Here are the R codes of the R labs organised by Serena Arima in supplement of my lectures. This is quite impressive and helpful to the students, as illustrated by the first example below (using the abc software).

### Example 1: Conjugate model (Normal-Inverse Gamma)
### y1,y2,...,yn ~N(mu,sigma2)
### mu|sigma2 ~ N(0,sigma2), sigma2 ~IG(1/2,1/2)

library(abc)

### Iris data: sepal width of Iris Setosa
data(iris3)
y=iris3[,2,1]

### We want to obtain the following quantities
### "par.sim" "post.mu" "post.sigma2" "stat.obs" "stat.sim"

## STAT.OBS: stat.obs are mean and variance (log scale) of the data
mean(y)
log(var(y))
stat.obs
stat.oss=c(mean(y),log(var(y)))

### PAR.SIM: par.sim simulated values from the prior distribution
n.sim=10000
gdl=1
invsigma.sim=rchisq(n.sim,df=gdl)
sigma.sim=gdl/invsigma.sim

m=3
mu.sim=c()
for(i in 1:length(sigma.sim)){
 mu.sim=c(mu.sim,rnorm(1,mean=m,sd=sqrt(((sigma.sim[i])))))}
prior.sim=data.frame(mu.sim,sigma.sim)

### STAT.SIM: for mu and sigma simulated from the prior,
### generate data from the model y ~ N(mu,sigma^2)
stat.simulated=matrix(NA,nrow=length(mu.sim),ncol=2)

for(i in 1:length(mu.sim)){
 y.new=rnorm(length(y),mu.sim[i],sqrt(sigma.sim[i]))
 stat.simulated[i,1]=mean(y.new)
 stat.simulated[i,2]=log(var(y.new))}

### Obtain posterior distribution using ABC
post.value=abc(target=stat.oss, param=prior.sim,
sumstat=data.frame(stat.simulated),tol=0.001,method="rejection")
summary(post.value)

posterior.values=post.value$unadj.values
mu.post=posterior.values[,1]
sigma.post=posterior.values[,2]

### True values, thanks to conjugancy
post.mean.mu=(length(y)/(length(y)+1))*mean(y)
post.a.sigma=length(y)/2
post.b.sigma=0.5+0.5*sum((y-mean(y))^2)

hist(mu.post,main="Posterior distribution of mu")
abline(v=post.mean.mu,col=2,lwd=2)

hist(sigma.post,main="Posterior distribution of sigma2")
abline(v=post.b.sigma/(post.a.sigma-1),col=2,lwd=2)

I am having a great time teaching this “ABC in Roma” course, in particular because of the level of interaction and exchange with the participants (after, if not during, the classes).