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Calculating pi with Monte Carlo simulation
I came across Monte Carlo sampling in a class on Bayesian statistics, where a Markov Chain Monte Carlo (MCMC) sampler was used to approximate probability distributions that were otherwise hard to calculate due to nasty integrals. This posts illustrates the basic idea of Monte Carlo sampling, by using it to approximate the number \(\pi\). The basic procedure is as follows: Take a circle with radius \(r\) The area of the circle is \(\pi r^2\) Draw a containing square which then has area …
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