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
Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Elements of information theory
Elements of information theory
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Knowledge representation and inference in similarity networks and Bayesian multinets
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Approximating Bayesian Belief Networks by Arc Removal
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The Sample Complexity of Learning Fixed-Structure Bayesian Networks
Machine Learning - Special issue on learning with probabilistic representations
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Machine Learning - Special issue on learning with probabilistic representations
A tutorial on learning with Bayesian networks
Learning in graphical models
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
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
Probabilistic reasoning for complex systems
Probabilistic reasoning for complex systems
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Accelerating EM: an empirical study
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
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
On the sample complexity of learning Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Basic principles of learning Bayesian logic programs
Probabilistic inductive logic programming
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This paper describes a method for parameter learning in Object-Oriented Bayesian Networks (OOBNs). We propose a methodology for learning parameters in OOBNs, and prove that maintaining the object orientation imposed by the prior model will increase the learning speed in object-oriented domains. We also propose a method to efficiently estimate the probability parameters in domains that are inot strictly object oriented. Finally, we attack type uncertainty, a special case of model uncertainty typical to object-oriented domains.