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### lab5

Course: MATH 459, Fall 2008
School: Washington University...
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Word Count: 152

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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)) start =...

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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)) start = c( mean(y), log(sd(y))) 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) post.means=apply(fit2\$par,2,mean) fit2\$accept 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]) # Try a bad choice of proposal distribution start.bad = array(c(65,1)...

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