Monte_Carlo_Simulations | using random numbers to produce data

Monte_Carlo_Simulations is a Python library. Monte_Carlo_Simulations has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Monte_Carlo_Simulations build file is not available. You can download it from GitHub.

Monte Carlo simulations work by using random numbers. You define a problem region and run the simulation until it converges to an answer. As with any random number system, there is inherent uncertainty, but after thousands of steps, it's likely you'll get within a few sigfigs of the right answer. One example of a Monte Carlo simulation is shown in this repository (approximating_pi.py). This code works by generating random points in the x-y plane. The points are generated inside a square. Inside that square is a circle, when each point is generated, if the distance to the origin is less than or equal to the circle radius, it is said to be "inside" the circle.