Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...

Abstract: Kernel density estimation (KDE), a flexible nonparametric technique unconstrained by specific data distribution assumptions, is extensively employed in fault modeling. However, its ...

The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...

gaussian_kde provides multivariate kernel density estimation (KDE) with Gaussian kernels and optionally weighed data points. Given a dataset $X = {x_1, \cdots, x_n ...

Abstract: Kernel clustering methods have been used successfully to cluster non linearly separable data. In this paper, we propose a modification of the Kernel K-means, called the Multi-Scale Kernel ...