def METHOD_NAME():
    """This test will fit a design matrix containing the column vectors
    a=[1, 1] and b=[1, 2] to a light curve with flux=[5, 10].
    The best coefficients for this problem are [0, 5] because 0*a + 5*b == flux,
    however we will verify that changing the priors will yield different
    solutions.
    """
    lc1 = LightCurve(flux=[5, 10])
    lc2 = LightCurve(flux=[5, 10], flux_err=[1, 1])
    design_matrix = DesignMatrix(pd.DataFrame({"a": [1, 1], "b": [1, 2]}))
    for dm in [design_matrix, design_matrix.to_sparse()]:
        for lc in [lc1, lc2]:
            rc = RegressionCorrector(lc)
            # No prior
            rc.correct(dm)
            assert_almost_equal(rc.coefficients, [0, 5])
            # Strict prior centered on correct solution
            dm.prior_mu = [0, 5]
            dm.prior_sigma = [1e-6, 1e-6]
            rc.correct(dm)
            assert_almost_equal(rc.coefficients, [0, 5])
            # Strict prior centered on incorrect solution
            dm.prior_mu = [99, 99]
            dm.prior_sigma = [1e-6, 1e-6]
            rc.correct(dm)
            assert_almost_equal(rc.coefficients, [99, 99])
            # Wide prior centered on incorrect solution
            dm.prior_mu = [9, 9]
            dm.prior_sigma = [1e6, 1e6]
            rc.correct(dm)
            assert_almost_equal(rc.coefficients, [0, 5])