// In this normal linear regression we asses
// the influence of the prior for the slope
// parameter

data {
  int<lower=0> n;
  vector[n] y;
  vector[n] x;
  vector[20] tauprior;
}
parameters {
  vector[20] beta0;
  vector[20] beta1;
  real<lower=0> sigma[20];
}
model {
  //priors
  beta0 ~ normal(0,10);
  sigma~uniform(0,50);
  // likelihood
  for(k in 1:20){
    beta1[k] ~ normal(0,tauprior[k]);
    y ~ normal(beta0[k] + beta1[k] * x, sigma[k]);
  }
}