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A stimulus-free graphical probabilistic switching model for sequential circuits using dynamic bayesian networks

机译：使用动态贝叶斯网络的顺序电路的无刺激图形概率切换模型

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Sanjukta Bhanja; N. Ranganathan;
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年度 2004
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1. A Stimulus-Free Graphical Probabilistic Switching Model for Sequential Circuits Using Dynamic Bayesian Networks [J] . SANJUKTA BHANJA, KARTHIKEYAN LINGASUBRAMANIAN, N. RANGANATHAN ACM Transactions on Design Automation of Electronic Systems . 2006,第3期

机译：使用动态贝叶斯网络的时序电路无刺激图形概率切换模型
2. On the relationship between deterministic and probabilistic directed Graphical models: from Bayesian networks to recursive neural networks. [J] . Baldi P, Rosen Zvi M Neural Networks: The Official Journal of the International Neural Network Society . 2005,第8期

机译：关于确定性和概率定向图形模型之间的关系：从贝叶斯网络到递归神经网络。
3. Probabilistic Modeling of QCA Circuits Using Bayesian Networks [J] . Bhanja S., Sarkar S. IEEE transactions on nanotechnology . 2006,第期

机译：贝叶斯网络对QCA电路的概率建模
4. A stimulus-free graphical probabilistic switching model for sequential circuits using dynamic bayesian networks [C] . Sanjukta Bhanja, Karthikeyan Lingasubramanian, N. Ranganathan, Annual conference on Design automation;Conference on Design automation . 2004

机译：使用动态贝叶斯网络的时序电路的无刺激图形概率切换模型
5. A probabilistic and graphical analysis of evidence in O.J. Simpson's murder case using Bayesian networks. [D] . Olumide, Kunle. 2010

机译：O.J.的证据的概率和图形分析辛普森使用贝叶斯网络谋杀案。
6. Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data [O] . Andreas Kuehne, Urs Mayr, Daniel C. Sévin, 2018

机译：代谢网络细分：一种概率图形建模方法，可从非目标代谢组学数据中识别代谢调节的部位和顺序
7. On the Relationship Between Deterministic and Probabilistic Directed Graphical Models: from Bayesian Networks to Recursive Neural Networks [O] . Pierre Baldi A, Michal Rosen-zvi A 2008

机译：确定性与概率有向图模型的关系：从贝叶斯网络到递归神经网络
8. Dynamic Bayesian Networks as a Probabilistic Metamodel for Combat Simulations. [R] . Kelleher, C. T. 2014

机译：动态贝叶斯网络作为战斗模拟的概率元模型。

A stimulus-free graphical probabilistic switching model for sequential circuits using dynamic bayesian networks

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