  The network influence model is a model for binary outcome variables that
accounts for dependencies between outcomes for units that are relationally
tied. The basic influence model was previously extended to afford a suite of
new dependence assumptions and because of its relation to traditional Markov
random field models it is often referred to as the auto logistic
actor-attribute model (ALAAM). We extend on current approaches for fitting
ALAAMs by presenting a comprehensive Bayesian inference scheme that supports
testing of dependencies across subsets of data and the presence of missing
data. We illustrate different aspects of the procedures through three empirical
examples: masculinity attitudes in an all-male Australian school class,
educational progression in Swedish schools, and un-employment among adults in a
community sample in Australia.
