Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Top-Down Construction and Repetetive Structures Representation in Bayesian Networks
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Bayesian Logic Programs
Probabilistic reasoning for complex systems
Probabilistic reasoning for complex systems
A General Framework for Mining Frequent Subgraphs from Labeled Graphs
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
SPOOK: a system for probabilistic object-oriented knowledge representation
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 engineering for complex belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Structured probabilistic inference
International Journal of Approximate Reasoning
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In many domains where experts are the main source of knowledge, e.g., in reliability and risk management, a framework well suited for modeling, maintenance and exploitation of complex probabilistic systems is essential. In these domains, models usually define closed-world systems and result from the aggregation of multiple patterns repeated many times. Object Oriented-based Frameworks (OOF) such as Probabilistic Relational Models thus offer an effective way to represent such systems. OOFs define patterns as classes and substitute large Bayesian networks (BN) by graphs of instances of these classes. In this framework, Structured Inference avoids many computation redundancies by exploiting class knowledge, hence reducing BN inference times by orders of magnitude. However, to keep modeling and maintenance costs low, OOF classes often encode only generic situations. More complex situations, even those repeated many times, are only represented by combinations of instances. In this paper, we propose to determine such combination patterns and exploit them as classes to speed-up Structured Inference. We prove that determining an optimal set of patterns is NP-hard. We also provide an efficient algorithm to approximate this set and show numerical experiments that highlight its practical efficiency.