高斯混合模型的自然梯度变量推理

贝叶斯方法通过使用后验分布来估计不确定性的量度。这些方法中的一个困难来源是归一化常数的计算。计算准确的后验一般是难以实现的，我们通常对其进行近似计算。变量推理（VI）方法用通常从一个简单的系列中选择的分布来近似后验，并使用优化。这项工作的主要贡献是描述了一套用于高斯混合物的自然梯度变异推理的更新规则，它可以对每个混合物成分独立运行，并可能是并行的。

原文题目：Natural Gradient Variational Inference with Gaussian Mixture Models

原文：Bayesian methods estimate a measure of uncertainty by using the posterior distribution. One source of difficulty in these methods is the computation of the normalizing constant. Calculating exact posterior is generally intractable and we usually approximate it. Variational Inference (VI) methods approximate the posterior with a distribution usually chosen from a simple family using optimization. The main contribution of this work is described is a set of update rules for natural gradient variational inference with mixture of Gaussians, which can be run independently for each of the mixture components, potentially in parallel.