Use of Model Reparametrization to Improve Variational Bayes
通过模型重参数化改进变分贝叶斯推断
Abstract We propose using model reparametrization to improve variational Bayes inference for hierarchical models whose variables can be classified as global (shared across observations) or local (observation-specific). Posterior dependence between local and global variables is minimized by applying an invertible affine transformation on the local variables. The functional form of this transformation is deduced by approximating the posterior distribution of each local…