## Paper: Convergence Results for Relational Bayesian Networks (at LICS 1998)

**Manfred Jaeger**

### Abstract

Relational Bayesian networks are an extension of the method of
probabilistic model construction by Bayesian networks. They define
probability distributions on finite relational structures by
conditioning the probability of a ground atom r(a_{1}, ..., a
_{n}) on first-order properties of a_{1}, ..., a_{n
} that have been established by previous random decisions. In this
paper we investigate from a finite model theory perspective the
convergence properties of the distributions defined in this manner. A
subclass of relational Bayesian networks is identified that define
distributions with convergence laws for first-order properties

### BibTeX

@InProceedings{Jaeger-ConvergenceResultsf, author = {Manfred Jaeger}, title = {Convergence Results for Relational Bayesian Networks}, booktitle = {Proceedings of the Thirteenth Annual IEEE Symposium on Logic in Computer Science (LICS 1998)}, year = {1998}, month = {June}, pages = {44--55 }, location = {Indianapolis, IN, USA}, publisher = {IEEE Computer Society Press} }