:: a typeCtrl KGHC 9.10.3 · lts/ghc-9.10.x · c74966e · 2026-09-27
Modulestatistics-0.16.3.0Haskell2010
Kernel density estimation. This module provides a fast, robust, non-parametric way to estimate the probability density function of a sample.
This estimator does not use the commonly employed "Gaussian rule of thumb". As a result, it outperforms many plug-in methods on multimodal samples with widely separated modes.
kde Gaussian kernel density estimator for one-dimensional data, using the method of Botev et al.
The result is a pair of vectors, containing:
The coordinates of each mesh point. The mesh interval is chosen to be 20% larger than the range of the sample. (To specify the mesh interval, use kde_.)
Density estimates at each mesh point.
kde_ :: (Vector v CD, Vector v Double, Vector v Int)=> IntThe number of mesh points to use in the uniform discretization
of the interval (min,max). If this value is not a power of
two, then it is rounded up to the next power of two.
-> DoubleLower bound (min) of the mesh range.
-> DoubleUpper bound (max) of the mesh range.
-> v Double-> (v Double, v Double)Gaussian kernel density estimator for one-dimensional data, using the method of Botev et al.
The result is a pair of vectors, containing:
The coordinates of each mesh point.
Density estimates at each mesh point.
Botev. Z.I., Grotowski J.F., Kroese D.P. (2010). Kernel density estimation via diffusion. Annals of Statistics 38(5):2916–2957. http://arxiv.org/pdf/1011.2602