  In this paper we investigate the geometry of the likelihood of the unknown
parameters in a simple class of Bayesian directed graphs with hidden variables.
This enables us, before any numerical algorithms are employed, to obtain
certain insights in the nature of the unidentifiability inherent in such
models, the way posterior densities will be sensitive to prior densities and
the typical geometrical form these posterior densities might take. Many of
these insights carry over into more complicated Bayesian networks with
systematic missing data.
