Kernel Density Estimation (KDE) was first proposed by Rosenblatt and Parzen. Unlike parametric methods that assume a specific distribution and parameters in advance, this method estimates the probability distribution of indicators directly from the given data sample, thus it belongs to non-parametric methods. This method is commonly applied to estimate the probability density of random variables. By utilizing smoothing techniques, it accurately depicts the distribution of random variables using continuous density curves.
