diff --git a/src/bayesvalidrox/bayes_inference/bayes_inference.py b/src/bayesvalidrox/bayes_inference/bayes_inference.py
index 85f68c4aa3422afa8989d50c43e3f4f8063fd10d..3ba266cb9b3d10f90a80d4d2b7dd7484e6cf9e6f 100644
--- a/src/bayesvalidrox/bayes_inference/bayes_inference.py
+++ b/src/bayesvalidrox/bayes_inference/bayes_inference.py
@@ -1034,10 +1034,10 @@ class BayesInference:
             axes = np.array(figPosterior.axes).reshape((len(par_names), len(par_names)))
             for yi in range(len(par_names)):
                 ax = axes[yi, yi]
-                ax.set_xlim(PCEModel.BoundTuples[yi])
+                ax.set_xlim(PCEModel.bound_tuples[yi])
                 for xi in range(yi):
                     ax = axes[yi, xi]
-                    ax.set_xlim(PCEModel.BoundTuples[xi])
+                    ax.set_xlim(PCEModel.bound_tuples[xi])
 
         # Extract the axes
         # axes = np.array(figPosterior.axes).reshape((NofPa, NofPa))
diff --git a/src/bayesvalidrox/bayes_inference/mcmc.py b/src/bayesvalidrox/bayes_inference/mcmc.py
index 1c918be1a7004721eae339d7cad4e0d3d48e29a2..4edf8be7220a78e1a6b657062205c4258e648bbd 100755
--- a/src/bayesvalidrox/bayes_inference/mcmc.py
+++ b/src/bayesvalidrox/bayes_inference/mcmc.py
@@ -95,16 +95,16 @@ class MCMC():
                 # Pick samples based on a uniform dist between min and max of
                 # each dim
                 initsamples = np.zeros((self.nwalkers, ndim))
-                BoundTuples = []
+                bound_tuples = []
                 for idxDim in range(ndim):
                     lower = np.min(self.initsamples[:, idxDim])
                     upper = np.max(self.initsamples[:, idxDim])
-                    BoundTuples.append((lower, upper))
+                    bound_tuples.append((lower, upper))
                     dist = st.uniform(loc=lower, scale=upper-lower)
                     initsamples[:, idxDim] = dist.rvs(size=self.nwalkers)
 
                 # Update lower and upper
-                PCEModel.ExpDesign.BoundTuples = BoundTuples
+                PCEModel.ExpDesign.bound_tuples = bound_tuples
 
         # Check if sigma^2 needs to be inferred
         if Discrepancy.optSigma != 'B':
@@ -116,8 +116,8 @@ class MCMC():
             # Update ndim
             ndim = initsamples.shape[1]
 
-            # Update BoundTuples
-            PCEModel.ExpDesign.BoundTuples += Discrepancy.ExpDesign.BoundTuples
+            # Update bound_tuples
+            PCEModel.ExpDesign.bound_tuples += Discrepancy.ExpDesign.bound_tuples
 
         print("\n>>>> Bayesian inference with MCMC for "
               f"{self.BayesOpts.Name} started. <<<<<<")
@@ -278,7 +278,7 @@ class MCMC():
             for i in range(len(Discrepancy.InputDisc.Marginals)):
                 par_names.append(Discrepancy.InputDisc.Marginals[i].Name)
 
-        params_range = PCEModel.ExpDesign.BoundTuples
+        params_range = PCEModel.ExpDesign.bound_tuples
 
         # Plot traces
         if self.verbose and self.nsteps < 10000:
@@ -358,9 +358,9 @@ class MCMC():
 
         PCEModel = self.BayesOpts.PCEModel
         Discrepancy = self.BayesOpts.Discrepancy
-        nSigma2 = len(Discrepancy.ExpDesign.BoundTuples) if Discrepancy.optSigma != 'B' else -len(theta)
+        nSigma2 = len(Discrepancy.ExpDesign.bound_tuples) if Discrepancy.optSigma != 'B' else -len(theta)
         priorDist = PCEModel.ExpDesign.priorSpace
-        params_range = PCEModel.ExpDesign.BoundTuples
+        params_range = PCEModel.ExpDesign.bound_tuples
         ndimTheta = theta.ndim
         theta = theta if ndimTheta != 1 else theta.reshape((1,-1))
         nsamples = theta.shape[0]
@@ -379,7 +379,7 @@ class MCMC():
                 # Check if bias term needs to be inferred
                 if Discrepancy.optSigma != 'B':
                     if self.check_ranges(theta[i,-nSigma2:], 
-                                         Discrepancy.ExpDesign.BoundTuples):
+                                         Discrepancy.ExpDesign.bound_tuples):
                         if all('unif' in Discrepancy.ExpDesign.InputObj.Marginals[i].DistType  for i in \
                             range(Discrepancy.ExpDesign.ndim)):
                             logprior[i] = 0.0
@@ -397,7 +397,7 @@ class MCMC():
         PCEModel = BayesOpts.PCEModel
         Discrepancy = self.BayesOpts.Discrepancy
         if Discrepancy.optSigma != 'B':
-            nSigma2 = len(Discrepancy.ExpDesign.BoundTuples)
+            nSigma2 = len(Discrepancy.ExpDesign.bound_tuples)
         else:
             nSigma2 = -len(theta)
         # Check if bias term needs to be inferred