesian Network are designed for different problems, in which many kinds of weak information are fused to be strong information.
在动态过程识别中，动态贝叶斯网络可以根据具体问题设计具体的动态贝叶斯网络结构。
参考来源 - 非特定人自然的人体动作识别·2,447,543篇论文数据，部分数据来源于NoteExpress
以上来源于: WordNet
Dynamic Bayesian Network (DBN), because of extensibility, powerful description, inference and learning abilities for the time series, being used in the speech recognition.
动态贝叶斯网络(DBN)，以其扩展性和对时间序列的强大描述、推导和学习能力，逐渐被应用于连续语音识别中。
A new method was represented to model dynamic linear regression system driven by data, in which a bayesian network was combined with the RBF neural network.
结合贝叶斯网络和神经网络，提出了一种建立数据驱动型的动态线性回归系统模型的方法。
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