diff --git a/BayesianKDEy/full_experiments.py b/BayesianKDEy/full_experiments.py
index ad8bbf4..3c99bc5 100644
--- a/BayesianKDEy/full_experiments.py
+++ b/BayesianKDEy/full_experiments.py
@@ -54,12 +54,13 @@ def methods():
     yield 'BayesianACC', ACC(LR()), acc_hyper, lambda hyper: BayesianCC(LR(), mcmc_seed=0)
 
     yield 'BootstrapHDy', DMy(LR()), hdy_hyper, lambda hyper: AggregativeBootstrap(DMy(LR(), **hyper), n_test_samples=1000, random_state=0),
-    # yield 'BayesianHDy', PQ(LR(), stan_seed=0), hdy_hyper
+    yield 'BayesianHDy', DMy(LR()), hdy_hyper, lambda hyper: PQ(LR(), stan_seed=0, **hyper),
 
     yield 'BootstrapKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: AggregativeBootstrap(KDEyML(LR(), **hyper), n_test_samples=1000, random_state=0, verbose=True),
     yield 'BayesianKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: BayesianKDEy(mcmc_seed=0, **hyper),
     yield 'BayesianKDEy*', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, **hyper),
-    yield 'BayKDEy*CLR', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, explore_CLR=True, step_size=.15, **hyper),
+    yield 'BayKDEy*CLR', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, explore_CLR=True, step_size=.1, **hyper),
+    # yield 'BayKDEy*CLR2', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, explore_CLR=True, step_size=.15, **hyper),
 
 
 def model_selection(train: LabelledCollection, point_quantifier: AggregativeQuantifier, grid: dict):
diff --git a/quapy/method/confidence.py b/quapy/method/confidence.py
index 6ff713a..7a0161b 100644
--- a/quapy/method/confidence.py
+++ b/quapy/method/confidence.py
@@ -323,13 +323,16 @@ class ConfidenceIntervals(ConfidenceRegionABC):
     :param samples: np.ndarray of shape (n_bootstrap_samples, n_classes)
     :param confidence_level: float, the confidence level (default 0.95)
     """
-    def __init__(self, samples, confidence_level=0.95):
+    def __init__(self, samples, confidence_level=0.95, bonferroni_correction=False):
         assert 0 < confidence_level < 1, f'{confidence_level=} must be in range(0,1)'
 
         samples = np.asarray(samples)
 
         self.means_ = samples.mean(axis=0)
         alpha = 1-confidence_level
+        if bonferroni_correction:
+            n_classes = samples.shape[-1]
+            alpha = alpha/n_classes
         low_perc = (alpha/2.)*100
         high_perc = (1-alpha/2.)*100
         self.I_low, self.I_high = np.percentile(samples, q=[low_perc, high_perc], axis=0)