model{
#Likelihood
for( i in 1 : N) {<br/>
 Disease[i] ~ dbern (thetacombine[i])<br/>
 thetacombine[i]<-theta1new[i]*theta0new[i]<br/>
 theta1new[i]<-pow((theta1[i]+0.0000000001),G[i])<br/>
 theta0new[i]<-pow((theta0[i]+0.0000000001),(G[i]-1))<br/>
}
  #Priors regressors <br/>
  theta1 ~ dbeta (a1,b1)<br/>
  theta0 ~ dbeta (a2,b2)<br/>
}

bayes_data <- list(
Disease = Disease,
G = G,
N = ntot,
a1=5,
b1=1,
a2=1,
b2=3
 )

bayes_posterior_jags <- jags.model(
 file = bayes_model_str,
 data = bayes_data,
 inits = bayes_init,
 n.chains = 3,
 n.adapt = 100
 )<br/>