This document describes several variations of Buffon's needle experiment to approximate the value of π through random simulations. It provides R code implementations of dropping virtual needles on a plane with parallel lines and counting how many cross a line. The probability of a needle crossing can be related to π. It also describes using a bivariate normal distribution to approximate e. The document discusses the theoretical probabilities and derivations, provides R functions to simulate the experiments, and analyzes the results to estimate confidence intervals for π and e.

1. Buffon’s needle(s)
1. Write an R function called dart(n,t,l) that (a) throws uniformly n points (x, y) inside the
square [−1, 1] × [−1, 1] and (b) returns the proportion of points within the unit circle,
x2 + y 2 ≤ 1. Use dart to derive an approximation of the constant π by this experiment
and plot the evolutions of the approximation when the number n of dots grows from 10
to 105 .
Buffon’s needle is one of the earliest instances of using simulation to approximate an integral.
It uses random throws of needles of length over a wooden floor made of planks of width t ≥
and derives the constant π from the proportion of needles crossing a plank separation (or line).
This model assumes all planks are identical, horizontal, and parallel.
2. If we assume that the centre of the needle falls between two lines with uniform probability,
show (or use the fact) that the probability that this centre stands within a distance /2
of the closest line is /t.
3. Conditioning on the event that the centre of the needle is within /2 of the closest line of
the plank and assuming that the needle is oriented uniformly over (0, π) with respect to
the axis of the line, show (or use the fact) that the probability that the needle crosses the
closest line is 2/π.
4. Conclude that the overall probability that the needle crosses the closest line is 2 /tπ.
5. If A and B are the coordinates of the extremities of a random needle, show that it crosses
a line if and only if the boolean
(trunc(A[,2]) != trunc(B[,2]))
is true.
6. Write an R code buffon(l,t,n) that approximate this probability by throwing n centres
uniformly at random over the [−10t, 10t] × [−10t, 10t] unit square (which contains 19
lines), then generating the angles of the n needles of length l wrt the line uniformly over
(0, π) and counting how many needles cross a line. Run this code for n = 105 needles and
check whether or not π belongs to the Monte Carlo confidence interval.
7. Expand your R code buffon(l,t,n) to plot the plank separations and the random needles
on top of those.
8. Implement the following R code :
BuffonsNeedle <- function(n=100, sd=2){
#(C.) Allan Roberts, 2013.
X <- rnorm(n,5,sd)
Y <- rnorm(n,5,sd)
Angle <- runif(n,0,2*pi)
X2 <- X + cos(Angle)
Y2 <- Y + sin(Angle)
CrossesLine <- ( floor(Y) != floor(Y2) )
p <- sum(CrossesLine)/n
return(c(p,2/p))
}
(a) Explain why (or accept the fact that) this R code is only an approximation to Buffon’s
problem.
1

2. (b) Identify from the R code what the corresponding values of
and t are.
(c) For n = 105 , give a confidence interval on π and check whether π belongs to this
interval.
(d) Repeat the above verification with sd=0.2, then with sd=.002.
0.1
More Buffon’s needles
1. If X, Y are normal N (0, 1) rv’s, show (or use the fact) that the probability that X 2 +Y 2 ≤ 1
is equal to
√
P(X 2 + Y 2 ≤ 1) = 1 − 1/ e
2. Write an R function called hitin(n) that (a) throws n points (x, y) from the standard
bivariate normal distribution N2 (0, I) and (b) returns the proportion of points within the
unit circle x2 + y 2 ≤ 1. Use hitin to derive an approximation of the constant e by this
experiment and plot the evolution of the approximation when the number n of dots grows
√
from 10 to 106 . Provide a confidence interval that has a 95% probability to contain 1/ e.
3. We now consider Buffon’s needle experiment, where metal needles of length are thrown
upon a wooden floor made of planks of width t ≥ and where the number of times the
needles cross a plank separation (or line) is counted. The resulting proportion leads to an
approximation of π. (This model assumes all planks are identical, parallel, and horizontal.)
(a) We assume that the centre of the needle falls uniformly over a large room, meaning
that the second component is uniform over [−D × t, D × t], where 1
D. And that
the orientation of the needle is uniform over (0, π). In that case, show (or use the
fact) that the probability of crossing a line is line is 2 /tπ.
(b) If A and B are the coordinates of the extremities of a random needle, show that it
crosses a line if and only if the boolean
(trunc(A[,2]) != trunc(B[,2]))
takes the value TRUE.
(c) Write an R code buffon(l,t,n,D) that approximate this probability by throwing n
centres uniformly at random over the [−D × t, D × t] × [−D × t, D × t] unit square
(which contains 2D1 lines), then generating the angles of the n needles wrt the line
uniformly over (0, π) and counting how many needles cross a line.
(d) Study the evolution of the approximation of π as n = 104 and as D decreases from
D = 100 to D = 3 by plotting several independent approximations for each value of
D you consider.
(e) Expand your R code buffon(l,t,n,D) to plot the lines and the random needles.
4. Implement the following R code :
BuffonsNeedle <- function(N=1000,D=50,L=.25){
# warning: L is the half-length
numbhits <- function(A,B){
sum(abs(trunc(A[,2])-trunc(B[,2]))>0)}
O <- runif(N,min=0,max=pi/2) #angle
U <- L+runif(N)*(D*sqrt(1+apply(cbind(sin(O)^2,cos(O)^2),1,min))-2*L)
C <- cbind(U*cos(O),U*sin(O)) # centre
2

3. A <- C+L*cbind(cos(O),sin(O)) # endpoint A
B <- C-L*cbind(cos(O),sin(O)) # endpoint B
return(2*2*L*N/numbhits(A,B))
}
(a) Explain why this R code is only approximately solving Buffon’s original problem and
identify from the R code what the corresponding values of and t are.
(b) Study how the corresponding approximation of π converges with N .
(c) Repeat the study with D=100, then with D=10.
3