return new GaussianCurveFitter(initialGuess,
                                       newMaxIter);
    }

    /** {@inheritDoc} */
    @Override
    protected LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> observations) {

        // Prepare least-squares problem.
        final int len = observations.size();
        final double[] target  = new double[len];
        final double[] weights = new double[len];

        int i = 0;
        for (WeightedObservedPoint obs : observations) {
            target[i]  = obs.getY();
            weights[i] = obs.getWeight();
            ++i;
        }

        final AbstractCurveFitter.TheoreticalValuesFunction model =
                new AbstractCurveFitter.TheoreticalValuesFunction(FUNCTION, observations);

        final double[] startPoint = initialGuess != null ?
            initialGuess :
            // Compute estimation.
            new ParameterGuesser(observations).guess();

        // Return a new least squares problem set up to fit a Gaussian curve to the
        // observed points.
        return new LeastSquaresBuilder().
                maxEvaluations(Integer.MAX_VALUE).
                maxIterations(maxIter).
                start(startPoint).
                target(target).
                weight(new DiagonalMatrix(weights)).
                model(model.getModelFunction(), model.getModelFunctionJacobian()).
                build();

    }

    /**
     * Guesses the parameters {@code norm}, {@code mean}, and {@code sigma}
     * of a {@link org.hipparchus.analysis.function.Gaussian.Parametric}
     * based on the specified observed points.
     */
    public static class ParameterGuesser {
        /** Normalization factor. */
        private final double norm;