The variable selection problem has a natural Bayesian solution: Any collection of models having different sets of regressors can be computed via their Bayes factors.
Let , then
and let , then
Then we can construct the following Gibbs sampler:
unit information prior
the parameter estimation should be invariant to changes in the scale of the regressors.
For the second case, we can derive a Monte Carlo approximation: