diff --git a/elfi/methods/inference/__init__.py b/elfi/methods/inference/__init__.py
new file mode 100644
index 00000000..6e031999
--- /dev/null
+++ b/elfi/methods/inference/__init__.py
@@ -0,0 +1 @@
+# noqa: D104
diff --git a/elfi/methods/inference/romc.py b/elfi/methods/inference/romc.py
index f75c1e91..8e278437 100644
--- a/elfi/methods/inference/romc.py
+++ b/elfi/methods/inference/romc.py
@@ -493,7 +493,8 @@ def __init__(self, model, bounds=None, discrepancy_name=None, output_names=None,
         self.distances = None
         self.result = None  # RomcSample object
 
-        self.progress_bar = ProgressBar()
+        self.progress_bar = ProgressBar(prefix='Progress', suffix='Complete',
+                                        decimals=1, length=50, fill='=')
 
         super(ROMC, self).__init__(model, output_names, **kwargs)
 
@@ -633,8 +634,9 @@ def _solve_gradients(self, **kwargs):
         attempted = [False for _ in range(n1)]
         tic = timeit.default_timer()
         if parallelize is False:
+            self.progress_bar.reinit_progressbar(reinit_msg="Solving gradients")
             for i in range(n1):
-                self.progress_bar.update_progressbar(i, n1)
+                self.progress_bar.update_progressbar(i + 1, n1)
                 attempted[i] = True
                 is_solved = optim_probs[i].solve_gradients(**kwargs)
                 solved[i] = is_solved
@@ -681,8 +683,9 @@ def _solve_bo(self, **kwargs):
         attempted = []
         solved = []
         tic = timeit.default_timer()
+        self.progress_bar.reinit_progressbar(reinit_msg="Bayesian Optimization")
         for i in range(n1):
-            self.progress_bar.update_progressbar(i, n1)
+            self.progress_bar.update_progressbar(i + 1, n1)
             attempted.append(True)
             is_solved = optim_problems[i].solve_bo(**kwargs)
             solved.append(is_solved)
@@ -774,8 +777,9 @@ def _build_boxes(self, **kwargs):
         # main
         computed_bb = [False for _ in range(n1)]
         if parallelize is False:
+            self.progress_bar.reinit_progressbar(reinit_msg="Building boxes")
             for i in range(n1):
-                self.progress_bar.update_progressbar(i, n1)
+                self.progress_bar.update_progressbar(i + 1, n1)
                 if accepted[i]:
                     is_built = optim_problems[i].build_region(**kwargs)
                     computed_bb.append(is_built)
@@ -808,8 +812,9 @@ def _fit_models(self, **kwargs):
 
         # main
         if parallelize is False:
+            self.progress_bar.reinit_progressbar(reinit_msg="Fitting models")
             for i in range(n1):
-                self.progress_bar.update_progressbar(i, n1)
+                self.progress_bar.update_progressbar(i + 1, n1)
                 if accepted[i]:
                     optim_problems[i].fit_local_surrogate(**kwargs)
         else:
diff --git a/elfi/methods/posteriors.py b/elfi/methods/posteriors.py
index e396d6f3..2e49a349 100644
--- a/elfi/methods/posteriors.py
+++ b/elfi/methods/posteriors.py
@@ -320,7 +320,8 @@ def __init__(self,
         self.parallelize = parallelize
         self.partition = None
 
-        self.progress_bar = ProgressBar()
+        self.progress_bar = ProgressBar(prefix='Progress', suffix='Complete',
+                                        decimals=1, length=50, fill='=')
 
     def _pdf_unnorm_single_point(self, theta: np.ndarray) -> float:
         """Evaluate the unnormalised pdf, at a single input point.
@@ -588,11 +589,12 @@ def sample(self, n2: int, seed=None) -> (np.ndarray, np.ndarray):
         if self.parallelize is False:
             w = []
             distances = []
+            self.progress_bar.reinit_progressbar(reinit_msg="Sampling posterior regions")
             for i in range(nof_regions):
                 w.append([])
                 # indicator_region = self.regions[i].contains
                 for j in range(n2):
-                    self.progress_bar.update_progressbar(i * n2 + j, nof_regions * n2)
+                    self.progress_bar.update_progressbar(i * n2 + j + 1, nof_regions * n2)
                     cur_theta = theta[i, j]
                     q = regions[i].pdf(cur_theta)
                     if q == 0.0: