Updates the model (when more than one observation is available) and saves the parameters (if available).

def _update_model(self, normalization_type='stats'):
        """
        Updates the model (when more than one observation is available) and saves the parameters (if available).
        """
        if self.num_acquisitions % self.model_update_interval == 0:

            # input that goes into the model (is unziped in case there are categorical variables)
            X_inmodel = self.space.unzip_inputs(self.X)

            # Y_inmodel is the output that goes into the model
            if self.normalize_Y:
                Y_inmodel = normalize(self.Y, normalization_type)
            else:
                Y_inmodel = self.Y

            self.model.updateModel(X_inmodel, Y_inmodel, None, None)

        # Save parameters of the model
        self._save_model_parameter_values()