AbstractThe paper presents a generic approach of approximating inference. The method is based on the concept of valuation algebras with its wide range of possible applications in many different domains. We present convenient resource-bounded anytime algorithms, where the maximal time of computation is determined by the user
Abstract. Previous work on context-specific independence in Bayesian networks is driven by a common ...
AbstractIn this paper, we generalize the utility theory to allow to use various performance measures...
In many applications a key step is estimating some unknown quantity ~$mu$ from a sequence of trials,...
The paper presents a generic approach of approximating inference. The method is based on the concept...
In this thesis, we construct a general theoretical framework for anytime inference which automatical...
This book provides a rigorous algebraic study of the most popular inference formalisms with a specia...
Many different formalisms for treating uncertainty or, more generally, information and knowledge, ha...
Valuation algebras abstract a large number of formalisms for automated reasoning and enable the defi...
AbstractThis paper proposes a new approximation method for Dempster–Shafer belief functions. The met...
Valuation algebras abstract a large number of formalisms for automated reasoning and enable the defi...
In most real-world applications the choice of the right representation language represents a fundame...
This article describes an approximation algorithm for computing the probability of propositional for...
This Article is brought to you for free and open access by the Computer Science at ScholarWorks@UMas...
Abstract. Reasoning with large or complex ontologies is one of the bottle-necks of the Semantic Web....
Reasoning with large or complex ontologies is one of the bottle-necks of the Semantic Web. In this p...
Abstract. Previous work on context-specific independence in Bayesian networks is driven by a common ...
AbstractIn this paper, we generalize the utility theory to allow to use various performance measures...
In many applications a key step is estimating some unknown quantity ~$mu$ from a sequence of trials,...
The paper presents a generic approach of approximating inference. The method is based on the concept...
In this thesis, we construct a general theoretical framework for anytime inference which automatical...
This book provides a rigorous algebraic study of the most popular inference formalisms with a specia...
Many different formalisms for treating uncertainty or, more generally, information and knowledge, ha...
Valuation algebras abstract a large number of formalisms for automated reasoning and enable the defi...
AbstractThis paper proposes a new approximation method for Dempster–Shafer belief functions. The met...
Valuation algebras abstract a large number of formalisms for automated reasoning and enable the defi...
In most real-world applications the choice of the right representation language represents a fundame...
This article describes an approximation algorithm for computing the probability of propositional for...
This Article is brought to you for free and open access by the Computer Science at ScholarWorks@UMas...
Abstract. Reasoning with large or complex ontologies is one of the bottle-necks of the Semantic Web....
Reasoning with large or complex ontologies is one of the bottle-necks of the Semantic Web. In this p...
Abstract. Previous work on context-specific independence in Bayesian networks is driven by a common ...
AbstractIn this paper, we generalize the utility theory to allow to use various performance measures...
In many applications a key step is estimating some unknown quantity ~$mu$ from a sequence of trials,...
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