论文标题
贝叶斯变量选择的差异推理方法
A Variational Inference method for Bayesian variable selection
论文作者
论文摘要
可变选择是统计中的经典问题。在本文中，我们考虑了基于Spike and-slab先验的贝叶斯变量选择问题，而Ročková和George（2014）提出的混合正态分布。由Ormerod and You（2017，2023）的动机，我们使用变异推理并崩溃的变异推理方法来解决贝叶斯问题，而不是MCMC。像Ormerod和您（2017，2023）一样，我们也解释了如何诱导稀疏性估计器，在某些温和的假设下，我们还证明了一致且渐近的结果。
Variable selection is a classic problem in statistics. In this paper, we consider a Bayes variable selection problem based on spike-and-slab prior with mixed normal distribution proposed by Ročková and George (2014). Motivated by Ormerod and You (2017, 2023), we use the variational inference and collapsed variational inference method to solve the Bayesian problem instead of MCMC. Like Ormerod and You (2017, 2023), we also explain how the sparsity estimator is induced, and under certain mild assumptions, we also prove the consistent and asymptotic results.
