  A method for implicit variable selection in mixture of experts frameworks is
proposed. We introduce a prior structure where information is taken from a set
of independent covariates. Robust class membership predictors are identified
using a normal gamma prior. The resulting model setup is used in a finite
mixture of Bernoulli distributions to find homogenous clusters of women in
Mozambique based on their information sources on HIV. Fully Bayesian inference
is carried out via the implementation of a Gibbs sampler.
