The increasing diversity of scientific and engineering data has driven the development of flexible techniques for inferring probability distributions without assuming a specific parametric family.
Kernel density estimation (KDE) is a versatile nonparametric approach to infer continuous probability distributions from finite samples. By superimposing smooth kernel functions—most commonly Gaussian ...
Authors Jeff Tracey-PR, James K. Sheppard, Glenn K. Lockwood, Amit Chourasia, Mahidhar Tatineni, Robert N. Fisher, Robert S. Sinkovits ...
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