Communications of the ACM
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Parameter Learning in Object-Oriented Bayesian Networks
Annals of Mathematics and Artificial Intelligence
Eighteenth national conference on Artificial intelligence
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
Learning Factor Graphs in Polynomial Time and Sample Complexity
The Journal of Machine Learning Research
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
The Journal of Machine Learning Research
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Artificial Intelligence in Medicine
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We consider the problem of PAC learning probabilistic networks in the case where the structure of the net is specified beforehand. We allow the conditional probabilities to be represented in anymanner (as tables or specialized functions) and obtain sample complexity bounds for learning nets with and without hidden nodes.