2014
DOI: 10.1007/s10994-014-5443-2
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Abstract: Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey, we review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently … Show more

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Cited by 62 publications
(40 citation statements)
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“…A term is ground if it contains no first-order variables; otherwise it is a first-order term. In the context of a statistical model, we refer to first-order terms as Parametrized Random Variables (PRVs) [16]. A grounding replaces each first-order variable in a term by a constant; the result is a ground term.…”
Section: B Relational Datamentioning
confidence: 99%
“…A term is ground if it contains no first-order variables; otherwise it is a first-order term. In the context of a statistical model, we refer to first-order terms as Parametrized Random Variables (PRVs) [16]. A grounding replaces each first-order variable in a term by a constant; the result is a ground term.…”
Section: B Relational Datamentioning
confidence: 99%
“…From the high-level point of view of this review, all lifted inference algorithms are concerned with a similar problem: Efficient inference in graphical models containing symmetries. For a more in-depth discussion, we refer to the review papers of Kersting (2012) and Kimmig et al (2015), as well as the books by De and Getoor and Taskar (2007).…”
Section: Lifted Inferencementioning
confidence: 99%
“…In this survey, we systematically review the literature on these approaches and develop a new conceptual model to classify the approaches. Previous surveys on this topic (Kersting, 2012;Kimmig, Mihalkova, & Getoor, 2015) have focussed on a specific class of such algorithms, known as lifted inference. In this review, we put more emphasis on inference in sequential processes (known as Bayesian filtering, a method that is highly relevant for many different application domains), and consider algorithms devised in a number of different research fields, like control theory, modeling and simulation, and computer vision.…”
Section: Introductionmentioning
confidence: 99%
“…Parametrized Random Variables (PRVs) [45]. A grounding replaces each first-order variable in a term/literal/formula by a constant, the result is a ground term.…”
Section: Notation and Definitionmentioning
confidence: 99%