  Consider a set of categorical variables where at least one of them is binary.
The log-linear model that describes the counts in the resulting contingency
table implies a specific logistic regression model, with the binary variable as
the outcome. Within the Bayesian framework, the $g$-prior and mixtures of
$g$-priors are commonly assigned to the parameters of a generalized linear
model. We prove that assigning a $g$-prior (or a mixture of $g$-priors) to the
parameters of a certain log-linear model designates a $g$-prior (or a mixture
of $g$-priors) on the parameters of the corresponding logistic regression. By
deriving an asymptotic result, and with numerical illustrations, we demonstrate
that when a $g$-prior is adopted, this correspondence extends to the posterior
distribution of the model parameters. Thus, it is valid to translate inferences
from fitting a log-linear model to inferences within the logistic regression
framework, with regard to the presence of main effects and interaction terms.
