Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Elements of information theory
Elements of information theory
Learning in graphical models
Learning Bayesian networks with local structure
Learning in graphical models
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Multi-Terminal Binary Decision Diagrams: An Efficient DataStructure for Matrix Representation
Formal Methods in System Design
A Differential Approach to Inference in Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Tree Induction for Probability-Based Ranking
Machine Learning
Probabilistic decision graphs-combining verification and AI techniques for probabilistic inference
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Case-factor diagrams for structured probabilistic modeling
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Algorithms for learning decomposable models and chordal graphs
UAI'97 Proceedings of the Thirteenth 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
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Supervised classification using probabilistic decision graphs
Computational Statistics & Data Analysis
The PDG-Mixture Model for Clustering
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Structural-EM for learning PDG models from incomplete data
International Journal of Approximate Reasoning
Modelling and inference with Conditional Gaussian Probabilistic Decision Graphs
International Journal of Approximate Reasoning
Refining a Bayesian Network using a Chain Event Graph
International Journal of Approximate Reasoning
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Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence relations that cannot be captured in a Bayesian network structure, and can sometimes provide computationally more efficient representations than Bayesian networks. In this paper we present an algorithm for learning PDGs from data. First experiments show that the algorithm is capable of learning optimal PDG representations in some cases, and that the computational efficiency of PDG models learned from real-life data is very close to the computational efficiency of Bayesian network models.