diff --git a/sdmetrics/single_table/bayesian_network.py b/sdmetrics/single_table/bayesian_network.py
index d2f03151..cc2ed187 100644
--- a/sdmetrics/single_table/bayesian_network.py
+++ b/sdmetrics/single_table/bayesian_network.py
@@ -7,6 +7,8 @@
 
 from sdmetrics.goal import Goal
 from sdmetrics.single_table.base import SingleTableMetric
+from sklearn.preprocessing import LabelEncoder
+from pomegranate import bayesian_network
 
 LOGGER = logging.getLogger(__name__)
 
@@ -16,12 +18,6 @@ class BNLikelihoodBase(SingleTableMetric):
 
     @classmethod
     def _likelihoods(cls, real_data, synthetic_data, metadata=None, structure=None):
-        try:
-            from pomegranate import BayesianNetwork
-        except ImportError:
-            raise ImportError(
-                'Please install pomegranate with `pip install pomegranate` on a version of python '
-                '< 3.11. This metric is not supported on python versions >= 3.11.')
 
         real_data, synthetic_data, metadata = cls._validate_inputs(
             real_data, synthetic_data, metadata)
@@ -30,19 +26,29 @@ def _likelihoods(cls, real_data, synthetic_data, metadata=None, structure=None):
 
         if not fields:
             return np.full(len(real_data), np.nan)
+        
+        encoders = {field: LabelEncoder() for field in fields}
+
+        real_data_encoded = real_data.copy()
+        synthetic_data_encoded = synthetic_data.copy()
+
+        for field in fields:
+            real_data_encoded[field] = encoders[field].fit_transform(real_data_encoded[field])
+
+        for field in fields:
+            synthetic_data_encoded[field] = encoders[field].fit_transform(synthetic_data_encoded[field])
 
         LOGGER.debug('Fitting the BayesianNetwork to the real data')
         if structure:
-            if isinstance(structure, dict):
-                structure = BayesianNetwork.from_json(json.dumps(structure)).structure
-
-            bn = BayesianNetwork.from_structure(real_data[fields].to_numpy(), structure)
+            bn = bayesian_network.BayesianNetwork(structure=structure, algorithm='chow-liu')
         else:
-            bn = BayesianNetwork.from_samples(real_data[fields].to_numpy(), algorithm='chow-liu')
+            bn = bayesian_network.BayesianNetwork(algorithm='chow-liu')
+
+        bn.fit(real_data_encoded[fields].to_numpy())
 
         LOGGER.debug('Evaluating likelihood of the synthetic data')
         probabilities = []
-        for _, row in synthetic_data[fields].iterrows():
+        for _, row in synthetic_data_encoded[fields].iterrows():
             try:
                 probabilities.append(bn.probability([row.to_numpy()]))
             except ValueError: