Kernel Density Estimation in Python Using Scikit-Learn
Introduction This article is an introduction to kernel density estimation using Python’s machine learning library scikit-learn. Kernel density estimation (KDE) is a non-parametric method for estimating the probability density function of a given random variable. It is also referred to by its traditional name, the Parzen-Rosenblatt Window method, after its discoverers. Given a sample of independent, identically distributed (i.i.d) observations ((x_1,x_2,ldots,x_n)) of a random variable from an unknown source distribution, the kernel density estimate, is given by: $$p(x) = frac{1}{nh} […]
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