diff --git a/examples/simulation_example_SplineFlow.ipynb b/examples/simulation_example_SplineFlow.ipynb
index 8259e0e..0fe06f7 100644
--- a/examples/simulation_example_SplineFlow.ipynb
+++ b/examples/simulation_example_SplineFlow.ipynb
@@ -109,7 +109,7 @@
    "source": [
     "# Select Normalizing Flow\n",
     "\n",
-    "In the following, we specify a list of candidate normalizing flows. The function *dist_select* returns the negative log-likelihood of each specification. The  normalizing flow with the lowest negative log-likelihood is selected. The function also plots the density of the target variable and the fitted density, using the best suitable  normalizing flow among the specified ones. However, note that choosing the best performing flow based solely on training data may lead to overfitting, since normalizing flows have a higher risk of overfitting compared to parametric distributions. When using normalizing flows, it is crucial to carefully select the specifications to strike a balance between model complexity and generalization ability."
+    "In the following, we specify a list of candidate normalizing flows. The function *flow_select* returns the negative log-likelihood of each specification. The  normalizing flow with the lowest negative log-likelihood is selected. The function also plots the density of the target variable and the fitted density, using the best suitable  normalizing flow among the specified ones. However, note that choosing the best performing flow based solely on training data may lead to overfitting, since normalizing flows have a higher risk of overfitting compared to parametric distributions. When using normalizing flows, it is crucial to carefully select the specifications to strike a balance between model complexity and generalization ability."
    ]
   },
   {