Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Axioms and algorithms for inferences involving probabilistic independence
Information and Computation
Information Sciences: an International Journal
Stochastic independence, algebraic independence and abstract connectedness
Theoretical Computer Science
Subset Dependencies and a Completeness Result for a Subclass of Embedded Multivalued Dependencies
Journal of the ACM (JACM)
Formal Properties of Conditional Independence in Different Calculi of AI
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Strong Conditional Independence for Credal Sets
Annals of Mathematics and Artificial Intelligence
On Stochastic Conditional Independence: the Problems of Characterization and Description
Annals of Mathematics and Artificial Intelligence
Stable independence and complexity of representation
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Graphoid properties of qualitative possibilistic independence relations
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Graphoid properties of epistemic irrelevance and independence
Annals of Mathematics and Artificial Intelligence
Epistemic irrelevance on sets of desirable gambles
Annals of Mathematics and Artificial Intelligence
Computing lower and upper expectations under epistemic independence
International Journal of Approximate Reasoning
A Property of Independency Relations Induced by Probabilistic Distributions with Binary Variables
Fundamenta Informaticae - SPECIAL ISSUE ON TRAJECTORIES OF LANGUAGE THEORY Dedicated to the memory of Alexandru Mateescu
Conditional independence structure and its closure: Inferential rules and algorithms
International Journal of Approximate Reasoning
Acyclic Directed Graphs to Represent Conditional Independence Models
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Acyclic directed graphs representing independence models
International Journal of Approximate Reasoning
Exploiting independencies to compute semigraphoid and graphoid structures
International Journal of Approximate Reasoning
Finding P-maps and I-maps to represent conditional independencies
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
A Property of Independency Relations Induced by Probabilistic Distributions with Binary Variables
Fundamenta Informaticae - SPECIAL ISSUE ON TRAJECTORIES OF LANGUAGE THEORY Dedicated to the memory of Alexandru Mateescu
Qualitative combination of independence models
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
International Journal of Approximate Reasoning
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The concept of conditional independence (CI) has an important role in probabilistic reasoning, that is a branch of artificial intelligence where knowledge is modeled by means of a multidimensional finite‐valued probability distribution. The structures of probabilistic CI are described by means of semigraphoids, that is lists of CI‐statements closed under four concrete inference rules, which have at most two antecedents. It is known that every CI‐model is a semigraphoid, but the converse is not true. In this paper, the semigraphoid closure of every couple of CI‐statements is proved to be a CI‐model. The substantial step to it is to show that every probabilistically sound inference rule for axiomatic characterization of CI properties (= axiom), having at most two antecedents, is a consequence of the semigraphoid inference rules. Moreover, all potential dominant triplets of the mentioned semigraphoid closure are found.