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 variable conditional on the global variables by a Gaussian density via a second order Taylor expansion. Variational Bayes inference for the reparametrized model is then obtained using stochastic approximation. Our approach can be readily extended to large datasets via a divide and recombine strategy. Using generalized linear mixed models, we demonstrate that reparametrized variational Bayes (RVB) provides improvements in both accuracy and convergence rate compared to state of the art Gaussian variational approximation methods.

中文翻译：

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使用模型重新参数化以改善变异贝叶斯†

我们建议使用模型重新参数化来改善分层模型的变分贝叶斯推理，这些模型的变量可以分为全局（在各个观察值之间共享）或局部（特定于观察值）。通过对局部变量应用可逆仿射变换，可以使局部变量和全局变量之间的后验依赖性最小。这种变换的功能形式是通过二阶泰勒展开式，以高斯密度来近似以全局变量为条件的每个局部变量的后验分布来推导的。然后使用随机逼近获得重新参数化模型的变分贝叶斯推断。通过划分和重组策略，我们的方法可以轻松扩展到大型数据集。使用广义线性混合模型，