Access the full text.
Sign up today, get DeepDyve free for 14 days.
P. Walley (2000)
Towards a unified theory of imprecise probabilityInt. J. Approx. Reason., 24
J. Pearl (1988)
Probabilistic reasoning in intelligent systems
M. Studený (1997)
Semigraphoids and structures of probabilistic conditional independenceAnnals of Mathematics and Artificial Intelligence, 21
F.G. Cozman (1997)
Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence
Fabio Cozman (1998)
Irrelevance and Independence Relations in quasi-Bayesian NetworksArXiv, abs/1301.7368
S. Moral, Nic Wilson (1995)
Revision rules for convex sets of probabilities
(1999)
Conditional independence, in: Encyclopedia of Statistical Sciences
(1993)
Propagation of convex sets of probabilities in directed acyclic networks, in: Uncertainty in Intelligent Systems
P.P. Shenoy, G. Shafer (1990)
Uncertainty in Artificial Intelligence 4
G. Shafer, Prakash Shenoy (1991)
Local Computation in Hypertrees
R. Jeffrey (1968)
The Logic of Decision
E. Fagiuoli, Marco Zaffalon (1998)
2U: An Exact Interval Propagation Algorithm for Polytrees with Binary VariablesArtif. Intell., 106
Prakash Shenoy, G. Shafer (1990)
Axioms for probability and belief-function proagation
Fabio Cozman (2000)
Credal networksArtif. Intell., 120
A. Cano, S. Moral (2000)
Handbook of Defeasible and Uncertainty Management Systems, Vol. 5, Algorithms for Uncertainty and Defeasible Reasoning
Fabio Cozman, P. Walley (2005)
Graphoid properties of epistemic irrelevance and independenceAnnals of Mathematics and Artificial Intelligence, 45
Paolo Vicig (2000)
Epistemic independence for imprecise probabilities
J.E. Cano, S. Moral, J.F. Verdegay-López (1993)
Uncertainty in Intelligent Systems
D. Dubois, S. Moral, H. Prade (1999)
Handbook of Defeasible Reasoning and Uncertainty Management Systems, Vol. 3, Belief Change
K. Fertig, J. Breese (1993)
Probability Intervals Over Influence DiagramsIEEE Trans. Pattern Anal. Mach. Intell., 15
D. Dubois, S. Moral, H. Prade (1998)
Belief Change Rules in Ordinal and Numerical Uncertainty Theories
A. Cano, S. Moral (1999)
A Review of Propagation Algorithms for Imprecise Probabilities
A. Cano, J. Cano, S. Moral (1994)
Convex Sets Of Probabilities Propagation By Simulated Annealing
Fabio Cozman (2000)
Separation Properties of Sets of Probability MeasuresArXiv, abs/1301.3845
A. Cano, S. Moral (2000)
Algorithms for Imprecise Probabilities
N. Wilson, S. Moral (1994)
Proceedings of the Eleventh European Conference on Artificial Intelligence (ECAI'94)
S. Moral (2005)
Epistemic irrelevance on sets of desirable gamblesAnnals of Mathematics and Artificial Intelligence, 45
P. Walley (1990)
Statistical Reasoning with Imprecise Probabilities
G. Coletti, R. Scozzafava (2002)
Stochastic independence for upper and lower probabilities in a coherent setting
E. Castillo, J. Gutiérrez, A. Hadi (1996)
Expert Systems and Probabiistic Network Models
Fabio Cozman (1997)
Robustness Analysis of Bayesian Networks with Local Convex Sets of DistributionsArXiv, abs/1302.1531
Inés Couso, S. Moral, P. Walley (2000)
A survey of concepts of independence for imprecise probabilitiesRisk Decision and Policy, 5
F.G. Cozman (1999)
Proceedings of the Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (Ecsqaru'99)
Fabio Cozman (1999)
Irrelevance and Independence Axioms in Quasi-Bayesian Theory
G. Coletti, D. Dubois, R. Scozzafava (1995)
Mathematical models for handling partial knowledge in artificial intelligence
J. Pearl (1991)
Probabilistic reasoning in intelligent systems - networks of plausible inference
A.P. Dawid (1999)
Encyclopedia of Statistical Sciences, Update Vol. 2
Nic Wilson, S. Moral (1994)
A Logical View of Probability
B. Tessem (1992)
Interval probability propagationInt. J. Approx. Reason., 7
E. Castillo, J. Gutiérrez, A. Hadi (1996)
Expert Systems and Probabilistic Network Models
L. Campos, S. Moral (1995)
Independence Concepts for Convex Sets of ProbabilitiesArXiv, abs/1302.4940
K. Fertig, J. Breese (2013)
Interval Influence Diagrams
This paper investigates the concept of strong conditional independence for sets of probability measures. Couso, Moral and Walley [7] have studied different possible definitions for unconditional independence in imprecise probabilities. Two of them were considered as more relevant: epistemic independence and strong independence. In this paper, we show that strong independence can have several extensions to the case in which a conditioning to the value of additional variables is considered. We will introduce simple examples in order to make clear their differences. We also give a characterization of strong independence and study the verification of semigraphoid axioms.
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Oct 10, 2004
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.