叶斯网络(Bayesian Networks),又称信度网(Belief Nnetworks),因果网(Causal Networks) 或概率网(Probabilistic Networks),是当今人工智能领域不确定知识表达和推理技术的主流方法 [13-16] ,这主要归功于贝叶斯网络良好的知识表达框架。
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针对序贯概率比检验(SPRT)无法控制抽取样本量等不足之处,提出了一种改进的抽样检验方法——序贯网图检验。
This paper proposed a new sampling plan, the sequential mesh test, in order to overcome the disadvantages of the widely used Sequential Probability Ratio Test (SPRT).
提出了一种被动声传感器网时延概率定位的综合处理算法。
A passive acoustic sensor network time-delay probabilistic localization algorithm is present in this paper.
所提出计算模型为贝叶斯网的概率推理提供了一种新的局部计算方法。
The proposed computation models will supply new local computation methods for Bayesian network probabilistic inferences.
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