model
{
  # priors
  beta0 ~ dunif(-1000000,1000000)
  beta1 ~ dunif(0,20000)
  tau ~ dunif(0,1)
 
 
  # likelihood
  for(i in 1:N)
  {
    Y[i] ~ dnorm(mu[i], tau)
    mu[i] <- beta0 + beta1*X[i]
  }
}