KSL-89-35
## Algorithms for Bayesian Belief-Network Precomputation

**Reference: **
Herskovits, E. &
Cooper, G. F. Algorithms for Bayesian Belief-Network Precomputation. 1991.

**Abstract:**
Bayesian belief networks show promise as a representational framework for
constructing expert systems; they provide platforms for knowledge acquisition
and for normative probabilistic inference. Despite the intuitive appeal of
this inference paradigm, the run-time complexity of general belief-network
computation may be too great for solving many complex problems in a practical
amount of time. Therefore, researchers have focused their attention on
developing approximate or special-case algorithms for belief-network
inference. For belief networks with a highly skewed distribution of joint
probabilities, storing a small number of cases to capture a large proportion
of the likely uses of the network may lead to a significant increase in the
speed of inference. We report here preliminary results of a set of algorithms
that cache (precompute and store) a small subset of a belief network to
decrease the expected running time for probability computation.

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