Happy Pi Day! I don’t encounter very much in my area of statistics, so this post might seem a little forced… In this post, I’m going to show one way to estimate .

The starting point is the integral identity

There are two ways to see why this identity is true. The first is that the integral is simply computing the area of a quarter-circle with unit radius. The second is by explicitly evaluating the integral:

If , then the integral identity means that

Hence, if we take i.i.d. draws from the uniform distribution on , it is reasonable to expect that

The code below shows how well this estimation procedure does for one run as the sample size goes from 1 to 200:

set.seed(1)
N <- 200
x <- runif(N)
samples <- 4 * sqrt(1 - x^2)
estimates <- cumsum(samples) / 1:N
plot(1:N, estimates, type = "l",
     xlab = "Sample size", ylab = "Estimate of pi",
     main = "Estimates of pi vs. sample size")
abline(h = pi, col ="red", lty = 2)

The next plot shows the relative error on the y-axis instead (the red dotted line represents 1% relative error):

rel_error <- abs(estimates - pi) / pi * 100
plot(1:N, rel_error, type = "l",
xlab = "Sample size", ylab = "Relative error (%)",
main = "Relative error vs. sample size")
abline(h = 0)
abline(h = 1, col = "red", lty = 2)