Add to ORKGStatistical relational models provide compact encodings of probabilistic dependencies in relational domains, but result in highly intractable graphical models. The goal of lifted inference is to carry out probabilistic inference without needing to reason about each individual separately, by instead treating exchangeable, undistinguished objects as a whole. In this paper, we study the domain recursion inference rule, which, despite its central role in early theoretical results on domain-lifted inference, has later been believed redundant. We show that this rule is more powerful than expected, and in fact significantly extends the range of models for which lifted inference runs in time polynomial in the number of individuals in the domain. Th...
First-order model counting emerged recently as a novel rea- soning task, at the core of efficient al...
Many AI problems arising in a wide variety of fields such as machine learning, semantic web, network...
Statistical relational learning (SRL) augments probabilistic models with relational representations ...
Statistical relational models provide compact encodings of probabilistic dependencies in relational...
In recent work, we proved that the domain recursion inference rule makes domain-lifted inference pos...
Statistical relational models combine aspects of first-order logic, databases and probabilistic grap...
Statistical relational models combine aspects of first-order logic and probabilistic graphical model...
This tutorial explains the core ideas behind lifted probabilistic inference in statistical relationa...
In this paper we study lifted inference for the Weighted First-Order Model Counting problem (WFOMC),...
We introduce a general framework for defining classes of probabilistic-logic models and associated c...
Representing, learning, and reasoning about knowledge are central to artificial intelligence (AI). A...
High-level representations of uncertainty, such as probabilistic logics and programs, have been arou...
Lifted inference algorithms exploit symmetries in probabilistic models to speed up inference. They s...
Following ideas in Poole~\poole, which we correct, formalize and extend, this paper presents the fir...
Representations that mix graphical models and first-order logic - called either first-order or relat...
First-order model counting emerged recently as a novel rea- soning task, at the core of efficient al...
Many AI problems arising in a wide variety of fields such as machine learning, semantic web, network...
Statistical relational learning (SRL) augments probabilistic models with relational representations ...
Statistical relational models provide compact encodings of probabilistic dependencies in relational...
In recent work, we proved that the domain recursion inference rule makes domain-lifted inference pos...
Statistical relational models combine aspects of first-order logic, databases and probabilistic grap...
Statistical relational models combine aspects of first-order logic and probabilistic graphical model...
This tutorial explains the core ideas behind lifted probabilistic inference in statistical relationa...
In this paper we study lifted inference for the Weighted First-Order Model Counting problem (WFOMC),...
We introduce a general framework for defining classes of probabilistic-logic models and associated c...
Representing, learning, and reasoning about knowledge are central to artificial intelligence (AI). A...
High-level representations of uncertainty, such as probabilistic logics and programs, have been arou...
Lifted inference algorithms exploit symmetries in probabilistic models to speed up inference. They s...
Following ideas in Poole~\poole, which we correct, formalize and extend, this paper presents the fir...
Representations that mix graphical models and first-order logic - called either first-order or relat...
First-order model counting emerged recently as a novel rea- soning task, at the core of efficient al...
Many AI problems arising in a wide variety of fields such as machine learning, semantic web, network...
Statistical relational learning (SRL) augments probabilistic models with relational representations ...