The article discusses Bayesian methods for comparing different models. It introduces new ways to compare models when certain information is missing. The concepts of imaginary training samples, minimal training samples, and various types of Bayes factors are explained. These methods are applied to different types of distributions and linear regression systems. Simulation results are used to compare the effectiveness of the new Bayes factors. The Full Bayesian Significance Test is also presented and applied to a linear mixture model.