Minimal Assumption Distribution Propagation In Belief Networks
Ron Musick
Computer Science Division
University of California
Berkeley, California 94720
musick@cs.berkeley.edu
Abstract
As belief networks are used to model increasingly complex situations,
the need to automatically construct them from large databases
will become paramount. This paper concentrates on solving a part of
the belief network induction problem: that of learning the
quantitative structure (the conditional probabilities), given the
qualitative structure. In particular, a theory is presented that
shows how to propagate inference distributions in a belief network,
with the only assumption being that the given qualitative structure is
correct. Most inference algorithms must make at least this
assumption. The theory is based on four network transformations that
are sufficient for any inference in a belief network. Furthermore,
the claim is made that contrary to popular belief, error will not
necessarily grow as the inference chain grows. Instead, for QBN
belief nets induced from large enough samples, the error is more
likely to decrease as the size of the inference chain increases.
Appeared in
UAI 1993