Relational Bayesian Networks

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.

Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)

Cited by in corpus (5)

• Loglinear models for first-order probabilistic reasoning
• CP-logic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming
• Complexity Analysis and Variational Inference for Interpretation-based Probabilistic Description Logic
• Deriving a Stationary Dynamic Bayesian Network from a Logic Program with Recursive Loops
• Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments