Statistical analysis with missing data
Statistical analysis with missing data
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Elements of information theory
Elements of information theory
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Approximating Bayesian Belief Networks by Arc Removal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning - Special issue on learning with probabilistic representations
The Sample Complexity of Learning Fixed-Structure Bayesian Networks
Machine Learning - Special issue on learning with probabilistic representations
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Learning probabilistic relational models
Relational Data Mining
Parameter Learning in Object-Oriented Bayesian Networks
Annals of Mathematics and Artificial Intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Top-Down Construction and Repetetive Structures Representation in Bayesian Networks
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
Structural Learning in Object Oriented Domains
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Learning the Dimensionality of Hidden Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Probabilistic reasoning for complex systems
Probabilistic reasoning for complex systems
Learning probabilistic networks
The Knowledge Engineering Review
Challenge: what is the impact of Bayesian networks on learning?
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning Bayesian networks from incomplete data with stochastic search algorithms
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Inference in multiply sectioned Bayesian networks with extended Shafer-Shenoy and lazy propagation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning Bayesian nets that perform well
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
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
On the sample complexity of learning Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Network engineering for complex belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
MEBN: A language for first-order Bayesian knowledge bases
Artificial Intelligence
Force deployment analysis with generalized grammar
Information Fusion
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
Interacting meaningfully with machine learning systems: Three experiments
International Journal of Human-Computer Studies
Bayesian learning of Bayesian networks with informative priors
Annals of Mathematics and Artificial Intelligence
Tuning expert systems for cost-sensitive decisions
Advances in Artificial Intelligence
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by domain experts. Unfortunately, conventional learning algorithms do not easily incorporate prior information, if this information is too vague to be encoded as properties that are local to families of variables. For instance, conventional algorithms do not exploit prior information about repetitive structures, which are often found in object oriented domains such as computer networks, large pedigrees and genetic analysis. In this paper we propose a method for doing structural learning in object oriented domains. It is demonstrated that this method is more efficient than conventional algorithms in such domains, and it is argued that the method supports a natural approach for expressing and incorporating prior information provided by domain experts.