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Version du 15 décembre 2020 à 19:09
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Kernel Density Estimation The Kernel Density Estimation is a mathematic process of finding an estimate probability density function of a random variable. The estimation attempts to infer characteristics of a population, based on a finite data set. The data smoothing problem often is used in signal processing and data science, as it is a powerful way to estimate probability density. In short, the technique allows one to create a smooth curve given a set of random data. However, the estimation can also be used to generate points that only appear to have come from a specific sample set. This feature is particularly useful in project simulations and in object modeling.
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