Download 14 Principles of Good Practice for Using Monte

14 Principles of Good Practice for Using Monte-Carlo Techniques
After Burmaster and Anderson (1994)
1.
Show all formulae used and the appropriate units.
2.
Calculate point estimates using mean values and “conservative” values.
3.
Perform a sensitivity analysis to find dominant values and distributions.
4.
Only do a probabilistic risk assessment (PRA) if conservative point estimates are significant.
5.
Provide detailed input information in a table form and provide all references.
6.
Show input variability and uncertainty in terms of COV, 90th and 10th percentiles, or range.
7.
Use real data whenever possible. Estimated or guessed data must be justified and explained.
8.
Discuss methods of fitting the distributions and goodness of fit.
9.
Discuss correlations among the input variables. Strong correlation is || ≥0.6.
10.
Provide detailed output statistics in numerical and graphical form. These should include all key
percentiles (1%, 5%, 10%, 80%, 90%, 95%, 99%), and summary statistics (mean, median, Q1,
Q3, IQR, standard deviation, skewness).
11.
Perform a probabilistic sensitivity analysis for key inputs in such a way as to clearly distinguish
between variability and uncertainty.
12.
Do enough runs to ensure a stable output.
13.
Use and document a sufficiently random random number generator.
14.
Explicitly identify limitations, bias, and further study needs.
Remember that you need a lot of information and make a lot of assumptions when using Monte-Carlo
simulations. Monte-Carlo methods cannot do everything. See separate handout on what MonteCarlo cannot do.
L. M. Lye (2003)