  In this work we discuss a novel model prior probability for variable
selection in linear regression. The idea is to determine the prior mass in an
objective sense, by considering the worth of each of the possible regression
models, given the number of covariates under consideration. Through a
simulation study, we show that the proposed prior outperforms the uniform prior
and the Scott \& Berger prior in a scenario of no prior knowledge about the
size of the true regression models. We illustrate the use of the prior using
two well-known data sets with, respectively, 15 and 4 covariates.
