def gaussian_prior_model_for_arguments(self, arguments):
        """
        Create a new instance of model mapper with a set of Gaussian priors based on tuples provided by a previous \
        nonlinear search.

        Parameters
        ----------
        arguments: [(float, float)]
            Tuples providing the mean and sigma of gaussians

        Returns
        -------
        new_model: ModelMapper
            A new model mapper populated with Gaussian priors
        """
        new_model = copy.deepcopy(self)

        model_arguments = {t.name: arguments[t.prior] for t in self.direct_prior_tuples}

        for tuple_prior_tuple in self.tuple_prior_tuples:
            setattr(new_model, tuple_prior_tuple.name,
                    tuple_prior_tuple.prior.gaussian_tuple_prior_for_arguments(arguments))
        for prior_tuple in self.direct_prior_tuples:
            setattr(new_model, prior_tuple.name, model_arguments[prior_tuple.name])
        for constant_tuple in self.constant_tuples:
            setattr(new_model, constant_tuple.name, constant_tuple.constant)

        for name, prior_model in self.direct_prior_model_tuples:
            setattr(new_model, name, prior_model.gaussian_prior_model_for_arguments(arguments))

        return new_model
Create a new instance of model mapper with a set of Gaussian priors based on tuples provided by a previous \
        nonlinear search.

        Parameters
        ----------
        arguments: [(float, float)]
            Tuples providing the mean and sigma of gaussians

        Returns
        -------
        new_model: ModelMapper
            A new model mapper populated with Gaussian priors