Exploring Unknown Universes in Probabilistic Relational Models
Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them. Lifted inference algorithms handle sets of known individuals for tractable inference. Universes may not always be known, though, or may only described by assumptions such as "small universes are more likely". Without a universe, inference is no longer possible for lifted algorithms, losing their advantage of tractable inference. The aim of this paper is to define a semantics for models with unknown universes decoupled from a specific constraint language to enable lifted and thereby, tractable inference.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Polynomial-Time Relational Probabilistic Inference in Open Universes
Reasoning under uncertainty is a fundamental challenge in Artificial Intelligence. As with most of these challenges, there is a harsh dilemma between the expressive power of the language used, and the tractability of the…
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains
We propose a framework for learning a fragment of probabilistic computation tree logic (pCTL) formulae from a set of states that are labeled as safe or unsafe. We work in a relational setting and combine ideas from relat…
Relational ReasoningEffective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data
With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning tec…
BIG-bench Machine LearningMedical DiagnosisRelational ReasoningTowards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models
Probabilistic relational models provide a well-established formalism to combine first-order logic and probabilistic models, thereby allowing to represent relationships between objects in a relational domain. At the same …
Privacy PreservingScalable Deep Generative Relational Models with High-Order Node Dependence
We propose a probabilistic framework for modelling and exploring the latent structure of relational data. Given feature information for the nodes in a network, the scalable deep generative relational model (SDREM) builds…
Data AugmentationLink PredictionVocal Bursts Intensity Prediction