data {
  int<lower=0> n_dogs;
  int<lower=0> n_trials;
  int<lower=0,upper=1> y[n_dogs,n_trials];
}
parameters {
  vector[3] beta;
}
transformed parameters {
  matrix[n_dogs,n_trials] n_avoid;
  matrix[n_dogs,n_trials] n_shock;
  matrix[n_dogs,n_trials] p;

  for (j in 1:n_dogs) {
    n_avoid[j,1] <- 0;
    n_shock[j,1] <- 0;
    for (t in 2:n_trials) {
      n_avoid[j,t] <- n_avoid[j,t-1] + 1 - y[j,t-1];
      n_shock[j,t] <- n_shock[j,t-1] + y[j,t-1];
    }
    for (t in 1:n_trials)
      p[j,t] <- beta[1] + beta[2] * n_avoid[j,t] + beta[3] * n_shock[j,t];
  }
}
model {
  beta ~ normal(0, 100);
  for (i in 1:n_dogs) {
    for (j in 1:n_trials)
      y[i,j] ~ bernoulli_logit(p[i,j]);
  }
}