Connectionist learning of belief networks
Artificial Intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Being Bayesian about Network Structure
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Mixture approximations to Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning Bayesian nets that perform well
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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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 model that 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 conditional Bernoulli BN model.