Exploiting Qualitative Knowledge in the Learning of Conditional
  Probabilities of Bayesian Networks
  Algorithms for learning the conditional probabilities of Bayesian networks
with hidden variables typically operate within a high-dimensional search space
and yield only locally optimal solutions. One way of limiting the search space
and avoiding local optima is to impose qualitative constraints that are based
on background knowledge concerning the domain. We present a method for
integrating formal statements of qualitative constraints into two learning
algorithms, APN and EM. In our experiments with synthetic data, this method
yielded networks that satisfied the constraints almost perfectly. The accuracy
of the learned networks was consistently superior to that of corresponding
networks learned without constraints. The exploitation of qualitative
constraints therefore appears to be a promising way to increase both the
interpretability and the accuracy of learned Bayesian networks with known
structure.
