Expectation Propogation for approximate inference in dynamic Bayesian
  networks
  We describe expectation propagation for approximate inference in dynamic
Bayesian networks as a natural extension of Pearl s exact belief
propagation.Expectation propagation IS a greedy algorithm, converges IN many
practical cases, but NOT always.We derive a DOUBLE - loop algorithm, guaranteed
TO converge TO a local minimum OF a Bethe free energy.Furthermore, we show that
stable fixed points OF (damped) expectation propagation correspond TO local
minima OF this free energy, but that the converse need NOT be the CASE .We
illustrate the algorithms BY applying them TO switching linear dynamical
systems AND discuss implications FOR approximate inference IN general Bayesian
networks.
