The article discusses a method called Bayesian Variable Selection using I-Priors for linear models. By incorporating additional information (prior knowledge) into the data analysis, this approach helps in selecting the best variables for the model. The I-priors, a type of Gaussian distribution, are used to improve the accuracy of the model by considering the Fisher information of the model parameters. The researchers found that the I-prior method outperformed other techniques in terms of model size, predictive ability, and overall model performance in both simulated and real-world applications.