How do you estimate the parameter error/uncertainties using the Nelder-Mead method in scipy.optimize.minimize using the final_simplex in the output

Question

I am trying to estimate the error/uncertainties in the estimation of the power-law fitting parameters (m, n, b) of the form y = m(x-b)^n, which I fitted to a dataset. When I use the Nelder-Mead method from scipy.optimize.minimize I get a final simplex instead of a hess_inv output. I would like to know how to make use of this output to estimate the error/uncertainties of the output parameters.

The output gotten from using the Nelder-Mead method in the scipy.optimize.minimize is as follows:

 final_simplex: (array([[ 1.55654908e+05, -4.07968007e-01,  8.77754647e-03],
       [ 1.48986925e+05, -4.02001971e-01,  8.78807346e-03],
       [ 1.45901136e+05, -3.99397548e-01,  8.77251338e-03],
       [ 1.54536008e+05, -4.08305325e-01,  8.76484118e-03]]), array([0.12402099, 0.12527722, 0.12545908, 0.12566021]))
           fun: 0.1240209890015327
       message: 'Maximum number of function evaluations has been exceeded.'
          nfev: 600
           nit: 348
        status: 1
       success: False
             x: array([ 1.55654908e+05, -4.07968007e-01,  8.77754647e-03])