Monday, April 19, 2004 12:24:52 AM
Monte Carlo analysis is most useful in situations where you don't have a well-defined statistical distribution to work with - you're instead essentially bootstrapping your data into an empirical statistical distribution
Example - you know that factors a,b,c,...,z are all factors in a complex formula, where the terms might even be dependent on one another. Instead of trying to figure out a (possibly impossible) statistical approximation for such a curve, you program the formula to be run 10K times, using the known distribution of each factor (a, b, etc) as the inputs. Now chart the distribution of those 10,000 trials, and you have a pretty good idea of the distribution without having to have gone through some exceedingly complicated math, which might not even have been possible in the first place (not all stats distributions are nice and neat).
neye
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