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: WiFi fingerprint-based localization is one of the most attractive and promising techniques targeted for indoor localization, and has attained much attention in the past decades. In addition ...

Density estimation is a fundamental component in statistical analysis, aiming to infer the probability distribution of a random variable from a finite sample without imposing restrictive parametric ...

After publication of an earlier version of this paper, we received feedback that there were several incorrect references to related methods in the literature. These errors are corrected in the current ...

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

This project implements and compares nonparametric estimators for convolution densities ψ = f ⋆ g, where f and g are unknown probability density functions. The implementation uses higher-order kernels ...

Abstract: In practical classification problems, imbalanced class distribution is very common. Traditional classification methods usually aim at optimizing the overall accuracy, which leads to ...