Kernel Density Estimate
Kernel Density Estimate (KDE) is a non-parametric method used to estimate the probability density function of a random variable. It is a way to estimate the probability density function of a continuous random variable based on a finite data sample. The basic idea behind KDE is to replace each data point with a kernel function, which is a smooth and symmetric probability density function. The kernel function is then used to estimate the probability density function of the random variable at any point in the domain. The bandwidth parameter of the kernel function controls the smoothness of the estimated density function. A larger bandwidth results in a smoother density estimate, while a smaller bandwidth results in a more jagged density estimate.
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