diff --git a/trap/prediction_server.py b/trap/prediction_server.py
index 275a451..b89bd20 100644
--- a/trap/prediction_server.py
+++ b/trap/prediction_server.py
@@ -326,13 +326,18 @@ class PredictionServer:
             start = time.time()
             with warnings.catch_warnings():
                 warnings.simplefilter('ignore') # prevent deluge of UserWarning from torch's rrn.py
+
+                # in the OnlineMultimodalGenerativeCVAE (see trajectron.model.online_mgcvae.py) each node's distribution
+                # is put stored in self.latent.p_dist by OnlineMultimodalGenerativeCVAE.p_z_x(). Type: torch.distributions.OneHotCategorical
+                # Later sampling in discrete_latent.py: DiscreteLatent.sample_p()
                 dists, preds = trajectron.incremental_forward(input_dict,
                                                             maps,
                                                             prediction_horizon=self.config.prediction_horizon, # TODO: make variable
                                                             num_samples=self.config.num_samples, # TODO: make variable
-                                                            full_dist=self.config.full_dist,
-                                                            gmm_mode=self.config.gmm_mode,
-                                                            z_mode=self.config.z_mode)
+                                                            full_dist=self.config.full_dist, # "The model’s full sampled output, where z and y are sampled sequentially"
+                                                            gmm_mode=self.config.gmm_mode, # "If True: The mode of the Gaussian Mixture Model (GMM) is sampled (see trajectron.model.mgcvae.py)"
+                                                            z_mode=self.config.z_mode # "Predictions from the model’s most-likely high-level latent behavior mode" (see trajecton.models.components.discrete_latent:sample_p(most_likely_z=z_mode))
+                                                            )
             end = time.time()
             logger.debug("took %.2f s (= %.2f Hz) w/ %d nodes and %d edges" % (end - start,
                                                                             1. / (end - start), len(trajectron.nodes),