How to build a smooth density estimation for a discrete sample using jittering

Let’s say you have a sample with tied values. If you draw a kernel density estimation (KDE) for such a sample, you may get a serrated pattern like this: KDE requires samples from continuous distributions while tied values arise in discrete or mixture distributions. Even if the original distribution is continuous, you may observe artificial sample discretization due to a limited resolution of the measuring tool. This effect may lead to distorted density plots like in the above picture...

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