Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data
预测变量缺失下参数与非参数回归的变分贝叶斯推断
Bayesian hierarchical models are attractive structures for conducting regression analyses when the data are subject to missingness. However, the requisite probability calculus is challenging and Monte Carlo methods typically are employed. We develop an alternative approach based on deterministic variational Bayes approximations. Both parametric and nonparametric regression are considered. Attention is restricted to the more challenging case of missing predictor data. We…