**Kernel Density Estimation**

In statistics, **kernel density estimation (KDE)** is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the *Parzen–Rosenblatt window* method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form.

Read more about Kernel Density Estimation: Definition, Bandwidth Selection, Relation To The Characteristic Function Density Estimator, Statistical Implementation

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—Ralph Waldo Emerson (1803–1882)

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—Anne Brontë (1820–1849)