核密度估计（Kernel density estimation，KDE），是一种用于估计概率密度函数的非参数方法。令 \(x_1,x_2,\cdots,x_n\) 为独立同分布 \(F\) 的 \(n\) 个样本点，设其概率密度函数为 \(f\)，核密度估计如下：

nearest_neighbor() is a way to generate a specification of a model before fitting and allows the model to be created using different packages in R. The main arguments for the model are: neighbors: The number of neighbors considered at each prediction. weight_func: The type of kernel function that weights the distances between samples. dist_power: The parameter used when calculating the ...

A kernel is higher-order kernel if > 2: These kernels will have negative parts and are not probability densities. They are also refered to as bias-reducing kernels. Common second-order kernels are listed in the following table Table 1: Common Second-Order Kernels Kernel Equation R(k) 2(k) eff(k) Uniform k 0(u) = 1 2 1(juj 1) 1=2 1=3 1:0758 ...

def Epanechnikov (r): return 0.75 * (1-r ** 2) def T (r): return 1-r: def result_with_weights (dic, kernel): """ Return the key from dic: with max result with weights: counted with kernel function """ dispatcher = {"Epanechnikov": Epanechnikov, "T": T, "quartical": quartical} res = {} max_res = 0: max_label = 0: for key, item in dic. items (): res [key] = sum (map (dispatcher [kernel], item)) Free Online Software (Calculator) computes the Kernel Density Estimation for any data series according to the following Kernels: Gaussian, Epanechnikov, Rectangular, Triangular, Biweight, Cosine, and Optcosine. FIGTree is a fast library that can be used to compute Kernel Density Estimates using a Gaussian Kernel. MATLAB and C/C++ interfaces ...