Bounded Kernel Density Estimation

<p>Anintuitive idea is to assume that the density function from which the samples are drawn is smooth, and leverage it to fill-in the gaps of our high frequency histogram.</p> <p>This is precisely what the Kernel Density Estimation (KDE) does. It estimates the global density as the average of local density kernels K centered around each sample. A Kernel is a non-negative function integrating to&nbsp;<code>1</code>, e.g uniform, triangular, normal&hellip; Just like adjusting the bin size in a histogram, we introduce a bandwidth parameter&nbsp;<code>h</code>&nbsp;that modulates the deviation of the kernel around each sample point. It thus controls the smoothness of the resulting density estimate.</p> <p><a href="https://towardsdatascience.com/bounded-kernel-density-estimation-2082dff3f47f"><strong>Read More</strong></a></p>