diff --git a/BayesianKDEy/commons.py b/BayesianKDEy/commons.py
index a9ed1ba..bbd9531 100644
--- a/BayesianKDEy/commons.py
+++ b/BayesianKDEy/commons.py
@@ -22,13 +22,13 @@ def fetch_UCI_binary(data_name):
 # global configurations
 
 binary = {
-    'datasets': qp.datasets.UCI_BINARY_DATASETS,
+    'datasets': qp.datasets.UCI_BINARY_DATASETS.copy(),
     'fetch_fn': fetch_UCI_binary,
     'sample_size': 500
 }
 
 multiclass = {
-    'datasets': qp.datasets.UCI_MULTICLASS_DATASETS,
+    'datasets': qp.datasets.UCI_MULTICLASS_DATASETS.copy(),
     'fetch_fn': fetch_UCI_multiclass,
     'sample_size': 1000
 }
diff --git a/BayesianKDEy/prior_effect.py b/BayesianKDEy/prior_effect.py
index 8574bc4..4fa1f19 100644
--- a/BayesianKDEy/prior_effect.py
+++ b/BayesianKDEy/prior_effect.py
@@ -14,6 +14,7 @@ from sklearn.linear_model import LogisticRegression as LR
 from copy import deepcopy as cp
 from tqdm import tqdm
 from full_experiments import model_selection
+from itertools import chain
 
 
 def select_imbalanced_datasets(top_m=5):
@@ -107,13 +108,52 @@ def experiment(dataset: Dataset,
         return results
 
 
+def concat_reports(reports):
+    final_report = {
+        k: list(chain.from_iterable(report[k] for report in reports))
+        for k in reports[0]
+    }
+    df = pd.DataFrame(final_report)
+    return df
+
+
+def plot_results(df):
+    import seaborn as sns
+    import matplotlib.pyplot as plt
+
+    sns.set_theme(style="whitegrid", context="paper")
+
+    fig, axes = plt.subplots(1, 2, figsize=(10, 4), sharey=True)
+
+    for ax, prior in zip(axes, ['informative', 'wrong']):
+        sub = df[df['prior-type'] == prior]
+
+        sns.lineplot(
+            data=sub,
+            x='concentration',
+            y='ae',
+            hue='method_name',
+            marker='o',
+            errorbar='se',  # o 'sd'
+            ax=ax
+        )
+
+        ax.set_xscale('log')
+        ax.set_title(f'Prior: {prior}')
+        ax.set_xlabel('Concentration')
+        ax.set_ylabel('MAE')
+
+    plt.tight_layout()
+    plt.show()
+
+
 if __name__ == '__main__':
     result_dir = Path('./results/prior_effect')
     selected = select_imbalanced_datasets()
     print(f'selected datasets={selected}')
     qp.environ['SAMPLE_SIZE'] = multiclass['sample_size']
     reports = []
-    for data_name in selected:
+    for data_name in selected[:2]:
         data = multiclass['fetch_fn'](data_name)
         for method_name, surrogate_quant, hyper_params, bay_constructor in methods():
             result_path = experiment_path(result_dir, data_name, method_name)
@@ -125,6 +165,9 @@ if __name__ == '__main__':
             )
             reports.append(report)
 
+    # concat all reports as a dataframe
+    df = concat_reports(reports)
 
+    plot_results(df)
 
-    # df = pd.DataFrame(results)
\ No newline at end of file
+    print('ONLY TWO DATASETS')
\ No newline at end of file