Uncertainty Quantification with the Central Limit Theorem
In our last post, we approximated the mathematical constant \(\pi\) with simulated dart throwing in R. We also saw that, in general, as we increased the number of darts we threw, our estimate for \(\pi\) generally became more accurate. In this post, we'll see how can we can perform uncertainty quantification with the Central Limit Theorem for our \(\pi\) estimate. We’ll refer to the estimate that we compute for \(\pi\) as \(\hat{\pi}\).
