2U: an exact interval propagation algorithm for polytrees with binary variables
Enrico Fagiuoli,Marco Zaffalon +1 more
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TLDR
The computation on a general Bayesian network with convex sets of conditional distributions is formalized as a global optimization problem and it is shown that such a problem can be reduced to a combinatorial problem, suitable to exact algorithmic solutions.About:
This article is published in Artificial Intelligence.The article was published on 1998-11-01 and is currently open access. It has received 143 citations till now. The article focuses on the topics: Bayesian network & Conditional probability distribution.read more
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Rudiments of rough sets
Zdziasław Pawlak,Andrzej Skowron +1 more
TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.
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Credal networks
TL;DR: Credal networks as mentioned in this paper is a compact representation for a set of probability distributions, and it is closely related to very popular statistical models such as Markov chains, Bayesian networks, Markov random fields, etc.
Proceedings Article
Lifted first-order probabilistic inference
TL;DR: This paper presents the first exact inference algorithm that operates directly on a first- order level, and that can be applied to any first-order model (specified in a language that generalizes undirected graphical models).
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Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model
TL;DR: This paper investigates three different classification properties, and suggests three distinct definitions accordingly, based on the common structure of the specific definitions of relative reducts and discernibility matrices.
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Graphical models for imprecise probabilities
TL;DR: An overview of graphical models that can handle imprecision in probability values is presented, as this model has received considerable attention in the literature.
References
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Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
BookDOI
Handbook of global optimization
TL;DR: This paper presents algorithms for global optimization of mixed-integer nonlinear programs using the Reformulation-Linearization/Convexification Technique (RLT) and an introduction to dynamical search.
Book
Uncertainty in Artificial Intelligence 2
TL;DR: Qualitative Probabilistic Reasoning and Cognitive models, Dempster-Shafer Theory in Knowledge Representation, and Possibility Theory: Semantics and Applications.
Journal ArticleDOI
Measures of uncertainty in expert systems
TL;DR: Each of the four measures seems to be useful in special kinds of problems, but only lower and upper previsions appear to be sufficiently general to model the most common types of uncertainty.