The article discusses a new method called the power-expected posterior prior, which is a type of prior distribution used in Bayesian statistics. This method is a mixture of g-priors and is particularly useful for model selection in linear models. The researchers show that this new method can be represented as a mixture of g-priors, making it computationally manageable. They also compare it to other similar methods and highlight the importance of the posterior distribution of g in model selection and averaging.