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.
HUANG Siyuan
,
XIE Tianfa
,
XIONG Shifeng
. Reconstruction density estimation based on sequential algorithms[J]. Journal of University of Chinese Academy of Sciences, 2025
, 42(6)
: 721
-728
.
DOI: 10.7523/j.ucas.2024.018
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