The code that one needs to write in order to perform this operation is extremely simple. Code to Calculate the Value of Using Random Numbers In order to see how the value becomes closer to the true value of π with an increase in the number of points we take into consideration, we must write a few lines of code. The entire basis of this method hinges on one of the most fundamental mathematical theorems that we all learn as children: the formula for the area of a circle.Īlthough this is not the exact value of π, for a set of just 10 points, it is remarkably close. In this article, we will analyze the use of a simple Monte Carlo Simulation to calculate the value of π (pi). Monte Carlo Simulations find their use in various fields, like Finance, Telecommunications and much more, however, we will not get into that in this article. The more random data we use, the closer we get to the true value. With random sampling, the data we are using has no bias so we can simulate situations where the probabilities we obtain are close to the actual value. This is done by using repeated random sampling to our advantage. Monte Carlo Simulations can be thought of as computational algorithms that enable us to model probabilities that are difficult to calculate. I want to start by explaining what a Monte Carlo Simulation is. How Do We Calculate the Value of π Using Random Numbers? In this article, I shall first aim to explain how the value of π can be calculated using random numbers and I shall also provide a short Python code snippet that shows how this can be done and visualized by writing a simple program in just a few lines. It would, then, seem absurd that a number that is seemingly so specific and universal can be estimated using just random numbers. Due to its fundamental involvement with circles, π has found applications in virtually every industry, ranging from sports to designing power lines and everything in between. We interact with the number π (‘pi’) everyday.
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