#example of generating a sample from the joint posterior distribution of 
#a normal model with uninformative prior
#we simulate a sample of size 1000 from a N(0,2) 
yy_rnorm(1000,0,sqrt(4))
draw_function(w=1,NN,yy){
  if (w == 1) postscript("/home/biostats/fdominic/course/babymcmc.ps")
 par(mfrow=c(2,1)) 
  mu_rep(NA,NN)
  sigmasqr_rep(NA,NN)
  nn_length(yy) 
  ss_var(yy)
  ybar_mean(yy)
  mu[1]_0              #initial values
  sigmasqr[1]_2
  for(m in 2:NN){
    AA_(nn-1)/2
    BB_.5*(nn-1)*ss
    sigmasqr[m]_1/(rgamma(1,AA)/BB)
    mu[m]_rnorm(1,ybar, sqrt(sigmasqr[m]/nn))
  }
  hist(mu,density=-1,yaxt="n",ylab="",xlab="mu",cex=2,nclass=50)
  hist(sqrt(sigmasqr),density=-1,yaxt="n",ylab="",xlab="sigma",cex=2,nclass=50)
  
par(oma=c(0,0,0,0))
  par(mfrow=c(1,1))
  if (w == 1) dev.off()}