中国科学院大学学报2025，Vol.42Issue(6)：721-728,8.DOI:10.7523/j.ucas.2024.018
基于序贯算法的重构密度估计
Reconstruction density estimation based on sequential algorithms
摘要
Abstract
In this paper,for the density estimation given by the reconstruction approach,an algorithm based on the sequential idea is proposed to solve the node selection problem in the reconstructed density estimation.Since density estimation can be regarded as an unsupervised learning problem,i.e.,there is no response variable y,the node sequential selection approach for regression is not applicable here.We regard the node as a parameter and select the next node by minimising the loss function,then determine the entire set of nodes using a greedy algorithm.This algorithm is simple to operate,further improves the estimation effect,and can reduce the impact on density estimation due to different node selection.In addition,in this paper,the prior is given according to the actual meanings of the parameters in the reconstruction approach,the samples of the posterior distribution are obtained using the Metropolis algorithm,so that the interval estimation of the density function point by point is constructed by approximating the overall quartile through the sample quartiles.Finally,we validate the sequential reconstruction density estimation and its interval estimation on several datasets.关键词
重构方法/密度估计/序贯算法/区间估计/贪心算法Key words
reconstruction approach/density estimation/sequential algorithm/interval estimation/greedy algorithm分类
数理科学引用本文复制引用
黄思源,谢田法,熊世峰..基于序贯算法的重构密度估计[J].中国科学院大学学报,2025,42(6):721-728,8.基金项目
国家重点研发计划(2022YFF0609903)和国家自然科学基金(12171462)资助 （2022YFF0609903）
