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 <code>1</code>, e.g uniform, triangular, normal… Just like adjusting the bin size in a histogram, we introduce a bandwidth parameter <code>h</code> 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>