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Kernel density estimation is a way to estimate the probability density function PDF of a random variable in a non-parametric way. It includes automatic bandwidth determination. The estimation works best for a unimodal distribution; bimodal or multi-modal distributions tend to be oversmoothed.

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Author information: 1 Department of Psychology, University of Kansas. Exploratory data analysis EDA can reveal important features of underlying distributions, and these features often have an impact on inferences and conclusions drawn from data. Graphical analysis is central to EDA, and graphical representations of distributions often benefit from smoothing.

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Rule of thumb : A method or procedure derived entirely from practice or experience [. She has taught writing for more than 25 years--primarily at Hinds Community College in Raymond, Mississippi, and at Nassau Community College--and has conducted writing workshops for many organizations. Besides teaching writing for over thirty years, both in Texas and New York, she has studied and taught film criticism.

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In statisticskernel 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 Rosenblattwho are usually credited with independently creating it in its current form.

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Kernel density estimation is a way to estimate the probability density function PDF of a random variable in a non-parametric way. It includes automatic bandwidth determination. The estimation works best for a unimodal distribution; bimodal or multi-modal distributions tend to be oversmoothed.

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