Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Bounded-parameter Markov decision process
Artificial Intelligence
Artificial Intelligence
Introduction to Linear Optimization
Introduction to Linear Optimization
Semigraphoids and structures of probabilistic conditional independence
Annals of Mathematics and Artificial Intelligence
Reasoning about Uncertainty
Expressive probabilistic description logics
Artificial Intelligence
Updating coherent previsions on finite spaces
Fuzzy Sets and Systems
Branching and bounds tighteningtechniques for non-convex MINLP
Optimization Methods & Software - GLOBAL OPTIMIZATION
Concentration inequalities and laws of large numbers under epistemic and regular irrelevance
International Journal of Approximate Reasoning
Artificial Intelligence
Separation properties of sets of probability measures
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Independence concepts for convex sets of probabilities
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Theoretical foundations for abstraction-based probabilistic planning
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Kuznetsov independence of variables X and Y means that, for any pair of bounded functions f(X) and g(Y), E[f(X)g(Y)]=E[f(X)]@?E[g(Y)], where E[@?] denotes interval-valued expectation and @? denotes interval multiplication. We present properties of Kuznetsov independence for several variables, and connect it with other concepts of independence in the literature; in particular we show that strong extensions are always included in sets of probability distributions whose lower and upper expectations satisfy Kuznetsov independence. We introduce an algorithm that computes lower expectations subject to judgments of Kuznetsov independence by mixing column generation techniques with nonlinear programming. Finally, we define a concept of conditional Kuznetsov independence, and study its graphoid properties.