Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Graphoids: a qualitative framework for probabilistic inference
Graphoids: a qualitative framework for probabilistic inference
An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Independence concepts in possibility theory: part I
Fuzzy Sets and Systems
Representation of irrelevance relations by annotated graphs
Fundamenta Informaticae
Conditional independence relations in possibility theory
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - special issue on models for imprecise probabilities and partial knowledge
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Semigraphoids and structures of probabilistic conditional independence
Annals of Mathematics and Artificial Intelligence
Conditional Independence in A Coherent Finite Setting
Annals of Mathematics and Artificial Intelligence
Strong Conditional Independence for Credal Sets
Annals of Mathematics and Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Conditional independence structures and graphical models
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Probabilistic Conditional Independence Structures: With 42 Illustrations (Information Science and Statistics)
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
Bayesian network learning algorithms using structural restrictions
International Journal of Approximate Reasoning
Racing algorithms for conditional independence inference
International Journal of Approximate Reasoning
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
Possibility theory: Conditional independence
Fuzzy Sets and Systems
Operations for inference in continuous Bayesian networks with linear deterministic variables
International Journal of Approximate Reasoning
Learning Bayesian network parameters under order constraints
International Journal of Approximate Reasoning
Logical and algorithmic properties of stable conditional independence
International Journal of Approximate Reasoning
Compositional models and conditional independence in evidence theory
International Journal of Approximate Reasoning
On the implication problem for probabilistic conditional independency
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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
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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In this paper we study the problem of representing probabilistic independence models, in particular those closed under graphoid properties. We focus on acyclic directed graph (DAG): a new algorithm to build a DAG, given an ordering among random variables, is described and peculiarities and advantages of this approach are discussed. Moreover, we provide a necessary and sufficient condition for the existence of a perfect map representing an independence model and we describe an algorithm based on this characterization.