# Bayes decision boundary of Figure 2.5 in Elements of Statistical Learning

When I read "Elements of Statistical Learning", I met some difficulty in calculating the Bayes decision boundary of Figure 2.5. In the package ElemStatLearn, it already calculated the probability at each point and used contours to draw the boundary. Can any one tell me how to calculate the probability?

In a traditional Bayes decision problem, the mixture distributions are usually normal distributions, but in this example, it uses two steps to generate the samples, so I have some difficulty in calculating the distribution.

I asked the authors this question, and apparently they no longer are in possession of the code that created the data. So there is no real way to reconstruct the Bayes rule for this particular data set. Otherwise, it would be based on the ratio of the densities that would have been known for the Gaussian mixture distributions that the authors used to create the two classes.

I was struggling with the same question; created some code that might be helpful. Didnt manage to create a good function to add contour lines, but per below should show the principle.

set.seed(1)
library(MASS)

#create original 10 center points/means for each class
I.mat=diag(2)
mu1=c(1,0);mu2=c(0,1)
mv.dist1=mvrnorm(n = 10, mu1, I.mat)
mv.dist2=mvrnorm(n = 10, mu2, I.mat)

values1=NULL;values2=NULL

#create 100 observations for each class, after random sampling of a center point, based on an assumed bivariate probability distribution around each center point
for(i in 1:10){
mv.values1=mv.dist1[sample(nrow(mv.dist1),size=1,replace=TRUE),]
sub.mv.dist1=mvrnorm(n = 10, mv.values1, I.mat/5)
values1=rbind(sub.mv.dist1,values1)
}
values1

#similar as per above, for second class
for(i in 1:10){
mv.values2=mv.dist2[sample(nrow(mv.dist2),size=1,replace=TRUE),]
sub.mv.dist2=mvrnorm(n = 10, mv.values2, I.mat/5)
values2=rbind(sub.mv.dist2,values2)
}
values2

#did not find probability function in MASS, so used mnormt
library(mnormt)

#create grid of points
grid.vector1=seq(-2,2,0.1)
grid.vector2=seq(-2,2,0.1)
length(grid.vector1)*length(grid.vector2)
grid=expand.grid(grid.vector1,grid.vector2)

#calculate density for each point on grid for each of the 100 multivariates distributions
prob.1=matrix(0:0,nrow=1681,ncol=10) #initialize grid
for (i in 1:1681){
for (j in 1:10){
prob.1[i,j]=dmnorm(grid[i,], mv.dist1[j,], I.mat/5)
}
}
prob.1
prob1.max=apply(prob.1,1,max)

#second class - as per above
prob.2=matrix(0:0,nrow=1681,ncol=10) #initialize grid
for (i in 1:1681){
for (j in 1:10){
prob.2[i,j]=dmnorm(grid[i,], mv.dist2[j,], I.mat/5)
}
}
prob.2
prob2.max=apply(prob.2,1,max)

#bind
prob.total=cbind(prob1.max,prob2.max)
class=rep(1,1681)
class[prob1.max<prob2.max]=2
cbind(prob.total,class)

#plot points
plot(grid[,1], grid[,2],pch=".", cex=3,col=ifelse(class==1, "coral", "cornflowerblue"))

points(values1,col="coral")
points(values2,col="cornflowerblue")

#check - original centers
# points(mv.dist1,col="coral")
# points(mv.dist2,col="cornflowerblue")

# bayesian decision boundary; first get conditional probability for class 1
prob.bayes <- matrix(prob1.max/(prob1.max + prob2.max), length(grid.vector1), length(grid.vector2))
contour(grid.vector1, grid.vector2, prob.bayes, levels = 0.5, labels = "", lwd = 2, add = TRUE)

• In the above code one should use mean instead of max, since each pdf is weighted by probability of picking corresponding distribution center, which is 1/10.
– ATol
Dec 16 '18 at 19:27
• what is mv ? mv.dist etc. Apr 16 '19 at 14:54