![]() Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis, making Monte Carlo simulation far superior to common “best guess” or “best/worst/most likely” analyses. Monte Carlo simulation software builds a spreadsheet model that lets you evaluate your plan numerically, allowing you to change the numbers, ask ‘what if’ and see the results.īy using probability distributions for uncertain inputs, you can represent the different possible values for these variables, along with their likelihood of occurrence. This data on possible results enables you to calculate the probabilities of different outcomes in your forecasts, as well as perform a wide range of additional analyses. ![]() The result of a Monte Carlo simulation is a range – or distribution – of possible outcome values. Depending upon the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. ![]() It then calculates results over and over, each time using a different set of random values from the input probability distributions. Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values-called a probability distribution-for any factor that has inherent uncertainty. ![]()
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