diff --git a/quapy/method/confidence.py b/quapy/method/confidence.py
index a630fe6..c997fb3 100644
--- a/quapy/method/confidence.py
+++ b/quapy/method/confidence.py
@@ -624,7 +624,7 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
                  classifier: BaseEstimator=None,
                  fit_classifier=True,
                  val_split: int = 5,
-                 n_bins: int = 4,
+                 nbins: int = 4,
                  fixed_bins: bool = False,
                  num_warmup: int = 500,
                  num_samples: int = 1_000,
@@ -642,7 +642,7 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
                               "Run `$ pip install quapy[bayes]` to install them.")
 
         super().__init__(classifier, fit_classifier, val_split)
-        self.n_bins = n_bins
+        self.nbins = nbins
         self.fixed_bins = fixed_bins
         self.num_warmup = num_warmup
         self.num_samples = num_samples
@@ -657,10 +657,10 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
         # Compute bin limits
         if self.fixed_bins:
             # Uniform bins in [0,1]
-            self.bin_limits = np.linspace(0, 1, self.n_bins + 1)
+            self.bin_limits = np.linspace(0, 1, self.nbins + 1)
         else:
             # Quantile bins
-            self.bin_limits = np.quantile(y_pred, np.linspace(0, 1, self.n_bins + 1))
+            self.bin_limits = np.quantile(y_pred, np.linspace(0, 1, self.nbins + 1))
 
         # Assign each prediction to a bin
         bin_indices = np.digitize(y_pred, self.bin_limits[1:-1], right=True)
@@ -670,14 +670,14 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
         neg_mask = ~pos_mask
 
         # Count positives and negatives per bin
-        self.pos_hist = np.bincount(bin_indices[pos_mask], minlength=self.n_bins)
-        self.neg_hist = np.bincount(bin_indices[neg_mask], minlength=self.n_bins)
+        self.pos_hist = np.bincount(bin_indices[pos_mask], minlength=self.nbins)
+        self.neg_hist = np.bincount(bin_indices[neg_mask], minlength=self.nbins)
 
     def aggregate(self, classif_predictions):
         Px_test = classif_predictions[:, self.pos_label]
         test_hist, _ = np.histogram(Px_test, bins=self.bin_limits)
         prevs = _bayesian.pq_stan(
-            self.stan_code, self.n_bins, self.pos_hist, self.neg_hist, test_hist,
+            self.stan_code, self.nbins, self.pos_hist, self.neg_hist, test_hist,
             self.num_samples, self.num_warmup, self.stan_seed
         ).flatten()
         self.prev_distribution = np.vstack([1-prevs, prevs]).T