Completely agree with Matija. MC model is actually used for verification of various approximation based models. So, it's the gold standard one can say. Educate yourself  on the topic Read the books I suggested earlier. Otherwise, lack of understanding will seriously impact your progress.  

Agree with all of the above.  I would add that practically speaking, you should model something "simple" with your MC to see if you can:

1) Get the MC to correspond to an analytical solution that you can compute yourself in closed form or find in the literature.

2) Simulate a reasonably simple problem which is posted in the literature and see what your results look like in comparison.  If you reproduce the right SHAPE of a distribution then you are on the right track, even if the normalization (overall factor) of your distribution does not correspond.  If the latter is the case, then the normalization factor between the two MAY be your calibration.  It depends on subsequent analysis to ensure that this is correct.  At least to "first order" as they say.  You will still need to understand why the normalizations are different!

Good Luck!

Andrew

Debera aplicar la guia para el calculo de las incertidumbre de las mediciones, metodo de montecarlo desde luego debes establecer cual es tu mesurando, que debe arrojarte cuales son las magnitudes influyentes