Abstract
Bayesian belief networks provide an intuitive and concise means of representing probabilistic
relationships among the variables in expert systems. A major drawback to this methodology
is its computational complexity. We present an introduction to belief networks, and
describe methods for precomputing, or caching, part of a belief network based on metrics
of probability and expected utility. These algorithms are examples of a general method
for decreasing expected running time for probabilistic inference.
We first present the necessary background, and then present algorithms for producing
caches based on metrics of expected probability and expected utility. We show how
these algorithms can be applied to a moderately complex belief network, and present
directions for future research.
Key-Words
Expert Systems - Algorithms - Computer-Assisted Diagnosis - Probability Theory
