Buffon versus Bertrand in R

Following my earlier post on Buffon’s needle and Bertrand’s paradox, above are four outcomes corresponding to four different generations (among many) of the needle locations. The upper right-hand side makes a difference in the number of hits (out of 1000). The R code corresponding to this generation was made in the métro, so do not expect subtlety:

#Several ways of throwing a needle at "random"
L=0.35 #half-length of the needle
D=20  #length of the room
N=10^3

numbhits=function(A,B){
 sum(abs(trunc(A[,2])-trunc(B[,2]))>0)}

par(mfrow=c(2,2),mar=c(1,1,1,1))
#version #1: uniform location of the centre
U=runif(N,min=L,max=D-L) #centre
O=runif(N,min=0,max=pi) #angle
C=cbind(runif(N,0,D),U)
A=C+L*cbind(cos(O),sin(O))
B=C-L*cbind(cos(O),sin(O))
plot(C,type="n",axes=F,xlim=c(0,D),ylim=c(0,D))
for (t in 1:N)
 lines(c(A[t,1],B[t,1]),c(A[t,2],B[t,2]),col="steelblue")
for (i in 1:(D-1))
 abline(h=i,lty=2,col="sienna")
title(main=paste(numbhits(A,B),"hits",sep=" "))

#version #2: uniform location of one endpoint
U=runif(N,min=2*L,max=D-(2*L)) #centre
O=runif(N,min=0,max=2*pi) #angle
A=cbind(runif(N,0,D),U)
B=A+2*L*cbind(cos(O),sin(O))
plot(A,type="n",axes=F,xlim=c(0,D),ylim=c(0,D))
for (t in 1:N)
 lines(c(A[t,1],B[t,1]),c(A[t,2],B[t,2]),col="steelblue")
for (i in 1:(D-1))
 abline(h=i,lty=2,col="sienna")
title(main=paste(numbhits(A,B),"hits",sep=" "))

#version #3: random ray from corner
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) #centre
C=cbind(U*cos(O),U*sin(O))
A=C+L*cbind(cos(O),sin(O))
B=C-L*cbind(cos(O),sin(O))
plot(C,type="n",axes=F,xlim=c(0,D),ylim=c(0,D))
for (t in 1:N)
 lines(c(A[t,1],B[t,1]),c(A[t,2],B[t,2]),col="steelblue")
for (i in 1:(D-1))
 abline(h=i,lty=2,col="sienna")
title(main=paste(numbhits(A,B),"hits",sep=" "))

#version #4: random ray from corner
O=runif(N,min=0,max=pi/2) #angle
U=runif(N)*(D*sqrt(1+apply(cbind(sin(O)^2,cos(O)^2),1,min))-2*L) #centre
A=cbind(U*cos(O),U*sin(O))
B=A+2*L*cbind(cos(O),sin(O))
plot(A,type="n",axes=F,xlim=c(0,D),ylim=c(0,D))
for (t in 1:N)
 lines(c(A[t,1],B[t,1]),c(A[t,2],B[t,2]),col="steelblue")
for (i in 1:(D-1))
 abline(h=i,lty=2,col="sienna")
title(main=paste(numbhits(A,B),"hits",sep=" "))

When running the R code for 10⁶ iterations, the approximations to π based on the standard formula are given by

[1] 3.194072
[1] 3.140457
[1] 3.213596
[1] 3.210177

Tags: statistics, R, Buffon's Needle, Bertrand's paradox, sigma-algebra