diff --git a/src/pyhgf/model/hgf.py b/src/pyhgf/model/hgf.py
index 74746de1..964e92d6 100644
--- a/src/pyhgf/model/hgf.py
+++ b/src/pyhgf/model/hgf.py
@@ -28,8 +28,6 @@ class HGF(Network):
         strength with children and parents (i.e. `"value_coupling_parents"`,
         `"value_coupling_children"`, `"volatility_coupling_parents"`,
         `"volatility_coupling_children"`).
-    verbose : bool
-        Verbosity level.
 
     """
 
@@ -62,7 +60,6 @@ def __init__(
             "2": 0.0,
             "3": 0.0,
         },
-        verbose: bool = True,
     ) -> None:
         r"""Parameterization of the HGF model.
 
@@ -107,27 +104,15 @@ def __init__(
             A dictionary containing the initial values for the tonic drift
             at different levels of the hierarchy. This represents the drift of the
             random walk. Defaults set all entries to `0.0` (no drift).
-        verbose :
-            The verbosity of the methods for model creation and fitting. Defaults to
-            `True`.
 
         """
-        self.verbose = verbose
         Network.__init__(self)
         self.model_type = model_type
         self.n_levels = n_levels
 
         if model_type not in ["continuous", "binary"]:
-            if self.verbose:
-                print("Initializing a network with custom node structure.")
+            raise ValueError("Invalid model type.")
         else:
-            if self.verbose:
-                print(
-                    (
-                        f"Creating a {self.model_type} Hierarchical Gaussian Filter "
-                        f"with {self.n_levels} levels."
-                    )
-                )
             if model_type == "continuous":
                 # Input
                 self.add_nodes(
diff --git a/src/pyhgf/model/network.py b/src/pyhgf/model/network.py
index b0e85b2d..80239853 100644
--- a/src/pyhgf/model/network.py
+++ b/src/pyhgf/model/network.py
@@ -65,7 +65,6 @@ def __init__(self) -> None:
         self.update_sequence: Optional[UpdateSequence] = None
         self.scan_fn: Optional[Callable] = None
         self.inputs: Inputs
-        self.verbose: bool = False
 
     def create_belief_propagation_fn(self, overwrite: bool = True) -> "Network":
         """Create the belief propagation function.
@@ -88,8 +87,6 @@ def create_belief_propagation_fn(self, overwrite: bool = True) -> "Network":
         # create the update sequence if it does not already exist
         if self.update_sequence is None:
             self.set_update_sequence()
-            if self.verbose:
-                print("... Create the update sequence from the network structure.")
 
         # create the belief propagation function
         # this function is used by scan to loop over observations
@@ -99,8 +96,6 @@ def create_belief_propagation_fn(self, overwrite: bool = True) -> "Network":
                 update_sequence=self.update_sequence,
                 structure=self.structure,
             )
-            if self.verbose:
-                print("... Create the belief propagation function.")
         else:
             if overwrite:
                 self.scan_fn = Partial(
@@ -108,11 +103,6 @@ def create_belief_propagation_fn(self, overwrite: bool = True) -> "Network":
                     update_sequence=self.update_sequence,
                     structure=self.structure,
                 )
-                if self.verbose:
-                    print("... Create the belief propagation function (overwrite).")
-            else:
-                if self.verbose:
-                    print("... The belief propagation function is already defined.")
 
         return self
 
@@ -138,8 +128,6 @@ def cache_belief_propagation_fn(self) -> "Network":
                 jnp.ones((1, 1)),
             ),
         )
-        if self.verbose:
-            print("... Cache the belief propagation function.")
 
         return self
 
@@ -175,8 +163,6 @@ def input_data(
         """
         if self.scan_fn is None:
             self = self.create_belief_propagation_fn()
-        if self.verbose:
-            print((f"Adding {len(input_data)} new observations."))
         if time_steps is None:
             time_steps = np.ones((len(input_data), 1))  # time steps vector
         else:
@@ -247,8 +233,6 @@ def input_custom_sequence(
             missing in the event log, or rejected trials).
 
         """
-        if self.verbose:
-            print((f"Adding {len(input_data)} new observations."))
         if time_steps is None:
             time_steps = np.ones(len(input_data))  # time steps vector
 
diff --git a/tests/test_categorical.py b/tests/test_categorical.py
index e08eafc0..f62cd593 100644
--- a/tests/test_categorical.py
+++ b/tests/test_categorical.py
@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from pyhgf.model import HGF
+from pyhgf.model import Network
 
 
 def test_categorical_state_node():
@@ -13,7 +13,7 @@ def test_categorical_state_node():
     input_data = np.vstack([[0.0] * input_data.shape[1], input_data])
 
     # create the categorical HGF
-    categorical_hgf = HGF(model_type=None, verbose=False).add_nodes(
+    categorical_hgf = Network().add_nodes(
         kind="categorical-input",
         node_parameters={
             "n_categories": 3,
diff --git a/tests/test_model.py b/tests/test_model.py
index ee50f758..386c6167 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -4,7 +4,7 @@
 import numpy as np
 
 from pyhgf import load_data
-from pyhgf.model import HGF
+from pyhgf.model import HGF, Network
 from pyhgf.response import total_gaussian_surprise
 
 
@@ -16,7 +16,7 @@ def test_HGF():
     #####################
 
     custom_hgf = (
-        HGF(model_type=None)
+        Network()
         .add_nodes(kind="continuous-input")
         .add_nodes(kind="binary-input")
         .add_nodes(value_children=0)
diff --git a/tests/test_plots.py b/tests/test_plots.py
index 24956207..d6551c0e 100644
--- a/tests/test_plots.py
+++ b/tests/test_plots.py
@@ -4,7 +4,7 @@
 import numpy as np
 
 from pyhgf import load_data
-from pyhgf.model import HGF
+from pyhgf.model import HGF, Network
 
 
 def test_plotting_functions():
@@ -137,7 +137,7 @@ def test_plotting_functions():
     input_data = np.vstack([[0.0] * input_data.shape[1], input_data])
 
     # create the categorical HGF
-    categorical_hgf = HGF(model_type=None, verbose=False).add_nodes(
+    categorical_hgf = Network().add_nodes(
         kind="categorical-input",
         node_parameters={
             "n_categories": 3,