Kernel Density Estimation

Kernel Density Estimation (KDE) is a non-parametric method used to estimate the probability density function of a random variable. It is a technique that allows us to estimate the underlying distribution of a dataset. KDE works by placing a kernel function at each data point and then summing up the contributions of all the kernels to estimate the density at any given point. The bandwidth of the kernel function determines the smoothness of the estimated density. A smaller bandwidth results in a more detailed but noisy density estimate, while a larger bandwidth results in a smoother but less detailed density estimate. KDE is commonly used in exploratory data analysis, data visualization, and statistical inference.
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