
data {
  int<lower=0> n_i;
  int<lower=0> n_k;
  matrix[n_i, n_k] x;
  int y[n_i,n_k];
}

parameters {
  vector[n_i] delta;
  real<lower=0> sigma_delta;
  matrix[n_i,n_k] eps;
  vector[n_k] alpha;
  vector<lower=0>[n_k] tau_N;
  vector[n_k] rho;
  real mu_rho;
  real<lower=0> sigma_rho;
}

transformed parameters {
  vector[n_k] mu;
  vector[n_k] tau;
  matrix[n_i,n_k] bias;
  matrix[n_i,n_k] prev_mean = exp(rep_matrix(rho, n_i)' + x .* rep_matrix(alpha, n_i)' + rep_matrix(sigma_delta * delta, n_k));
  
  mu = log(1.0 ./ sqrt(1.0 + square(tau_N)));
  tau = sqrt(log(1.0 + square(tau_N)));
  for(k in 1:n_k){
	bias[,k] = exp(mu[k] + tau[k] * eps[,k]);
  }
 }

model {
  delta ~ std_normal();
  sigma_delta ~ cauchy(0, 2.5); // Half-cauchy suggested in stan-users-guide
  tau_N ~ cauchy(0, 2.5); // Half-cauchy suggested in stan-users-guide
  to_vector(eps) ~ std_normal();
  alpha ~ normal(0, 10);
  sigma_rho ~ cauchy(0, 2.5); // Half-cauchy suggested in stan-users-guide
  mu_rho ~ normal(0, 10);
  rho ~ normal(mu_rho, sigma_rho);
  
  for(k in 1:n_k){
    y[,k] ~ poisson(prev_mean[,k] .* bias[,k]);
  }
}


