Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Connectionist learning of belief networks
Artificial Intelligence
An introduction to variational methods for graphical models
Learning in graphical models
Context-specific approximation in probabilistic inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Models and selection criteria for regression and classification
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Structure and parameter learning for causal independence and causal interaction models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international 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
Bayesian learning of loglinear models for neural connectivity
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Possibilistic causality consistency problem based on asymmetrically-valued causal model
Fuzzy Sets and Systems - Possibility theory and fuzzy logic
Incorporating expert knowledge when learning Bayesian network structure: A medical case study
Artificial Intelligence in Medicine
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Learning hybrid bayesian networks by MML
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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A major problem for the learning of Bayesian networks (BNs) is the exponential number of parameters needed for conditional probability tables. Recent research reduces this complexity by modeling local structure in the probability tables. We examine the use of log-linear local models. While log-linear models in this context are not new (Whittaker, 1990; Buntine, 1991; Neal, 1992; Heckerman and Meek, 1997), it is generally subsumed under a naive Bayes model. We describe an alternative using a Minimum Message Length (MML) (Wallace and Freeman, 1987) metric for the selection of local models with causal independence, which we term a first-order model (FOM). We also combine FOMs and full conditional models on a node-by-node basis.