A visual introduction to Gaussian Belief Propagation

• URL: http://arxiv.org/abs/2107.02308v1
• Date: Mon, 5 Jul 2021 22:43:27 GMT
• Title: A visual introduction to Gaussian Belief Propagation
• Authors: Joseph Ortiz, Talfan Evans, Andrew J. Davison
• Abstract summary: We present a visual introduction to the 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.
• Score: 22.02770204949673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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