Real-Time Inference with Large-Scale Temporal Bayes Nets
  An increasing number of applications require real-time reasoning under
uncertainty with streaming input. The temporal (dynamic) Bayes net formalism
provides a powerful representational framework for such applications. However,
existing exact inference algorithms for dynamic Bayes nets do not scale to the
size of models required for real world applications which often contain
hundreds or even thousands of variables for each time slice. In addition,
existing algorithms were not developed with real-time processing in mind. We
have developed a new computational approach to support real-time exact
inference in large temporal Bayes nets. Our approach tackles scalability by
recognizing that the complexity of the inference depends on the number of
interface nodes between time slices and by exploiting the distinction between
static and dynamic nodes in order to reduce the number of interface nodes and
to factorize their joint probability distribution. We approach the real-time
issue by organizing temporal Bayes nets into static representations, and then
using the symbolic probabilistic inference algorithm to derive analytic
expressions for the static representations. The parts of these expressions that
do not change at each time step are pre-computed. The remaining parts are
compiled into efficient procedural code so that the memory and CPU resources
required by the inference are small and fixed.
