Logical Bayesian Networks (LBNs) have recently been introduced as another
language for knowledge based model construction of Bayesian networks, besides
existing languages such as Probabilistic Relational Models (PRMs) and Bayesian
Logic Programs (BLPs). The original description of LBNs introduces them as a
variant of BLPs and discusses the differences with BLPs but still leaves room
for a deeper discussion of the relationship between LBNs and BLPs.  Also the
relationship to PRMs was not treated in much detail.

In this paper, we first give a more compact and clear definition of LBNs.
Next, we describe in more detail how PRMs and BLPs relate to LBNs. Like this
we not only see what the advantages and disadvantages of LBNs are with respect
to PRMs and BLPs, we also gain  more insight into the relationships between
PRMs and BLPs.