demo/Chapter.6.7.R

In bayesball/LearnBayes: Learning Bayesian Inference

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# Section 6.7 Learning about a Normal Population from Grouped Data
##################################################################

library(LearnBayes)

 d=list(int.lo=c(-Inf,seq(66,74,by=2)),
        int.hi=c(seq(66,74,by=2), Inf),
        f=c(14,30,49,70,33,15))

y=c(rep(65,14),rep(67,30),rep(69,49),rep(71,70),rep(73,33),
  rep(75,15))
 mean(y)

 log(sd(y))

 start=c(70,1)
 fit=laplace(groupeddatapost,start,d)
 fit

 modal.sds=sqrt(diag(fit$var))

 proposal=list(var=fit$var,scale=2)
 fit2=rwmetrop(groupeddatapost,proposal,start,10000,d)

 fit2$accept

 post.means=apply(fit2$par,2,mean)
 post.sds=apply(fit2$par,2,sd)

 cbind(c(fit$mode),modal.sds)

 cbind(post.means,post.sds)

 mycontour(groupeddatapost,c(69,71,.6,1.3),d,
    xlab="mu",ylab="log sigma")
 points(fit2$par[5001:10000,1],fit2$par[5001:10000,2])