2014
DOI: 10.1017/s1471068414000283
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Abstract: Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination… Show more

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Cited by 12 publications
(7 citation statements)
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“…The main idea of lifted inference is to treat indistinguishable instances of random variables as one unit and perform inference at the population level. Lifted inference in the context of PLP has been performed by converting the problem to parfactor representation (Bellodi et al 2014) or weighted first-order model counting (den Broeck et al 2011). Lifted explanation graphs (Nampally and Ramakrishnan 2016) are a generalization of ground explanation graphs, which treat instances of random processes in a symbolic fashion.…”
Section: Related Workmentioning
confidence: 99%
“…The main idea of lifted inference is to treat indistinguishable instances of random variables as one unit and perform inference at the population level. Lifted inference in the context of PLP has been performed by converting the problem to parfactor representation (Bellodi et al 2014) or weighted first-order model counting (den Broeck et al 2011). Lifted explanation graphs (Nampally and Ramakrishnan 2016) are a generalization of ground explanation graphs, which treat instances of random processes in a symbolic fashion.…”
Section: Related Workmentioning
confidence: 99%
“…LP 2 (Bellodi et al 2014) uses an algorithm that extends Generalized Counting First Order Variable Elimination (GC-FOVE) (Taghipour et al 2013) for taking into account clauses that have variables in bodies not appearing in the head (existentially quantified variables). Weighted First Order Model Counting (WFOMC) (Van den Broeck et al 2014) uses a Skolemization algorithm that eliminates existential quantifiers from a theory without changing its weighted model count.…”
Section: Related Workmentioning
confidence: 99%
“…Various algorithms have been proposed for performing lifted inference for PLP (Van den Broeck et al 2014;Bellodi et al 2014), see Riguzzi et al (2017a) for a survey and comparison of the approaches.…”
Section: Introductionmentioning
confidence: 99%
“…The main idea of lifted inference is to treat indistinguishable instances of random variables as one unit and perform inference at the population level. Lifted inference in the context of PLP has been performed by converting the problem to parfactor representation [Bellodi et al, 2014] or weighted first-order model counting [ Van den Broeck et al, 2011]. Lifted explanation graphs [Nampally and Ramakrishnan, 2016] are a generalization of ground explanation graphs, which treat instances of random processes in a symbolic fashion.…”
Section: Related Workmentioning
confidence: 99%