  In Bayesian analysis of multi-way contingency tables, the selection of a
prior distribution for either the log-linear parameters or the cell
probabilities parameters is a major challenge. In this paper, we define a
flexible family of conjugate priors for the wide class of discrete hierarchical
log-linear models, which includes the class of graphical models. These priors
are defined as the Diaconis--Ylvisaker conjugate priors on the log-linear
parameters subject to "baseline constraints" under multinomial sampling. We
also derive the induced prior on the cell probabilities and show that the
induced prior is a generalization of the hyper Dirichlet prior. We show that
this prior has several desirable properties and illustrate its usefulness by
identifying the most probable decomposable, graphical and hierarchical
log-linear models for a six-way contingency table.
