Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
An Extended Relational Data Model For Probabilistic Reasoning
Journal of Intelligent Information Systems
Automated database schema design using mined data dependencies
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Hierarchical schemata for relational databases
ACM Transactions on Database Systems (TODS)
Multivalued dependencies and a new normal form for relational databases
ACM Transactions on Database Systems (TODS)
On the menbership problem for functional and multivalued dependencies in relational databases
ACM Transactions on Database Systems (TODS)
An Algorithm for Inferring Multivalued Dependencies with an Application to Propositional Logic
Journal of the ACM (JACM)
An Almost Linear-Time Algorithm for Computing a Dependency Basis in a Relational Database
Journal of the ACM (JACM)
On the Equivalence of Database Models
Journal of the ACM (JACM)
On the Desirability of Acyclic Database Schemes
Journal of the ACM (JACM)
A complete axiomatization for functional and multivalued dependencies in database relations
SIGMOD '77 Proceedings of the 1977 ACM SIGMOD international conference on Management of data
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
The Relational Structure of Belief Networks
Journal of Intelligent Information Systems
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
Nonserial Dynamic Programming
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Probabilistic Reasoning in a Distributed Multi-Agent Environment
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Recovery Protocols in Multi-Agent Probabilistic Reasoning Systems
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
Theory of Relational Databases
Theory of Relational Databases
Contextual weak independence in Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A method for implementing a probabilistic model as a relational database
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning belief networks in domains with recursively embedded pseudo independent submodels
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Testing implication of probabilistic dependencies
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On Axiomatizing Probabilistic Conditional Independencies in Bayesian Networks
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Triangulation of Bayesian Networks: A Relational Database Perspective
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Properties of Weak Conditional Independence
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A Structural Characterization of DAG-Isomorphic Dependency Models
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
On the Role of Contextual Weak Independence in Probabilistic Inference
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
A Web-Based Intelligent Tutoring System for Computer Programming
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
An efficient approach to mining indirect associations
Journal of Intelligent Information Systems
A web-based bayesian intelligent tutoring system for computer programming
Web Intelligence and Agent Systems
Maximal prime subgraph decomposition of Bayesian networks: A relational database perspective
International Journal of Approximate Reasoning
Critical remarks on belief updating in Bayesian networks
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Efficient mining of indirect associations using HI-mine
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
A new inference axiom for probabilistic conditional independence
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
A non-local coarsening result in granular probabilistic networks
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Constructing the Bayesian network structure from dependencies implied in multiple relational schemas
Expert Systems with Applications: An International Journal
Qualitative combination of independence models
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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A probabilistic network consists of a dependency structure and corresponding probability tables. The dependency structure is a graphical representation of the conditional independencies that are known to hold in the problem domain. In this paper, we propose an automated process for constructing the combined dependency structure of a multiagent probabilistic network. Each domain expert supplies any known conditional independency information and not necessarily an explicit dependency structure. Our method determines a succinct representation of all the supplied independency information called a minimal cover. This process involves detecting all inconsistent information and removing all redundant information. A unique dependency structure of the multiagent probabilistic network can be constructed directly from this minimal cover. The main result of this paper is that the constructed dependency structure is a perfect-map of the minimal cover. That is, every probabilistic conditional independency logically implied by the minimal cover can be inferred from the dependency structure and every probabilistic conditional independency inferred from the dependency structure is logically implied by the minimal cover.