Data Science, Statistics, Machine Learning.
ecdf becomes close to the true cdf. Kernel Density Estimation (KDE) KDE is a non-parametric method to estimate pdf of data generating distribution. KDE allocates high density to certain x if sample data has many datapoints around it. A datapoint’s contribution to certain x depends on its distance