diff --git a/pyhgf/updates/posterior/continuous/__init__.py b/pyhgf/updates/posterior/continuous/__init__.py
index fd874075..a87285b6 100644
--- a/pyhgf/updates/posterior/continuous/__init__.py
+++ b/pyhgf/updates/posterior/continuous/__init__.py
@@ -3,6 +3,5 @@
 
 __all__ = [
     "continuous_node_posterior_update_ehgf",
-    "continuous_node_posterior_update_unbounded",
     "continuous_node_posterior_update",
 ]
diff --git a/tests/test_updates/posterior/continuous.py b/tests/test_updates/posterior/continuous.py
index 6bc59f39..6982a63c 100644
--- a/tests/test_updates/posterior/continuous.py
+++ b/tests/test_updates/posterior/continuous.py
@@ -1,10 +1,11 @@
 # Author: Nicolas Legrand <nicolas.legrand@cas.au.dk>
 
+import jax.numpy as jnp
+
 from pyhgf.model import Network
 from pyhgf.updates.posterior.continuous import (
     continuous_node_posterior_update,
     continuous_node_posterior_update_ehgf,
-    continuous_node_posterior_update_unbounded,
 )
 
 
@@ -20,17 +21,56 @@ def test_continuous_posterior_updates():
 
     # Standard HGF updates -------------------------------------------------------------
     # ----------------------------------------------------------------------------------
+
+    # value update
+    attributes, edges, _ = network.get_network()
+    attributes[0]["temp"]["value_prediction_error"] = 1.0357
+    attributes[0]["mean"] = 1.0357
+
+    new_attributes = continuous_node_posterior_update(
+        attributes=attributes, node_idx=1, edges=edges
+    )
+    assert jnp.isclose(new_attributes[1]["mean"], 0.51785)
+
+    # volatility update
     attributes, edges, _ = network.get_network()
-    _ = continuous_node_posterior_update(attributes=attributes, node_idx=2, edges=edges)
+    attributes[1]["temp"]["effective_precision"] = 0.01798621006309986
+    attributes[1]["temp"]["value_prediction_error"] = 0.5225493907928467
+    attributes[1]["temp"]["volatility_prediction_error"] = -0.23639076948165894
+    attributes[1]["expected_precision"] = 0.9820137619972229
+    attributes[1]["mean"] = 0.5225493907928467
+    attributes[1]["precision"] = 1.9820137023925781
+
+    new_attributes = continuous_node_posterior_update(
+        attributes=attributes, node_idx=2, edges=edges
+    )
+    assert jnp.isclose(new_attributes[1]["mean"], -0.0021212)
+    assert jnp.isclose(new_attributes[1]["precision"], 1.0022112)
 
     # eHGF updates ---------------------------------------------------------------------
     # ----------------------------------------------------------------------------------
-    _ = continuous_node_posterior_update_ehgf(
+
+    # value update
+    attributes, edges, _ = network.get_network()
+    attributes[0]["temp"]["value_prediction_error"] = 1.0357
+    attributes[0]["mean"] = 1.0357
+
+    new_attributes = continuous_node_posterior_update_ehgf(
         attributes=attributes, node_idx=2, edges=edges
     )
+    assert jnp.isclose(new_attributes[1]["mean"], 0.51785)
 
-    # unbounded updates ----------------------------------------------------------------
-    # ----------------------------------------------------------------------------------
-    _ = continuous_node_posterior_update_unbounded(
+    # volatility update
+    attributes, edges, _ = network.get_network()
+    attributes[1]["temp"]["effective_precision"] = 0.01798621006309986
+    attributes[1]["temp"]["value_prediction_error"] = 0.5225493907928467
+    attributes[1]["temp"]["volatility_prediction_error"] = -0.23639076948165894
+    attributes[1]["expected_precision"] = 0.9820137619972229
+    attributes[1]["mean"] = 0.5225493907928467
+    attributes[1]["precision"] = 1.9820137023925781
+
+    new_attributes = continuous_node_posterior_update_ehgf(
         attributes=attributes, node_idx=2, edges=edges
     )
+    assert jnp.isclose(new_attributes[1]["mean"], -0.00212589)
+    assert jnp.isclose(new_attributes[1]["precision"], 1.0022112)