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 ...
This paper proposes a precise line loss rate probability density estimation method using the Bilateral Total Variation (BTV) filtering algorithm to suppress noise while preserving edge information in ...
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