## Convergence Results for Relational Bayesian Networks

Manfred Jaeger
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 $r(a_1,\ldots,a_n)$\ on first-order properties of
$a_1,\ldots,a_n$\ 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.

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