[BayesInference][MCMC] min and max will be respected if the intial samples are provided.

BayesValidRox/BayesInference/MCMC.py

@@ -56,12 +56,17 @@ class MCMC():
                 # when aPCE selected - gaussian kernel distribution
                 initsamples = priorDist.resample(self.nwalkers).T
         else:
-            # Pick samples based on a uniform dist between main and max of each dim
+            # Pick samples based on a uniform dist between min and max of each dim
             initsamples = np.zeros((self.nwalkers, ndim))
+            BoundTuples = []
             for idxDim in range(ndim):
                 lower, upper = np.min(self.initsamples[:,idxDim]),np.max(self.initsamples[:,idxDim])
+                BoundTuples.append((lower, upper))
                 initsamples[:,idxDim] = st.uniform(loc=lower, scale=upper-lower).rvs(size=self.nwalkers)
-        
+            
+            # Update lower and upper
+            PCEModel.ExpDesign.BoundTuples = BoundTuples
+            
         print("\n>>>> Bayesian inference with MCMC for {0} started. <<<<<<".format(self.BayesOpts.Name))
 
         if self.mp: