(eng): The aim of this work is to search for intuitive interpretations of penalized statistical estimators. Penalized estimates of the parameters of three models obtained by Bayesian reasoning are explained here to correspond to the intuition. First, the paper considers Bayesian estimates of the mean and covariance matrix for the multivariate normal distribution. Second, a connection of a robust regularized version of Mahalanobis distance with Bayesian estimation is discussed. Third, regularization networks, which represent a common nonparametric tool for regression modeling, are presented as Bayesian methods as well. On the whole, selected important multivariate and/or regression models are considered and novel interpretations are formulated.