Stochastic Timed Influence Nets

机译：随机定时影响网

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The existing Timed Influence Nets (TIN) framework is assumed that delays on arcs are constant. This constraint may turn out to be unrealistic in many real world situations. The proposed parametric enhancements would overcome the above limitation, and enable a system modeler to specify stochastic delay in a dynamic uncertain situation that the existing TIN fails to capture. The new class of models is named Stochastic Timed Influence Nets (STIN). Both TIN and STIN provide an easy-to-read and compact representation to several time-based probabilistic reasoning paradigms.

机译：假设现有的定时影响网（TIN）框架是弧延迟是恒定的。在许多真实的世界情况下，这一约束可能是不现实的。所提出的参数增强功能将克服上述限制，并使系统建模器能够在现有锡无法捕获的动态不确定情况下指定随机延迟。新一类模型被称为随机定时影响网（Stin）。 TIN和STIN都为几个基于时间的概率推理范例提供了易于阅读和紧凑的表示。

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《International Conference on Computer Application and System Modeling》|2010年||共5页
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作者
Zhu Yan-guang; Lei Yong-lin;
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中图分类 TP3-53;
关键词
probability Propagation algorithm; stochastic belief sequence; stochastic delay; stochastic timed influence nets; timed influence nets;

机译：概率传播算法;随机信念序列;随机延迟;随机定时影响网;定时影响网;

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Stochastic Timed Influence Nets

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