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Model-guided adaptive sampling for Bayesian model selection
Journal of the Korean Statistical Society ( IF 0.6 ) Pub Date : 2020-01-24 , DOI: 10.1007/s42952-020-00050-z
Qingzhao Yu , Bin Li

We propose an adaptive design for variable selection in Bayesian modeling process. First randomly select some models to evaluate (e.g. by posterior model probability). Using these models, we predict the performance of all models in the candidate pool, based on which more models are selected and evaluated, in which models with good predicted performance or large prediction variances have high probabilities of being selected. Newly sampled models are used to update the performance predictions of candidate models. Repeat the process until informative models are not likely to be left unsampled in terms of the preset model selection criterion. When there are high-dimensional variables, we propose the use of highest-resolution-minimum-aberration-fractional-factorial design to select candidate-model sets to enable inferences on main effects and low-level interactions of variables. Simulations and a real data example have shown that the proposed adaptive design is efficient in finding informative models compared with other variable selection procedures.



中文翻译:

贝叶斯模型选择的模型指导自适应采样

我们为贝叶斯建模过程中的变量选择提出了一种自适应设计。首先随机选择一些模型进行评估(例如通过后验模型概率)。使用这些模型,我们可以选择和评估更多模型,从而预测候选池中所有模型的性能,其中具有良好预测性能或较大预测方差的模型具有较高的被选择概率。新采样的模型用于更新候选模型的性能预测。重复该过程,直到根据预设的模型选择标准不太可能将信息模型保留下来。当存在高维变量时 我们建议使用最高分辨率-最小像差-分数阶乘设计来选择候选模型集,以推断主要影响和变量的低级交互作用。仿真和实际数据示例表明,与其他变量选择程序相比,所提出的自适应设计在查找信息模型方面有效。

更新日期:2020-01-24
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