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nonparametric regression
Nonparametric regression is a form of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data. Nonparametric regression requires larger sample sizes than regression based on parametric models because the data must supply the model structure as well as the model estimates.
==Gaussian process regression or Kriging==
(詳細はmultivariate normal distribution and the regression curve is estimated by its posterior mode. The Gaussian prior may depend on unknown hyperparameters, which are usually estimated via empirical Bayes.
Smoothing splines have an interpretation as the posterior mode of a Gaussian process regression.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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