Adaptive Kernel Density Estimation (Adaptive KDE) is a statistical technique used to estimate the probability density function of a random variable by adjusting the bandwidth of the kernel function based on the local density of data points. This method improves the estimation by allowing for variable smoothing, where areas with higher data concentration receive a smaller bandwidth for finer detail, while sparser areas use a larger bandwidth to avoid oversmoothing.
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