diff --git a/hyperit/hyperit.py b/hyperit/hyperit.py
index ca46731..a7d9ed5 100644
--- a/hyperit/hyperit.py
+++ b/hyperit/hyperit.py
@@ -447,9 +447,6 @@ def __which_estimator(self, measure: str) -> None:
                 self._Calc.setProperty(key, value)
 
         if initialise_parameter:
-            if self._measure == MeasureType.TE:
-                dataDim = self._data1.ndim
-                self._initialise_parameter = (dataDim, dataDim, *initialise_parameter)
             self._initialise_parameter = initialise_parameter
 
         if self._measure == MeasureType.TE and self._estimator_name == 'kernel':
@@ -495,19 +492,24 @@ def __setup_matrix(self) -> None:
         
         self._it_matrix = np.zeros((1, self._loop_range, self._loop_range, 16)) if self._epoch_average else np.zeros((self._n_epo, self._loop_range, self._loop_range, 16))
         
-
-    def __estimate_it(self, s1: np.ndarray, s2: np.ndarray) -> float | np.ndarray:
-        """ Estimates Mutual Information or Transfer Entropy for a pair of time-series signals using JIDT estimators. """
-
-        # Initialise parameter describes the dimensions of the data
+    def __initialise_estimator(self, s1: np.ndarray, s2: np.ndarray) -> None:
         
         if not self._initialise_parameter:
             self._Calc.initialise()
-        else:
-            if self._measure == MeasureType.TE and self._estimator == 'symbolic': # symbolic estimator takes only one argument so cannot be unrolled.
+            return
+
+        if self._measure == MeasureType.TE:
+            if self._estimator == 'symbolic': # symbolic estimator takes only one argument so cannot be unrolled.
                 self._Calc.initialise(self._initialise_parameter) 
-            else:
-                self._Calc.initialise(*self._initialise_parameter)
+                return
+            
+            self._Calc.initialise(s1.ndim, s2.ndim, *self._initialise_parameter)
+            return
+        
+        self._Calc.initialise(*self._initialise_parameter)
+    
+    def __estimate_it(self, s1: np.ndarray, s2: np.ndarray) -> float | np.ndarray:
+        """ Estimates Mutual Information or Transfer Entropy for a pair of time-series signals using JIDT estimators. """
                 
         self._Calc.setObservations(setup_JArray(s1), setup_JArray(s2))
         result = self._Calc.computeAverageLocalOfObservations() * np.log(2)
@@ -522,14 +524,6 @@ def __estimate_it(self, s1: np.ndarray, s2: np.ndarray) -> float | np.ndarray:
     def __estimate_it_epoch_average(self, s1: np.ndarray, s2: np.ndarray) -> float:
         """ Estimates Mutual Information or Transfer Entropy for a pair of time-series signals using JIDT estimators. 
             s1 and s2 should have shape (epochs, samples) referring to a pairwise comparison of two channels."""
-
-        if not self._initialise_parameter:
-            self._Calc.initialise()
-        else:
-            if self._measure == MeasureType.TE and self._estimator == 'symbolic': # symbolic estimator takes only one argument so cannot be unrolled.
-                self._Calc.initialise(self._initialise_parameter) 
-            else:
-                self._Calc.initialise(*self._initialise_parameter)
             
         self._Calc.startAddObservations()
         for epoch in range(self._n_epo):
@@ -574,6 +568,8 @@ def __filter_estimation(self, s1: np.ndarray, s2: np.ndarray) -> float | np.ndar
         elif self._measure == MeasureType.PhyID:
             return self.__estimate_atoms(s1, s2)
 
+        __initialise_estimator(s1, s2)
+
         return self.__estimate_it_epoch_average(s1, s2) if self._epoch_average else self.__estimate_it(s1, s2)