# How to built Gibbs sampler of Mixture Bayesian regression in R?

I am working on a Gibbs sampler of three parameters and we know the full conditional distribution of three parameters.

Note: This is just a comment but to quote a long code, I put it here.

for (ite in 2:NSim){
#Full conditional for pi

pi[ite]=rbeta(1, sum(delta[ite-1,])+0.5, sum(1-delta[ite-1,])+0.5)

#Full conditional for delta
for(j in 1:4){
p1=pi[ite]*exp(-beta[ite-1,j]^2/(20))
p0=((1-pi[ite])*10^3)*exp(-500*beta[ite-1,j]^2)
cat('\n',ite,j,(p1/(p0+p1)))
delta[ite,j]=rbinom(1, 1,prob=(p1/(p0+p1)))
}


The error says that maybe $$p1$$ and $$p0$$ are NAs. My experience when checking this type of error is that. Instead of for loop, just give $$ite=2$$ and $$j=1$$. Compute p1 and p0 as your formulas. Check carefully if they are NAs or not. If the codes are symmetric with regard to $$iter$$ and $$j$$, and if you can correct the error for this case, it will pass this.