By far my biggest effort in the enterprise was doing simulations. Often integrated with optimizations, and later AI applications, both knowledge based and neural approaches. Nice to see how it can be integrated with Python. We used IBM's forms, GPSS and Simscript; Sometimes specified by clients. Below an intro, more at the link
Monte Carlo Simulation and Variants with Python
Your Guide to Monte Carlo Simulation and Must Know Statistical Sampling Techniques With Python Implementation
By Tatev Karen
Monte Carlo Simulation is based on repeated random sampling. The underlying concept of Monte Carlo is to use randomness to solve problems that might be deterministic in principle. Monte Carlo simulation is one of the most popular techniques to draw inferences about a population without knowing the true underlying population distribution. This sampling technique becomes handy especially when one doesn’t have the luxury to repeatedly sample from the original population. Applications of Monte Carlo Simulation range from solving problems in theoretical physics to predicting trends in financial investments.
Monte Carlo has 3 main usages: estimate parameters or statistical measures, examine the properties of the estimates, approximate integrals. ..... '
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