9.1.概率密度估计(Density Estimation) 一些基本概念 Density estimation:estimating the probability density function p(x)based on a given set of training samples D={x1,x2,,Xw}. Estimated density:denoted by p(x). Training samples are i.i.d.and distributed according to p(x): Parametric estimation:parameter vector 0 of p(x; Non-parametric estimation:a function p:F->R O Finite number of training samples meaning that there will be some errors in the function (density)estimation 2/279.1. 概率密度估计 (Density Estimation) 一些基本概念 1 Density estimation: estimating the probability density function p(x) based on a given set of training samples D = {x1, x2, ..., xN}. 2 Estimated density: denoted by pˆ(x). 3 Training samples are i.i.d. and distributed according to p(x). 4 Parametric estimation: parameter vector θ of p(x; θ) 5 Non-parametric estimation: a function p : F −→ R 6 Finite number of training samples meaning that there will be some errors in the function (density) estimation. 2 / 27