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 ...
The increasing diversity of scientific and engineering data has driven the development of flexible techniques for inferring probability distributions without assuming a specific parametric family.
The open-access scDIG application combines feature selection, feature-weighted kernel density estimation and interactive ...
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