Parameter estimation of probabilistic models for discrete variables is often infeasible due to the calculation of the normalization constant required to ensure the model represents a valid probability distribution, and various approaches have been developed to resolve this problem. In this paper, we consider a computationally feasible estimator for discrete probabilistic models based on a concept of empirical localization. Furthermore, we propose a computationally feasible estimator similar to the MAP estimator in Bayesian estimation by extending the above estimator.