Efficient Search-Based Inference for Noisy-OR Belief Networks:
  TopEpsilon
  Inference algorithms for arbitrary belief networks are impractical for large,
complex belief networks. Inference algorithms for specialized classes of belief
networks have been shown to be more efficient. In this paper, we present a
search-based algorithm for approximate inference on arbitrary, noisy-OR belief
networks, generalizing earlier work on search-based inference for two-level,
noisy-OR belief networks. Initial experimental results appear promising.
