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The Factored Frontier (FF) algorithm is a simple approximateinferencealgorithm for Dynamic Bayesian Networks (DBNs). It is very similartothe fully factorized version of the Boyen-Koller (BK) algorithm, butinsteadof doing an exact update at every step followed bymarginalisation (projection),it always works with factoreddistributions. Hence it can be applied to modelsfor which the exactupdate step is intractable. We show that FF is equivalent to(oneiteration of) loopy belief propagation (LBP) on the original DBN, andthatBK is equivalent (to one iteration of) LBP on a DBN where wecluster some of thenodes. We then show empirically that byiterating, LBP can improve on theaccuracy of both FF and BK. Wecompare these algorithms on two real-world DBNs:the first is a modelof a water treatment plant, and the second is a coupledHMM, used tomodel freeway traffic.

The Factored Frontier Algorithm for Approximate Inference in DBNs