Add to ORKGLifting aims at improving the efficiency of probabilistic inference by exploiting symmetries in the model. Various methods for lifted probabilistic inference have been proposed, but our understanding of these methods and the relationships between them is still limited, compared to their propositional counterparts. The only existing theoretical characterization of lifting is a completeness result for weighted first-order model counting. This paper addresses the question whether the same completeness result holds for other lifted inference algorithms. We answer this question positively for lifted variable elimination (LVE). Our proof relies on introducing a novel inference operator for LVE.status: publishe
Lifted inference algorithms exploit repeated structure in probabilistic models to answer queries eff...
Probabilistic logical languages provide powerful formalisms for knowledge representation and learnin...
Various representations and inference methods have been proposed for lifted probabilistic inference ...
Lifting aims at improving the efficiency of probabilistic inference by exploiting symmetries in the ...
Various methods for lifted probabilistic inference have been proposed, but our understanding of thes...
Lifted probabilistic inference methods exploit symmetries in the structure of probabilistic models t...
Representing, learning, and reasoning about knowledge are central to artificial intelligence (AI). A...
Lifted inference methods exploit regularities in the structure of probabilistic models: they perform...
In this position paper we raise the question whether lifted inference can be performed by 'lifting' ...
Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models ...
Probabilistic logics are receiving a lot of attention today because of their expressive power for kn...
Probabilistic logics are receiving a lot of attention today because of their expres-sive power for k...
A probabilistic program often gives rise to a complicated underlying probabilistic model. Performing...
In this paper we study lifted inference for the Weighted First-Order Model Counting problem (WFOMC),...
Lifted inference algorithms exploit symmetries in probabilistic models to speed up inference. They s...
Lifted inference algorithms exploit repeated structure in probabilistic models to answer queries eff...
Probabilistic logical languages provide powerful formalisms for knowledge representation and learnin...
Various representations and inference methods have been proposed for lifted probabilistic inference ...
Lifting aims at improving the efficiency of probabilistic inference by exploiting symmetries in the ...
Various methods for lifted probabilistic inference have been proposed, but our understanding of thes...
Lifted probabilistic inference methods exploit symmetries in the structure of probabilistic models t...
Representing, learning, and reasoning about knowledge are central to artificial intelligence (AI). A...
Lifted inference methods exploit regularities in the structure of probabilistic models: they perform...
In this position paper we raise the question whether lifted inference can be performed by 'lifting' ...
Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models ...
Probabilistic logics are receiving a lot of attention today because of their expressive power for kn...
Probabilistic logics are receiving a lot of attention today because of their expres-sive power for k...
A probabilistic program often gives rise to a complicated underlying probabilistic model. Performing...
In this paper we study lifted inference for the Weighted First-Order Model Counting problem (WFOMC),...
Lifted inference algorithms exploit symmetries in probabilistic models to speed up inference. They s...
Lifted inference algorithms exploit repeated structure in probabilistic models to answer queries eff...
Probabilistic logical languages provide powerful formalisms for knowledge representation and learnin...
Various representations and inference methods have been proposed for lifted probabilistic inference ...