diff --git a/pyemma/thermo/estimators/DTRAM_estimator.py b/pyemma/thermo/estimators/DTRAM_estimator.py
index 02150f6ef..1efb8d05e 100644
--- a/pyemma/thermo/estimators/DTRAM_estimator.py
+++ b/pyemma/thermo/estimators/DTRAM_estimator.py
@@ -116,9 +116,9 @@ class DTRAM(_Estimator, _MEMM, _ProgressReporter):
 
     """
     def __init__(
-        self, bias_energies_full, lag, count_mode='sliding', connectivity='largest',
-        maxiter=10000, maxerr=1.0E-15, save_convergence_info=0, dt_traj='1 step',
-        init=None, init_maxiter=10000, init_maxerr=1.0E-8):
+            self, bias_energies_full, lag, count_mode='sliding', connectivity='largest',
+            maxiter=10000, maxerr=1.0E-15, save_convergence_info=0, dt_traj='1 step',
+            init=None, init_maxiter=10000, init_maxerr=1.0E-8):
         # set all parameters
         self.bias_energies_full = _types.ensure_ndarray(bias_energies_full, ndim=2, kind='numeric')
         self.lag = lag
@@ -142,21 +142,25 @@ def __init__(
         self.conf_energies = None
         self.log_lagrangian_mult = None
 
-    def _estimate(self, trajs):
+    def estimate(self, trajs):
         """
         Parameters
         ----------
         X : tuple of (ttrajs, dtrajs)
             Simulation trajectories. ttrajs contain the indices of the thermodynamic state and
             dtrajs contains the indices of the configurational states.
-        ttrajs : list of numpy.ndarray(X_i, dtype=int)
-            Every elements is a trajectory (time series). ttrajs[i][t] is the index of the
-            thermodynamic state visited in trajectory i at time step t.
-        dtrajs : list of numpy.ndarray(X_i, dtype=int)
-            dtrajs[i][t] is the index of the configurational state (Markov state) visited in
-            trajectory i at time step t.
+
+            ttrajs : list of numpy.ndarray(X_i, dtype=int)
+                Every elements is a trajectory (time series). ttrajs[i][t] is the index of the
+                thermodynamic state visited in trajectory i at time step t.
+            dtrajs : list of numpy.ndarray(X_i, dtype=int)
+                dtrajs[i][t] is the index of the configurational state (Markov state) visited in
+                trajectory i at time step t.
 
         """
+        return super(DTRAM, self).estimate(trajs)
+
+    def _estimate(self, trajs):
         # check input
         assert isinstance(trajs, (tuple, list))
         assert len(trajs) == 2
@@ -207,14 +211,14 @@ def _estimate(self, trajs):
 
         # run estimator
         self.therm_energies, self.conf_energies, self.log_lagrangian_mult, \
-            self.increments, self.loglikelihoods = _dtram.estimate(
-                self.count_matrices, self.bias_energies,
-                maxiter=self.maxiter, maxerr=self.maxerr,
-                log_lagrangian_mult=self.log_lagrangian_mult,
-                conf_energies=self.conf_energies,
-                save_convergence_info=self.save_convergence_info,
-                callback=_ConvergenceProgressIndicatorCallBack(
-                    self, 'DTRAM', self.maxiter, self.maxerr))
+        self.increments, self.loglikelihoods = _dtram.estimate(
+            self.count_matrices, self.bias_energies,
+            maxiter=self.maxiter, maxerr=self.maxerr,
+            log_lagrangian_mult=self.log_lagrangian_mult,
+            conf_energies=self.conf_energies,
+            save_convergence_info=self.save_convergence_info,
+            callback=_ConvergenceProgressIndicatorCallBack(
+                self, 'DTRAM', self.maxiter, self.maxerr))
         self._progress_force_finish(stage='DTRAM', description='DTRAM')
 
         # compute models
@@ -242,4 +246,4 @@ def log_likelihood(self):
                 self.bias_energies,
                 self.conf_energies,
                 self.count_matrices,
-                _np.zeros(shape=self.conf_energies.shape, dtype=_np.float64)))
+                _np.zeros(shape=self.conf_energies.shape, dtype=_np.float64)))
\ No newline at end of file
diff --git a/pyemma/thermo/estimators/TRAM_estimator.py b/pyemma/thermo/estimators/TRAM_estimator.py
index 6db388e1f..c4f0b6820 100644
--- a/pyemma/thermo/estimators/TRAM_estimator.py
+++ b/pyemma/thermo/estimators/TRAM_estimator.py
@@ -151,24 +151,28 @@ def __init__(
         self.log_lagrangian_mult = None
         self.loglikelihoods = None
 
-    def _estimate(self, X):
+    def estimate(self, X):
         """
         Parameters
         ----------
         X : tuple of (ttrajs, dtrajs, btrajs)
             Simulation trajectories. ttrajs contain the indices of the thermodynamic state, dtrajs
             contains the indices of the configurational states and btrajs contain the biases.
-        ttrajs : list of numpy.ndarray(X_i, dtype=int)
-            Every elements is a trajectory (time series). ttrajs[i][t] is the index of the
-            thermodynamic state visited in trajectory i at time step t.
-        dtrajs : list of numpy.ndarray(X_i, dtype=int)
-            dtrajs[i][t] is the index of the configurational state (Markov state) visited in
-            trajectory i at time step t.
-        btrajs : list of numpy.ndarray((X_i, T), dtype=numpy.float64)
-            For every simulation frame seen in trajectory i and time step t, btrajs[i][t,k] is the
-            bias energy of that frame evaluated in the k'th thermodynamic state (i.e. at the k'th
-            Umbrella/Hamiltonian/temperature).
+
+            ttrajs : list of numpy.ndarray(X_i, dtype=int)
+                Every elements is a trajectory (time series). ttrajs[i][t] is the index of the
+                thermodynamic state visited in trajectory i at time step t.
+            dtrajs : list of numpy.ndarray(X_i, dtype=int)
+                dtrajs[i][t] is the index of the configurational state (Markov state) visited in
+                trajectory i at time step t.
+            btrajs : list of numpy.ndarray((X_i, T), dtype=numpy.float64)
+                For every simulation frame seen in trajectory i and time step t, btrajs[i][t,k] is the
+                bias energy of that frame evaluated in the k'th thermodynamic state (i.e. at the k'th
+                Umbrella/Hamiltonian/temperature).
         """
+        return super(TRAM, self).estimate(trajs)
+
+    def _estimate(self, X):
         ttrajs, dtrajs_full, btrajs = X
         # shape and type checks
         assert len(ttrajs) == len(dtrajs_full) == len(btrajs)
diff --git a/pyemma/thermo/estimators/WHAM_estimator.py b/pyemma/thermo/estimators/WHAM_estimator.py
index e0dceb0ef..a81d81337 100644
--- a/pyemma/thermo/estimators/WHAM_estimator.py
+++ b/pyemma/thermo/estimators/WHAM_estimator.py
@@ -108,21 +108,24 @@ def __init__(
         self.therm_energies = None
         self.conf_energies = None
 
-    def _estimate(self, trajs):
-        # TODO: fix docstring
+    def estimate(self, trajs):
         """
         Parameters
         ----------
         X : tuple of (ttrajs, dtrajs)
             Simulation trajectories. ttrajs contain the indices of the thermodynamic state and
             dtrajs contains the indices of the configurational states.
-        ttrajs : list of numpy.ndarray(X_i, dtype=int)
-            Every elements is a trajectory (time series). ttrajs[i][t] is the index of the
-            thermodynamic state visited in trajectory i at time step t.
-        dtrajs : list of numpy.ndarray(X_i, dtype=int)
-            dtrajs[i][t] is the index of the configurational state (Markov state) visited in
-            trajectory i at time step t.
+
+            ttrajs : list of numpy.ndarray(X_i, dtype=int)
+                Every elements is a trajectory (time series). ttrajs[i][t] is the index of the
+                thermodynamic state visited in trajectory i at time step t.
+            dtrajs : list of numpy.ndarray(X_i, dtype=int)
+                dtrajs[i][t] is the index of the configurational state (Markov state) visited in
+                trajectory i at time step t.
         """
+        return super(WHAM, self).estimate(trajs)
+
+    def _estimate(self, trajs):
         # check input
         assert isinstance(trajs, (tuple, list))
         assert len(trajs) == 2