diff --git a/BPA/Ch.10/js_ms.stan b/BPA/Ch.10/js_ms.stan
index a5963ecc..86a9f902 100644
--- a/BPA/Ch.10/js_ms.stan
+++ b/BPA/Ch.10/js_ms.stan
@@ -121,6 +121,8 @@ generated quantities {
   array[M, n_occasions] int<lower=1, upper=3> z; // Latent state
   
   // Generate z[]
+  // This code generates the posterior predictive distribution
+  // unconditioned on the observation
   for (i in 1 : M) {
     z[i, 1] = 1;
     for (t in 2 : n_occasions) {
diff --git a/BPA/Ch.10/js_rest_occ.stan b/BPA/Ch.10/js_rest_occ.stan
index 1cacf650..f5d624c2 100644
--- a/BPA/Ch.10/js_rest_occ.stan
+++ b/BPA/Ch.10/js_rest_occ.stan
@@ -212,6 +212,8 @@ generated quantities {
   array[M, n_occasions] int z; // Latent state
   
   // Generate z[]
+  // This code generates the posterior predictive distribution
+  // unconditioned on the observation
   for (i in 1 : M) {
     int q = 1;
     real mu2;
diff --git a/BPA/Ch.10/js_super.stan b/BPA/Ch.10/js_super.stan
index 7b731058..013d538f 100644
--- a/BPA/Ch.10/js_super.stan
+++ b/BPA/Ch.10/js_super.stan
@@ -215,6 +215,8 @@ generated quantities {
   array[M, n_occasions] int<lower=0, upper=1> z; // Deflated latent state
   
   // Generate w[] and z[]
+  // This code generates the posterior predictive distribution
+  // unconditioned on the observation
   for (i in 1 : M) {
     int q = 1;
     if (bernoulli_rng(psi)) {
diff --git a/BPA/Ch.10/js_super_indran.stan b/BPA/Ch.10/js_super_indran.stan
index 0af7f9eb..2ac5b1b5 100644
--- a/BPA/Ch.10/js_super_indran.stan
+++ b/BPA/Ch.10/js_super_indran.stan
@@ -226,6 +226,8 @@ generated quantities {
   sigma2 = square(sigma);
   
   // Generate w[] and z[]
+  // This code generates the posterior predictive distribution
+  // unconditioned on the observation
   for (i in 1 : M) {
     int q = 1;
     if (bernoulli_rng(psi)) {
diff --git a/BPA/Ch.10/js_tempran.stan b/BPA/Ch.10/js_tempran.stan
index 5c39f141..07abf3d4 100644
--- a/BPA/Ch.10/js_tempran.stan
+++ b/BPA/Ch.10/js_tempran.stan
@@ -223,6 +223,8 @@ generated quantities {
   array[M, n_occasions] int z; // Latent state
   
   // Generate z[]
+  // This code generates the posterior predictive distribution
+  // unconditioned on the observation
   for (i in 1 : M) {
     int q = 1;
     real mu2;