Relational Bayesian Networks

2013/02/06 by Manfred Jaeger, Jaeger, Manfred · 3 citations
Computer Science · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Semantic Web and Ontologies

paper · pdf · doi:10.48550/arxiv.1302.1550

openalex publication_date 2013/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probability distribution over the relations is directly represented by a Bayesian network. By using a powerful way of specifying conditional probability distributions in these networks, the resulting formalism is more expressive than the previous ones. Particularly, it provides for constraints on equalities of events, and it allows to define complex, nested combination functions.

Cited by

• The Complexity of Bayesian Networks Specified by Propositional and\n Relational Languages
• Complexity Analysis and Variational Inference for Interpretation-based\n Probabilistic Description Logic
• Dynamic Bayesian Ontology Languages
• Why is Compiling Lifted Inference into a Low-Level Language so Effective?

Related

• Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments
• Importance Sampling on Relational Bayesian Networks
• Using First-Order Probability Logic for the Construction of Bayesian Networks
• A New Characterization of Probabilities in Bayesian Networks
• Bayesian Logic Programs