Kernel Density Estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. It is a fundamental tool in statistics used to describe the underlying distribution of data points without assuming any specific distribution. KDE is particularly useful in the field of nanotechnology for characterizing the distributions of nanoscale properties such as particle sizes, shapes, and surface charges.
