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 ...

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

How to Call Our MASS_{CR} and MASS_{OPT} Code? In order to compile our C++ code, you need to write the following shell scripts in the ".sh file". g++ -c init_visual.cpp -o init_visual.o g++ -c ...

Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...

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 ...