Kernel density estimation (KDE) is a cornerstone of non-parametric statistics, offering a flexible means to infer an underlying probability density from finite samples without assuming a predetermined ...
Bernstein polynomial estimators employ weighted sums of Beta basis functions to approximate unknown probability density functions on compact intervals. By representing the target density as a convex ...
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