Efficient inference in persistent Dynamic Bayesian Networks
  Numerous temporal inference tasks such as fault monitoring and anomaly
detection exhibit a persistence property: for example, if something breaks, it
stays broken until an intervention. When modeled as a Dynamic Bayesian Network,
persistence adds dependencies between adjacent time slices, often making exact
inference over time intractable using standard inference algorithms. However,
we show that persistence implies a regular structure that can be exploited for
efficient inference. We present three successively more general classes of
models: persistent causal chains (PCCs), persistent causal trees (PCTs) and
persistent polytrees (PPTs), and the corresponding exact inference algorithms
that exploit persistence. We show that analytic asymptotic bounds for our
algorithms compare favorably to junction tree inference; and we demonstrate
empirically that we can perform exact smoothing on the order of 100 times
faster than the approximate Boyen-Koller method on randomly generated instances
of persistent tree models. We also show how to handle non-persistent variables
and how persistence can be exploited effectively for approximate filtering.
