kerneltiheyden
Kerneltiheyden, typically referred to in English as kernel density estimation (KDE), is a non-parametric method for estimating the probability density function of a random variable from observed data. It produces a smooth density curve without assuming a specific parametric form for the underlying distribution.
The standard estimator is f̂(x) = (1/(n h)) ∑_{i=1}^n K((x − X_i)/h), where X_i are the data, K
Bandwidth selection is central to KDE. A too small h yields a noisy, overfitted estimate; a too
KDE complements histograms by providing a smooth, continuous density estimate and is used in data visualization,