Factor graph fragmentization of expectation propagation

TL;DR

This paper introduces factor graph fragmentization to streamline the derivation and implementation of expectation propagation algorithms across various graphical models, reducing repetitive algebra and enhancing software development.

Contribution

It proposes a novel fragment-based approach to expectation propagation, enabling reuse of message computations and simplifying algorithm coding.

Findings

Catalogued common fragments and messages for expectation propagation

Reduced algebraic repetition in inference algorithm derivation

Facilitated modular and efficient software implementation

Abstract

Expectation propagation is a general approach to fast approximate inference for graphical models. The existing literature treats models separately when it comes to deriving and coding expectation propagation inference algorithms. This comes at the cost of similar, long-winded algebraic steps being repeated and slowing down algorithmic development. We demonstrate how factor graph fragmentization can overcome this impediment. This involves adoption of the message passing on a factor graph approach to expectation propagation and identification of factor graph sub-graphs, which we call fragments, that are common to wide classes of models. Key fragments and their corresponding messages are catalogued which means that their algebra does not need to be repeated. This allows compartmentalization of coding and efficient software development.