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Abstract. Probabilistic relational models (PRMs) are a language for describing statistical models over typed relational domains. A PRM models the uncertainty over the attributes of objects in the domain and uncertainty over the relations between the objects.
Oct 4, 2018 · While notions of abstraction have matured for deterministic systems, the case for abstracting probabilistic models is not yet fully understood.
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Abstracting Probabilistic Relational Models - ResearchGate
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In this paper, we develop a foundational framework for abstraction in probabilistic relational models from first principles. These models borrow syntactic ...
Probabilistic relational models (PRMs) are a rich representation language for struc- tured statistical models. They combine a frame-based logical ...
Missing: Abstracting | Show results with:Abstracting
... Abstraction typically involves suppressing irrelevant information and therefore allows reasoning about complex problems that would otherwise be infeasible.
Particle-filter based. DPRM inference that uses abstraction smoothing to generalize over related objects. Intro to Probabilistic Relational Models – p.24/24.
Sep 6, 2024 · Abstract. Probabilistic relational models provide a well-established for- malism to combine first-order logic and probabilistic models, ...
In this paper, we propose a consistent extension of the relational model. We present a revised relational structure and extend the relational algebra.
The vocabulary of a relational model consists of a set of classes and a set ofrelations. Each entity type is associated with a set of attributes. Each attribute.
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Abstract. Probabilistic relational models (PRMs) are a language for describing statistical models over typed relational domains. A PRM models the un-.