diff --git a/BayesValidRox/BayesInference/MCMC.py b/BayesValidRox/BayesInference/MCMC.py
index db1c4a4a900521cbb77c5d4b6621af1bac154cbc..489f0e026ef84011d0d0d6fd3634b7d39b4a2485 100755
--- a/BayesValidRox/BayesInference/MCMC.py
+++ b/BayesValidRox/BayesInference/MCMC.py
@@ -60,6 +60,8 @@ class MCMC():
                 lower, upper = np.min(self.initsamples[:,idxDim]),np.max(self.initsamples[:,idxDim])
                 initsamples[:,idxDim] = st.uniform(loc=lower, scale=upper-lower).rvs(size=self.nwalkers)
         
+        print("\n>>>> Bayesian inference with MCMC started. <<<<<<")
+
         if self.mp:
             with Pool() as pool:
                 sampler = emcee.EnsembleSampler(self.nwalkers, ndim, self.log_posterior, args=[Observation, TotalSigma2], pool=pool)
@@ -73,6 +75,28 @@ class MCMC():
         # we'll throw-out the burn-in points and reshape:
         emcee_trace = sampler.chain[:, self.nburn:, :].reshape(-1, ndim).T
         
+        # Posterior diagnostics
+        try:
+            tau = sampler.get_autocorr_time(tol=0)
+        except emcee.autocorr.AutocorrError:
+            tau = 2
+        burnin = int(2*np.max(tau))
+        thin = int(0.5*np.min(tau))
+        samples = sampler.get_chain(discard=burnin, flat=True, thin=thin)
+        log_prob_samples = sampler.get_log_prob(discard=burnin, flat=True, thin=thin)
+        
+        print('\n')
+        print('-'*15 + 'Posterior diagnostics' + '-'*15)
+        print("mean auto-correlation time: {0:.3f}".format(np.mean(tau)))
+        print("thin: {0}".format(thin))
+        print("burn-in: {0}".format(burnin))
+        print("flat chain shape: {0}".format(samples.shape))
+        print("Mean acceptance fraction*: {0:.3f}".format(np.mean(sampler.acceptance_fraction)))
+        print("flat log prob shape: {0}".format(log_prob_samples.shape))
+        print("\n* This value must lay between 0.2 and 0.5")
+        print('-'*50)
+        
+        print("\n>>>> Bayesian inference with MCMC successfully completed. <<<<<<\n")
         # logml_dict = self.Marginal_llk_emcee(sampler, self.nburn, logp=None, maxiter=5000)
         # print('\nThe Bridge Sampling Estimation is %.5f.'%(logml_dict['logml']))
         
diff --git a/BayesValidRox/surrogate_models/surrogate_models.py b/BayesValidRox/surrogate_models/surrogate_models.py
index 4431814f0dd6a88b6b5e3e419b1420f266247fb1..56b7ff91d617d8d1d547ed51b8608e2328822cfb 100644
--- a/BayesValidRox/surrogate_models/surrogate_models.py
+++ b/BayesValidRox/surrogate_models/surrogate_models.py
@@ -1030,7 +1030,7 @@ class Metamodel:
                     if self.metaModel.lower() == 'pcekriging':
                         self.gp_poly[key]["y_"+str(idx+1)] =  self.GaussianProcessEmulator(CollocationPoints, self.LCerror[key]["y_"+str(idx+1)])
         
-            print("\n>>>> Training the {0} metamodel finished. <<<<<<\n".format(self.metaModel))
+            print("\n>>>> Training the {0} metamodel sucessfully completed. <<<<<<\n".format(self.metaModel))
         
         return self
     #--------------------------------------------------------------------------------------------------------
diff --git a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
index af95523e8751d7cc0231693d7e51c3f4f68d7d50..1a3a3d4007d22beec52f78bd4736f61c398ad5a3 100755
--- a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
+++ b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
@@ -39,7 +39,7 @@ from BayesInference.BayesInference import BayesInference, Discrepancy
 if __name__ == "__main__":
     
     # Number of parameters
-    ndim = 10 # 2, 10
+    ndim = 2 # 2, 10
     
     #=====================================================
     #=============   COMPUTATIONAL MODEL  ================
@@ -231,7 +231,7 @@ if __name__ == "__main__":
     # Compute and print RMSE error
     PostPCE.relErrorPCEModel(nSamples=3000)
     
-    # sys.exit('STOP!!')
+    sys.exit('STOP!!')
     #=====================================================
     #========  Bayesian inference with Emulator ==========
     #=====================================================
@@ -245,7 +245,7 @@ if __name__ == "__main__":
     
     # Select the inference method
     BayesOpts.SamplingMethod = 'MCMC'
-    BayesOpts.MCMCnSteps = 1000
+    BayesOpts.MCMCnSteps = 5000
     BayesOpts.MultiProcessMCMC = True
     
     
@@ -263,7 +263,7 @@ if __name__ == "__main__":
     
     
     Bayes_PCE = BayesOpts.create_Inference()
-
+    # sys.exit('STOP!!')
     #=====================================================
     #====== Bayesian inference with Forward Model ========
     #=====================================================