Belief Propagation by Message Passing in Junction Trees: Computing Each Message Faster Using GPU Parallelization

Understand

Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs.

• However, belief propagation over junction tree is known to be computationally intensive in the general case.
• Its complexity may increase dramatically with the connectivity and state space cardinality of Bayesian network nodes.
• In this paper, we address this computational challenge using GPU parallelization.