AAAI Publications, Twenty-Second International FLAIRS Conference

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Join Tree Propagation Utilizing Both Arc Reversal and Variable Elimination
Cory James Butz, Ken Konkel, Pawan Lingras

Last modified: 2009-03-18


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.


Bayesian; probabilistic; inference

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