程序代写 CS代考

#generate data set.seed(123) n=100; y=rnorm(n,2,1) Copyright By PowCoder代写 加微信 powcoder #unnormalised posterior unposterior=function(theta,data){ likelihood=prod(dnorm(data,theta,1)) prior=dnorm(theta,0,10) unpost=likelihood*prior return(unpost) n.samples=10000 theta.g=rnorm(n.samples,mean(y),sd(y)) w=numeric(n.samples) for(s in 1:n.samples){ w[s]=unposterior(theta=theta.g[s],data=y)/dnorm(theta.g[s],mean(y),sd(y)) q=w/sum(w) theta.f=sample(theta.g,n.samples,replace=TRUE,prob=q) #true posterior distribution for theta posterior=function(theta,data){ n=length(data) thetam=((0/100)+(n*mean(data)/1))/((1/100)+n/1) thetasd=sqrt(1/((1/100)+n/1)) post=dnorm(theta,mean=thetam,sd=thetasd) return(post) thetagrid=seq(-1,5,len=m) pdf(“sir.pdf”) hist(theta.f,freq=F,breaks=20,xlab=expression(theta),ylab=”Density”,main=””) lines(thetagrid,posterior(thetagrid,y),type=”l”) 程序代写 CS代考 加微信: powcoder QQ: 1823890830 Email: powcoder@163.com

#First fit the standard frequentist linear statistical model data(mtcars) help(mtcars) #creating a new dataframe with only mpg,drat,wt, and qsec as variables Copyright By PowCoder代写 加微信 powcoder vars=names(mtcars)%in%c(“cyl”, “disp”, “hp”, “vs”,”am”,”gear”,”carb”) #variables to exclude mtcars1=mtcars[!vars] fit=lm(mpg~drat+wt+qsec,data=mtcars1) summary(fit) #Now fit the Bayesian version of the model using JAGS require(rjags) #covariates and response y=mtcars1$mpg; drat=mtcars1$drat; wt=mtcars1$wt; qsec=mtcars1$qsec n=nrow(mtcars1)

Workshop 1: Conjugate Bayesian inference in R 1. Analysis of binomial data: drug. Consider the example from lecture 1 where a new drug is be- ing considered for relief of chronic pain, with the success rate ¦È being the proportion of patients experiencing pain relief. In the past, drugs of this type have shown variable

AGTA Tutorial 8 Please attempt the question before your tutorial. 1. Suppose you are running a VCG-based simultaneous multi-item auc- tion, where three related items A, B, and C, are being auctioned simultaneously, and each bidder can bid on any possible subset of the items. Suppose there are two bidders, X and Y , and