  The EM-algorithm is a general procedure to get maximum likelihood estimates
if part of the observations on the variables of a network are missing. In this
paper a stochastic version of the algorithm is adapted to probabilistic neural
networks describing the associative dependency of variables. These networks
have a probability distribution, which is a special case of the distribution
generated by probabilistic inference networks. Hence both types of networks can
be combined allowing to integrate probabilistic rules as well as unspecified
associations in a sound way. The resulting network may have a number of
interesting features including cycles of probabilistic rules, hidden
'unobservable' variables, and uncertain and contradictory evidence.
