Simulation

Simulating Coin Flips in C++

Occasionally I have to generate random numbers in C++, and since C++11 was published the langauge now has a massively powerful, flexible library for generating quality random numbers. The only issue is that for the life of me I cannot remember the API. This would probably not be a problem if I used it more frequently, but anyways, this blog post is a reminder for me of how to generate a uniform distribution.

A Basic Monte-Carlo Simulation in Common Lisp

Monte-Carlo simulations are algorithms that approximate numerical results by repeatedly sampling some space. Generally, the more samples you collect, the higher the accuracy of the result. In this blog post I am going to demonstrate how to use such a technique to approximate the value of the mathematical constant pi. The high level idea is as follows. We know the area of a circle can be found using the formula A = pi * r ^ 2, where r is the radius of the circle.