The Factored Frontier Algorithm for Approximate Inference in DBNs
  The Factored Frontier (FF) algorithm is a simple approximate
inferencealgorithm for Dynamic Bayesian Networks (DBNs). It is very similar
tothe fully factorized version of the Boyen-Koller (BK) algorithm, butinstead
of doing an exact update at every step followed bymarginalisation (projection),
it always works with factoreddistributions. Hence it can be applied to models
for which the exactupdate step is intractable. We show that FF is equivalent to
(oneiteration of) loopy belief propagation (LBP) on the original DBN, andthat
BK is equivalent (to one iteration of) LBP on a DBN where wecluster some of the
nodes. We then show empirically that byiterating, LBP can improve on the
accuracy 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 coupled
HMM, used tomodel freeway traffic.
