论文标题
逆g-wishart分布和变分信息传递
The Inverse G-Wishart Distribution and Variational Message Passing
论文作者
论文摘要
在因子图上传递的消息是用于任意图形大型模型的近似推理算法编码的强大范式。因子图片段的概念允许代数和计算机代码的分隔。我们表明，逆G-Wishart分布家族使基本变化消息传递因子图片段可以优雅，简洁地表达。这种片段出现在模型中，该模型制作了有关协方差矩阵或方差参数的近似推理，并且在当代统计和机器学习中无处不在。
Message passing on a factor graph is a powerful paradigm for the coding of approximate inference algorithms for arbitrarily graphical large models. The notion of a factor graph fragment allows for compartmentalization of algebra and computer code. We show that the Inverse G-Wishart family of distributions enables fundamental variational message passing factor graph fragments to be expressed elegantly and succinctly. Such fragments arise in models for which approximate inference concerning covariance matrix or variance parameters is made, and are ubiquitous in contemporary statistics and machine learning.
