论文检索

高维高斯图模型的 spike-and-slab 贝叶斯计算AI 解读

Gaussian graphical models are widely used to infer dependence structures. Bayesian methods are appealing to quantify uncertainty associated with structural learning, i.e., the plausibility of conditional independence statements given the data, and parameter estimates. However, computational demands have limited their application when the number of variables is large, which prompted the use of pseudo-Bayesian approaches. We propose fully Bayesian algorithms that provably…

利用数据整合与迁移学习改进变量选择性质AI 解读

We study variable selection (also called support recovery) in high-dimensional sparse linear regression when one has external information on which variables are likely to be associated with the response. Consistent recovery is only possible under somewhat restrictive conditions on sample size, dimension, signal strength, and sparsity. We investigate how these conditions can be relaxed by incorporating said external information. A key application that we consider is…

数据整合中的经验贝叶斯方法AI 解读

We discuss the use of empirical Bayes for data integration, in the sense of transfer learning. Our main interest is in settings where one wishes to learn structure (e.g. feature selection) and one only has access to incomplete data from previous studies, such as summaries, estimates or lists of relevant features. We discuss differences between full Bayes and empirical Bayes, and develop a computational framework for the latter. We discuss how empirical Bayes attains…

利用混杂变量重要性学习推断多个处理效应AI 解读

We address modelling and computational issues for multiple treatment effect inference under many potential confounders. Our main contribution is providing a trade-off between preventing the omission of relevant confounders, while not running into an over-selection of instruments that significantly inflates variance. We propose a novel empirical Bayes framework for Bayesian model averaging that learns from data the prior inclusion probabilities of key covariates. Our…