Algebraic decision diagrams and their applications
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
A Differential Approach to Inference in Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
A differential semantics for jointree algorithms
Artificial Intelligence
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Compiling Bayesian networks with local structure
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Compiling relational Bayesian networks for exact inference
International Journal of Approximate Reasoning
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
Compressing probabilistic Prolog programs
Machine Learning
On probabilistic inference by weighted model counting
Artificial Intelligence
Logical Compilation of Bayesian Networks with Discrete Variables
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Diagnosing faults in electrical power systems of spacecraft and aircraft
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
AND/OR multi-valued decision diagrams (AOMDDs) for graphical models
Journal of Artificial Intelligence Research
Optimizing inference in Bayesian networks and semiring valuation algebras
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
CLP(BN): constraint logic programming for probabilistic knowledge
Probabilistic inductive logic programming
Understanding the scalability of Bayesian network inference using clique tree growth curves
Artificial Intelligence
Probabilistic model-based diagnosis: an electrical power system case study
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
Towards software health management with bayesian networks
Proceedings of the FSE/SDP workshop on Future of software engineering research
Journal of Automated Reasoning
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Local structure and determinism in probabilistic databases
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
A model-learner pattern for bayesian reasoning
POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Bayesian inference using data flow analysis
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Software health management with Bayesian networks
Innovations in Systems and Software Engineering
A generic framework for a compilation-based inference in probabilistic and possibilistic networks
Information Sciences: an International Journal
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Compiling Bayesian networks has proven an effective approach for inference that can utilize both global and local network structure. In this paper, we define a new method of compiling based on variable elimination (VE) and Algebraic Decision Diagrams (ADDs). The approach is important for the following reasons. First, it exploits local structure much more effectively than previous techniques based on VE. Second, the approach allows any of the many VE variants to compute answers to multiple queries simultaneously. Third, the approach makes a large body of research into more structured representations of factors relevant in many more circumstances than it has been previously. Finally, experimental results demonstrate that VE can exploit local structure as effectively as state-of-the-art algorithms based on conditioning on the networks considered, and can sometimes lead to much faster compilation times.