最后给出了用于时间序列分析的动态贝叶斯网络的实例。
In the last, we give an example of dynamical Bayesian networks for time series data analysis.
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。
Dynamic Bayesian Network (DBN), because of extensibility, powerful description, inference and learning abilities for the time series, being used in the speech recognition.
分析了音视频联合建模的层级结构，利用动态贝叶斯网络对不同层级的音视频关联关系建立模型，并基于该模型进行音视频说话人识别的实验。
According to the hierarchical structure of audio-visual bimodal modeling, a new DBN is constructed to describe the natural audio and visual state asynchrony as well as their conditional .
提出了一种基于离散时间贝叶斯网络的动态故障树分析方法。
A new dynamic fault tree analysis method based on discrete-time Bayesian networks is proposed.
运用贝叶斯理论及方法，建立了网络系统风险的动态评估模型。
Based on Bayesian theory and concerning approaches, this paper establishes an evaluating model and gives the corresponding method to evaluate dynamically the network risk.
根据动态联盟企业信息具有不确定性的特点，应用贝叶斯网络对企业的风险概率进行识别。
According to the uncertain characteristics of information in virtual enterprise, the Bayesian network is used to identify its risk probability.
同时，进一步完善了软件项目开发风险管理流程，并利用贝叶斯网络的信念更新过程实现动态软件项目风险管理。
In addition, the flow of risk management in software project development is adjusted and dynamic risk management of software project is realized using BNs belief update.
文中采用贝叶斯正则化与BP网络结合的方法，建立动态前馈校正模型。
This paper establishes dynamic forward feedback correction model with the method of combining Bayes regularization and BP 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。
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.
涵盖各个领域的量化分析结合在一起使得基于贝叶斯网权限图的网络安全评估方法更加准确、精确，更加能够适应动态变化的网络。
The combination of analysis covering different kinds of knowledge makes the network security assessment more accurate, more precise and suits to the changes of networks.
文中采用贝叶斯正则化与BP网络结合的方法，建立动态前馈校正模型。
The BPNN model of Bayesian regularization method was adopted to create the adaptivity and generalization of BPNN.
文中采用贝叶斯正则化与BP网络结合的方法，建立动态前馈校正模型。
The BPNN model of Bayesian regularization method was adopted to create the adaptivity and generalization of BPNN.
应用推荐