# Convergence loop of the bayesian ridge regression
        for iter_ in range(self.n_iter):
            # Compute mu and sigma
            # sigma_ = lambda_ / alpha_ * np.eye(n_features) + np.dot(X.T, X)
            # coef_ = sigma_^-1 * XT * y
            ** lambda_ and alpha_ are used to calculate coef_**
            coef_ = ...
            ...

            # Update alpha and lambda
            ...
            **lambda_ and alpha_ are modified**
            lambda_ = ((gamma_ + 2 * lambda_1) /
                       (np.sum(coef_ ** 2) + 2 * lambda_2))
            alpha_ = ((n_samples - gamma_ + 2 * alpha_1) /
                      (rmse_ + 2 * alpha_2))

            # Compute the objective function
            ...

            # Check for convergence
            ...

        self.alpha_ = alpha_
        self.lambda_ = lambda_
        self.coef_ = coef_