Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Edge-valued binary decision diagrams for multi-level hierarchical verification
DAC '92 Proceedings of the 29th ACM/IEEE Design Automation Conference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
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
On the Role of Context-Specific Independence in Probabilistic Inference
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A Differential Approach to Inference in Bayesian Networks
UAI '00 Proceedings of the 16th 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
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Case-factor diagrams for structured probabilistic modeling
Journal of Computer and System Sciences
Conditional independence and chain event graphs
Artificial Intelligence
Probabilistic decision diagrams for exact probabilistic analysis
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Reachability analysis of uncertain systems using bounded-parameter Markov decision processes
Artificial Intelligence
Supervised classification using probabilistic decision graphs
Computational Statistics & Data Analysis
Mixed deterministic and probabilistic networks
Annals of Mathematics and Artificial Intelligence
The PDG-Mixture Model for Clustering
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Representing conditional independence using decision trees
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AND/OR multi-valued decision diagrams (AOMDDs) for graphical models
Journal of Artificial Intelligence Research
Learning probabilistic decision graphs
International Journal of Approximate Reasoning
Modelling and inference with Conditional Gaussian Probabilistic Decision Graphs
International Journal of Approximate Reasoning
Learning recursive probability trees from probabilistic potentials
International Journal of Approximate Reasoning
Refining a Bayesian Network using a Chain Event Graph
International Journal of Approximate Reasoning
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We adopt probabilistic decision graphs developed in the field of automated verification as a tool for probabilistic model representation and inference. We show that probabilistic inference has linear time complexity in the size of the probabilistic decision graph, that the smallest probabilistic decision graph for a given distribution is at most as large as the smallest junction tree for the same distribution, and that in some cases it can in fact be much smaller. Behind these very promising features of probabilistic decision graphs lies the fact that they integrate into a single coherent framework a number of representational and algorithmic optimizations developed for Bayesian networks (use of hidden variables, context-specific independence, structured representation of conditional probability tables).