kernel of density function

基本解释

In this paper, a new kernel estimator of multivariate density is proposed by using a univariate kernel function.
本文提出了利用一维核函数构造多维密度函数一个新估计的方法。
The background samples are chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation is used to estimate the probability density function of background intensity.
通过相隔固定的帧差值阅值化得到背景样本值，并采用高斯核密度估计方法计算背景灰度的概率密度函数。
Secondly, with the comprehensive consideration of continuous and discrete variables, the hybrid sampling density function is received through kernel density evaluation.
然后综合考虑连续变量与离散变量，利用核密度估计构建混合核抽样密度函数；
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