#source("c:/Work07/ShortCourse07/Programs/Truncated.txt",print.eval=TRUE)#
#This gives an accept-reject algorithm for a Truncated Normal#
par(mfrow=c(1,2))
a<-3.5			 		#Truncation Point
n<-100					#Sample size
#---------Generate a Sample Naively-------------------------
#---------Take n=100, a=1 then a=3.5--------------------------
X<-array(0,c(1,n))
for(i in 1:n)
{
z<-rnorm(1);while(z<a)z<-rnorm(1)
X[i]<-z
}
hist(X,col="grey",main="Naive")
#----------Use Accept Reject--------------------------------
nsim<-5000
u<-runif(nsim)
X<-rexp(nsim,a)+a
test<-a*exp(-.5*(X-a)^2)
X<-X*(u<test)
X<-X[X!=0]
hist(X,col="grey",main="AR")
length(X)/nsim