  A novel method for estimating Bayesian network (BN) parameters from data is
presented which provides improved performance on test data. Previous research
has shown the value of representing conditional probability distributions
(CPDs) via neural networks(Neal 1992), noisy-OR gates (Neal 1992, Diez 1993)and
decision trees (Friedman and Goldszmidt 1996).The Bernoulli mixture network
(BMN) explicitly represents the CPDs of discrete BN nodes as mixtures of local
distributions,each having a different set of parents.This increases the space
of possible structures which can be considered,enabling the CPDs to have
finer-grained dependencies.The resulting estimation procedure induces a
modelthat is better able to emulate the underlying interactions occurring in
the data than conventional conditional Bernoulli network models.The results for
artificially generated data indicate that overfitting is best reduced by
restricting the complexity of candidate mixture substructures local to each
node. Furthermore, mixtures of very simple substructures can perform almost as
well as more complex ones.The BMN is also applied to data collected from an
online adventure game with an application to keyhole plan recognition. The
results show that the BMN-based model brings a dramatic improvement in
performance over a conventional BN model.
