In this article, we present a visual introduction to Gaussian Belief Propagation (GBP), an approximate probabilistic inference algorithm that operates by passing messages between the nodes of arbitrarily structured factor graphs. A special case of loopy belief propagation, GBP updates rely only on local information and will converge independently of the message schedule. Our key argument is that, given recent trends in computing hardware, GBP has the right computational properties to act as a scalable distributed probabilistic inference framework for future machine learning systems.

翻译：在本篇文章中,我们介绍了高西亚信仰传播(GBP)的直观介绍,这是一种近似概率推论算法,在任意结构化要素图的节点之间传递信息。 环状信仰传播的特殊案例是,英镑更新仅依靠当地信息,并且将独立于电文时间表。 我们的主要论点是,鉴于计算机硬件的近期趋势,英镑具有正确的计算属性,可以作为未来机器学习系统的可缩放分布概率推论框架。