Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Decision analysis and expert systems
AI Magazine
Semigraphoids and structures of probabilistic conditional independence
Annals of Mathematics and Artificial Intelligence
Constructing the Dependency Structure of a Multiagent Probabilistic Network
IEEE Transactions on Knowledge and Data Engineering
Assessing Dependence: Some Experimental Results
Management Science
An application of formal argumentation: Fusing Bayesian networks in multi-agent systems
Artificial Intelligence
Conditional independence structure and its closure: Inferential rules and algorithms
International Journal of Approximate Reasoning
Acyclic directed graphs representing independence models
International Journal of Approximate Reasoning
Some complexity considerations in the combination of belief networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Finding consensus Bayesian network structures
Journal of Artificial Intelligence Research
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We deal with the problem of combining sets of independence statements coming from different experts. It is known that the independence model induced by a strictly positive probability distribution has a graphoid structure, but the explicit computation and storage of the closure (w.r.t. graphoid properties) of a set of independence statements is a computational hard problem. For this, we rely on a compact symbolic representation of the closure called fast closure and study three different combination strategies of two sets of independence statements, working on fast closures. We investigate when the complete DAG representability of the given models is preserved in the combined one.