Join Tree Propagation Utilizing Both Arc Reversal and Variable Elimination

In this paper, we put forth the first join tree propagation algorithm that selectively applies either arc reversal (AR) or variable elimination (VE) to build the propagated messages. Our approach utilizes a recent method for identifying the propagated join tree messages `a priori. When it is determined that precisely one message is to be constructed at a join tree node, VE is utilized to build this distribution; otherwise, AR is applied as it is better suited to construct multiple distributions passed between neighboring join tree nodes. Experimental results, involving evidence processing in seven real-world and one benchmark Bayesian network, empirically demonstrate that selectively applying VE and AR is faster than applying one of these methods exclusively on the entire network.

• Type: Journal
• Year: 2009
• Where: FLAIRS
• Authors: Cory J. Butz, Ken Konkel, Pawan Lingras
• Keywords: Artificial Intelligence, FLAIRS 2009, Join Tree, Join Tree Propagation, Propagated Join Tree