The article introduces new methods for choosing the best model in signal processing by considering prior information on model parameters. By incorporating this prior knowledge, researchers have developed two new criteria for selecting models in linear regression. These criteria, based on the Kullback-Leibler divergence, aim to improve upon existing model selection methods like AIC and BIC.