data { // everything to the right of the | in Bayes' Rule
  real<lower = 0> a0; // expectation for normal prior on VE
  real<lower = 0> b0; // standard deviation for normal prior on VE
  int<lower = 0>  n; // number of people with Covid in the RCT
  int<lower = 0, upper = n> y; // number of them who were vaccinated
}
parameters { // everything to the left of the | in Bayes' Rule
  real<upper = 1> theta; // = 1 - Pr(covid | vax) / Pr(covid | placebo)
}
model { // target has been initialized to zero basically
  target += beta_lpdf(theta | a0, b0);      // prior log-kernel
  target += binomial_lpmf(y | n, theta); // log-likelihood
} // model block is like a function that returns target
generated quantities { 
  real<lower = 0, upper = 1> VE = (1 - 2 * theta) / (1 - theta);
}