diff --git a/BayesianKDEy/full_experiments.py b/BayesianKDEy/full_experiments.py
index 2fd7080..a2f3c9c 100644
--- a/BayesianKDEy/full_experiments.py
+++ b/BayesianKDEy/full_experiments.py
@@ -6,13 +6,14 @@ from pathlib import Path
 from sklearn.linear_model import LogisticRegression
 import quapy as qp
 from BayesianKDEy._bayeisan_kdey import BayesianKDEy
+from method.aggregative import DistributionMatchingY as DMy
 from quapy.method.base import BinaryQuantifier
 from quapy.model_selection import GridSearchQ
 from quapy.data import Dataset
 # from BayesianKDEy.plot_simplex import plot_prev_points, plot_prev_points_matplot
-from quapy.method.confidence import ConfidenceIntervals, BayesianCC, PQ, WithConfidenceABC
+from quapy.method.confidence import ConfidenceIntervals, BayesianCC, PQ, WithConfidenceABC, AggregativeBootstrap
 from quapy.functional import strprev
-from quapy.method.aggregative import KDEyML
+from quapy.method.aggregative import KDEyML, ACC
 from quapy.protocol import UPP
 import quapy.functional as F
 import numpy as np
@@ -34,9 +35,14 @@ def new_classifier():
 
 def methods():
     cls, cls_hyper = new_classifier()
+    hdy_hyper = {**cls_hyper, 'n_bins': [3,4,5,8,16,32]}
+    kdey_hyper = {**cls_hyper, 'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}
+    yield 'BootstrapACC', AggregativeBootstrap(ACC(clone(cls)), n_test_samples=1000), cls_hyper
+    yield 'BootstrapHDy', AggregativeBootstrap(DMy(clone(cls), divergence='HD'), n_test_samples=1000), hdy_hyper
+    yield 'BootstrapKDEy', AggregativeBootstrap(KDEyML(clone(cls)), n_test_samples=1000), kdey_hyper
     # yield 'BayesianACC', BayesianCC(clone(cls), mcmc_seed=0), cls_hyper
-    # yield 'BayesianHDy', PQ(clone(cls), stan_seed=0), {**cls_hyper, 'n_bins': [3,4,5,8,16,32]}
-    yield 'BayesianKDEy', BayesianKDEy(clone(cls), mcmc_seed=0), {**cls_hyper, 'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}
+    # yield 'BayesianHDy', PQ(clone(cls), stan_seed=0), hdy_hyper
+    # yield 'BayesianKDEy', BayesianKDEy(clone(cls), mcmc_seed=0), kdey_hyper
 
 
 def experiment(dataset: Dataset, method: WithConfidenceABC, grid: dict):