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KDE
- Bivariate Kamma Kernel Density Estimate for large data set-optimize method
Parzen-window
- 这是一个有关parzen窗估计的代码，用来估计概率密度函数。采用了方窗、指数窗、高斯窗函数三种核函数，附有matlab程序。-This is an estimate of the code related to parzen window, used to estimate the probability density function. With a side window, the index window, Gaussian window function three kinds of
KernelDensityEstimation
- 实现k维度的核密度估计，包含了点积核和常规核函数的实现-implement k-dimensional kernel density estimation， supports product kernels ，Gausiankernel 。
KernelDensityEstimation
- 核密度估计的Python源代码,用于核方法估计-Kernel Density Estimation
KDE
- 核函数估计(一元，高斯核函数)，包括带宽优化-kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable.
MutualInfo
- 通过高斯核密度估计计算多元变量之间的互信息熵-The mutual information entropy between multivariate variables is calculated by Gaussian kernel density estimation
Nonparametric kernel density
- 计算数据的累计概率密度，采用三次样条插值计算分位点的值，区间预测，里面有具体程序及相关文献。(The cumulative probability density of the calculated data is calculated by three spline interpolation)
盲源分离
- 常用的盲分离算法有二阶统计量方法、高阶累积量方法、信息最大化（ Infomax ）以及独 立成分分析（ ICA ）等。这些方法取得最佳性能的条件总是与源信号的概率密度函数假设有关， 一旦假设的概率密度与实际信号的密度函数相差甚远，分离性能将大大降低。本文提出采用 核函数密度估计的方法进行任意信号源的盲分离，并通过典型算例与几种盲分离算法进行了 性能比较，验证了方法的可行性。(The commonly used blind separation algorithms include
VolSurface
- 波动率曲面matlab实现，可应用于期权市场上的任意期权。(The function VolSurface.m will then: - compute and output the Black-Scholes implied volatility (this will be a matrix). - get and plot the corresponding volatility surface using a kernel (Gaussian) density estimation.)