Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Information Sciences: an International Journal
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Conditional Independence in A Coherent Finite Setting
Annals of Mathematics and Artificial Intelligence
Independence and Possibilistic Conditioning
Annals of Mathematics and Artificial Intelligence
Stochastic Independence in a Coherent Setting
Annals of Mathematics and Artificial Intelligence
Stochastic independence for upper and lower probabilities in a coherent setting
Technologies for constructing intelligent systems
Probabilistic Conditional Independence Structures
Probabilistic Conditional Independence Structures
Independence concepts for convex sets of probabilities
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Computing lower and upper expectations under epistemic independence
International Journal of Approximate Reasoning
Conditional independence structure and its closure: Inferential rules and algorithms
International Journal of Approximate Reasoning
Possibility theory: Conditional independence
Fuzzy Sets and Systems
Acyclic directed graphs representing independence models
International Journal of Approximate Reasoning
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In this paper we study conditional independence structures arising from conditional probabilities and lower conditional probabilities. Such models are based on notions of stochastic independence apt to manage also those situations where zero evaluations on possible events are present: this is particularly crucial for lower probability.The "graphoid" properties of such models are investigated, and the representation problem of conditional independence structures is dealt with by generalizing the well-known classic separation criteria for undirected and directed acyclic graphs. Our graphical models describe the independence statements and the possible logical dependencies among the random variables.