Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks
نویسندگان
چکیده
We describe expectation propagation for ap proximate inference in dynamic Bayesian net works as a natural extension of Pearl's ex act belief propagation. Expectation propa gation is a greedy algorithm, converges in many practical cases, but not always. We de rive 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 prop agation 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 approxi mate inference in general Bayesian networks.
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