Kernel Density Estimation explained step by step

Kernel Density Estimator explained step by step

Intuitive derivation of the KDE formula

Introduction

To get a sense of the data distribution, we draw probability density functions (PDF). We are pleased when data fit well to a common density function, such as normal, Poisson, geometrical, etc. Then, the maximum likelihood approach can be used to fit the density function to the data.

Unfortunately, the data distribution is sometimes too irregular and does not resemble …

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