Improper priors in Bayesian model selection can cause issues, but a new method called the intrinsic Bayes factor helps by creating proper prior distributions for comparison. This article introduces a limiting procedure that justifies using the Bayes factor with intrinsic priors, especially for nested and nonnested models. The procedure is compared to other approximations like the Bayesian information criterion and the fractional Bayes factor.