摘要随着基因组测序技术和生物信息学的迅猛发展,近几年涌现了大量与疾病相关的组学数据即所谓高维数据.对于这类组学数据,共同特点是自变量个数p通常远大于观察例数n,且自变量间往往高度相关,从成千上万个组学数据中识别出真正有意义的自变量带来一些统计学挑战.本文对高维数据中的贝叶斯变量选择方法做论述.

abstractsWith the rapid development of genome sequencing technology and bioinformatics in recent years,it has become possible to measure thousands of omics data which might be associated with the progress of diseases,i.e."high-dimensional data".This type of omics data have a common feature that the number of variable p is usually greater than the observation cases n,and often has high correlation between independent variables.Therefore,it is a great statistical challenge to identify really meaningful variables from omics data.This paper summarizes the methods of Bayesian variable selection in the analysis of high-dimensional data.