Neural Enhanced Belief Propagation on Factor Graphs

Understand

A graphical model is a structured representation of locally dependent random variables.

• A traditional method to reason over these random variables is to perform inference using belief propagation.
• When provided with the true data generating process, belief propagation can infer the optimal posterior probability estimates in tree structured factor graphs.
• However, in many cases we may only have access to a poor approximation of the data generating process, or we may face loops in the factor graph, leading to suboptimal estimates.