Evolutionary Stochastic Search for Bayesian Model Exploration
Evolutionary Stochastic Search for Bayesian Model Exploration
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DOI：
10.1214/10-ba523
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发表时间：
2010-01-01
影响因子：
4.4
通讯作者：
Richardson, Sylvia
中科院分区：
文献类型：
--
作者：
Bottolo, Leonard;Richardson, Sylvia
实现贝叶斯变量选择的线性高斯回归模型分析高维数据集是当前许多领域的兴趣。为了使这种分析可行，我们提出了一种新的基于进化蒙特卡罗的采样算法，并设计为在“大p，小n”范式下工作，从而使完全贝叶斯多变量分析可行，例如在遗传学/基因组学实验中。给出了基因组学中的两个真实数据示例，展示了该算法在多达10,000个协变量的空间中的性能。最后，在广泛的仿真研究中，将该方法与最近提出的搜索算法进行了比较。
Implementing Bayesian variable selection for linear Gaussian regression models for analysing high dimensional data sets is of current interest in many fields. In order to make such analysis operational, we propose a new sampling algorithm based upon Evolutionary Monte Carlo and designed to work under the "large p, small n" paradigm, thus making fully Bayesian multivariate analysis feasible, for example, in genetics/genomics experiments. Two real data examples in genomics are presented, demonstrating the performance of the algorithm in a space of up to 10, 000 covariates. Finally the methodology is compared with a recently proposed search algorithms in an extensive simulation study.