高斯混合模型的一种新的统计极大运算及其评价

S. Tsukiyama, M. Fukui
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引用次数: 1

摘要

在统计静态时序分析(S-STA)中,将门延迟、信号到达时间、松弛时间等时序信息视为随机变量,统计最大运算是重要的基础运算。由于两个高斯随机变量的最大值不是高斯的,因此提出了各种表示非高斯分布的技术。其中,高斯混合模型与其他模型的不同之处在于,它可以很容易地处理各种相关性、非高斯分布和旋转分布,这在S-STA中很重要。本文提出了一种考虑累积分布函数曲线的高斯混合模型统计极大值运算,并给出了一些实验结果来评价其性能。
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A new statistical maximum operation for Gaussian mixture models and its evaluations
In the statistical static timing analysis (S-STA), the timing information, such as a gate delay, a signal arrival time, and a slack, is treated as a random variable, and the statistical maximum operation is an important basic operation. Since the maximum of two Gaussian random variables is not Gaussian, various techniques for representing a non-Gaussian distribution have been proposed. Among them, the Gaussian mixture model is distinguished from the others in that it can handle various correlations, non-Gaussian distributions, and slew distributions easily, which are important in S-STA. In this paper, we propose a new statistical maximum operation for Gaussian mixture models, which takes the cumulative distribution function curve into account, and show some experimental results to evaluate its performance.
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