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The simulation software package ususally offers two methods of generating samples from probability distributions: Monte Carlo sampling, and Latin Hypercube sampling. The latter is  the one we recommend you use for normal models. Monte Carlo sampling is mostly used when we are trying to replicate the pattern of randomly observed data. There are also a number of other sampling methods available in simulation you may wish to investigate. All the methods for generating random samples rely on a Seed value, and it is sometimes useful to control that value to check the quality of your results.


Sometimes you may find that a specific simulation software package does not If your simulation software doesn't have a distribution you would particularly like available to use, visit this section for methods to generate your own distributions. In ModelAssist we introduce a number of such distributions, which of course we need to somehow generate values for. This section introduces you to the techniques you will need. 


ModelAssist provides you with topics on how to determine how many iterations to run a Monte Carlo model for. It's a good question, which is  frequently asked in courses we give.