A visual introduction to Gaussian Belief Propagation
This is an incremental contribution, providing a visual tutorial on an existing algorithm to potentially aid in its adoption for distributed inference in machine learning.
The paper introduces Gaussian Belief Propagation (GBP) as an approximate probabilistic inference algorithm that uses message-passing on factor graphs, highlighting its local updates and convergence properties. It argues that GBP's computational traits make it suitable as a scalable distributed framework for future machine learning systems.
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.