Updated text on prediction

Author: fonnesbeck

examples/variational_inference/bayesian_neural_network_advi.ipynb

@@ -384,7 +384,7 @@
    "source": [
     "Now that we trained our model, lets predict on the hold-out set using a posterior predictive check (PPC). We can use {func}`pymc.sample_posterior_predictive` to generate new data (in this case class predictions) from the posterior (sampled from the variational estimation).\n",
     "\n",
-    "To predict on the entire test set (and not just the minibatches) we need to create a new model object that removes the minibatches, and predicts on the whole data set. Notice that we are using our fitted `trace` to sample from the posterior predictive distribution, using the posterior estimates from the original model. The {class}`Flat` distribution is just a placeholder to make the model work; the actual values are sampled from the posterior."
+    "To predict on the entire test set (and not just the minibatches) we need to create a new model object that removes the minibatches. Notice that we are using our fitted `trace` to sample from the posterior predictive distribution, using the posterior estimates from the original model. There is no new inference here, we are just using the same model and the same posterior estimates to generate predictions. The {class}`Flat` distribution is just a placeholder to make the model work; the actual values are sampled from the posterior."
    ]
   },
   {

examples/variational_inference/bayesian_neural_network_advi.myst.md

@@ -205,7 +205,7 @@ trace = approx.sample(draws=5000)
 
 Now that we trained our model, lets predict on the hold-out set using a posterior predictive check (PPC). We can use {func}`pymc.sample_posterior_predictive` to generate new data (in this case class predictions) from the posterior (sampled from the variational estimation).
 
-To predict on the entire test set (and not just the minibatches) we need to create a new model object that removes the minibatches, and predicts on the whole data set. Notice that we are using our fitted `trace` to sample from the posterior predictive distribution, using the posterior estimates from the original model. The {class}`Flat` distribution is just a placeholder to make the model work; the actual values are sampled from the posterior.
+To predict on the entire test set (and not just the minibatches) we need to create a new model object that removes the minibatches. Notice that we are using our fitted `trace` to sample from the posterior predictive distribution, using the posterior estimates from the original model. There is no new inference here, we are just using the same model and the same posterior estimates to generate predictions. The {class}`Flat` distribution is just a placeholder to make the model work; the actual values are sampled from the posterior.
 
 ```{code-cell} ipython3
 def sample_posterior_predictive(X_test, Y_test, trace, n_hidden=5):