Kernel density estimation (KDE) is a statistical technique used to estimate the probability density function of a random variable. It creates a smooth curve from discretely sampled data that reflects the underlying density distribution. KDE is widely used in various fields, including signal processing, statistics, machine learning, data visualization, etc…