  Now that Bayesian Networks (BNs) have become widely used, an appreciation is
developing of just how critical an awareness of the sensitivity and robustness
of certain target variables are to changes in the model. When time resources
are limited, such issues impact directly on the chosen level of complexity of
the BN as well as the quantity of missing probabilities we are able to elicit.
Currently most such analyses are performed once the whole BN has been elicited
and are based on Kullback-Leibler information measures. In this paper we argue
that robustness methods based instead on the familiar total variation distance
provide simple and more useful bounds on robustness to misspecification which
are both formally justifiable and transparent. We demonstrate how such formal
robustness considerations can be embedded within the process of building a BN.
Here we focus on two particular choices a modeller needs to make: the choice of
the parents of each node and the number of levels to choose for each variable
within the system. Our analyses are illustrated throughout using two BNs drawn
from the recent literature.
