Computer models are widely used for the prediction of complex physical phenomena. Based on observations of these physical phenomena, it is possible to calibrate the model parameters. In most cases, such computer models are misspecified, and the calibration process must be improved by including a model error term. The model error hyperparameters are, however, rarely learned jointly with the model parameters to reduce the dimensionality of the problem. Sequential and nonsequential approaches have been introduced to estimate the hyperparameters. The former, such as the Kennedy and O'Hagan (KOH) framework, estimates the model error hyperparameters before calibrating the model parameters. The latter, such as the full maximum a posteriori (FMP), introduces a functional dependence between the model parameters and the model error hyperparameters. Despite being more reliable in some cases (bimodality, e.g.), the FMP method still fails to estimate correctly the posterior distribution shape. This work proposes a new methodology for treating the model error term in computer code calibration. It builds upon the KOH and FMP framework. Called the complete maximum a posteriori (CMP) method, it provides a closed-form expression for the marginalization integral over the model error hyperparameters, significantly reducing the dimensionality of the calibration problem. Such expression relies on a set of assumptions that are more general and less stringent than the ones usually employed. The CMP method is applied to four examples of increasing complexity, from elementary to real fluid dynamics problems, including or not bimodality. Compared to the true reference solution and unlike the KOH and FMP, the CMP method correctly captures the shape of the posterior distribution, including all modes and their weights. Moreover, it provides an accurate estimate of the distribution tails.