diff --git a/frouros/detectors/data_drift/batch/distance_based/mmd.py b/frouros/detectors/data_drift/batch/distance_based/mmd.py
index 61708ba..928ff35 100644
--- a/frouros/detectors/data_drift/batch/distance_based/mmd.py
+++ b/frouros/detectors/data_drift/batch/distance_based/mmd.py
@@ -51,10 +51,6 @@ def __init__(
         )
         self.kernel = kernel
         self.chunk_size = chunk_size
-        self._chunk_size_x = None
-        self.X_chunks_combinations = None
-        self.X_num_samples = None
-        self.expected_k_xx = None
 
     @property
     def chunk_size(self) -> Optional[int]:
@@ -108,55 +104,16 @@ def _distance_measure(
         X: np.ndarray,  # noqa: N803
         **kwargs,
     ) -> DistanceResult:
-        mmd = self._mmd(X=X_ref, Y=X, kernel=self.kernel, **kwargs)
+        mmd = self._mmd(
+            X=X_ref,
+            Y=X,
+            kernel=self.kernel,
+            chunk_size=self.chunk_size,
+            **kwargs,
+        )
         distance_test = DistanceResult(distance=mmd)
         return distance_test
 
-    def _fit(
-        self,
-        X: np.ndarray,  # noqa: N803
-        **kwargs,
-    ) -> None:
-        super()._fit(X=X)
-        # Add dimension only for the kernel calculation (if dim == 1)
-        if X.ndim == 1:
-            X = np.expand_dims(X, axis=1)  # noqa: N806
-        self.X_num_samples = len(self.X_ref)  # type: ignore # noqa: N806
-
-        self._chunk_size_x = (
-            self.X_num_samples
-            if self.chunk_size is None
-            else self.chunk_size  # type: ignore
-        )
-
-        X_chunks = self._get_chunks(  # noqa: N806
-            data=X,
-            chunk_size=self._chunk_size_x,  # type: ignore
-        )
-        xx_chunks_combinations = itertools.product(X_chunks, repeat=2)  # noqa: N806
-
-        if kwargs.get("verbose", False):
-            num_chunks = (
-                math.ceil(self.X_num_samples / self._chunk_size_x) ** 2  # type: ignore
-            )
-            xx_chunks_combinations = tqdm.tqdm(
-                xx_chunks_combinations,
-                total=num_chunks,
-            )
-
-        k_xx_sum = (
-            self._compute_kernel(
-                chunk_combinations=xx_chunks_combinations,  # type: ignore
-                kernel=self.kernel,
-            )
-            # Remove diagonal (j!=i case)
-            - self.X_num_samples  # type: ignore
-        )
-
-        self.expected_k_xx = k_xx_sum / (  # type: ignore
-            self.X_num_samples * (self.X_num_samples - 1)  # type: ignore
-        )
-
     @staticmethod
     def _compute_kernel(chunk_combinations: Generator, kernel: Callable) -> float:
         k_sum = np.array([kernel(*chunk).sum() for chunk in chunk_combinations]).sum()
@@ -170,8 +127,8 @@ def _get_chunks(data: np.ndarray, chunk_size: int) -> Generator:
         )
         return chunks
 
+    @staticmethod
     def _mmd(  # pylint: disable=too-many-locals
-        self,
         X: np.ndarray,  # noqa: N803
         Y: np.ndarray,
         *,
@@ -183,33 +140,56 @@ def _mmd(  # pylint: disable=too-many-locals
             X = np.expand_dims(X, axis=1)  # noqa: N806
             Y = np.expand_dims(Y, axis=1)  # noqa: N806
 
-        X_chunks = self._get_chunks(  # noqa: N806
-            data=X,
-            chunk_size=self._chunk_size_x,  # type: ignore
+        x_num_samples = len(X)  # noqa: N806
+        chunk_size_x = (
+            kwargs["chunk_size"]
+            if "chunk_size" in kwargs and kwargs["chunk_size"] is not None
+            else x_num_samples
+        )
+        x_chunks, x_chunks_copy = itertools.tee(  # noqa: N806
+            MMD._get_chunks(
+                data=X,
+                chunk_size=chunk_size_x,  # type: ignore
+            ),
+            2,
         )
         y_num_samples = len(Y)  # noqa: N806
-        chunk_size_y = y_num_samples if self.chunk_size is None else self.chunk_size
+        chunk_size_y = (
+            kwargs["chunk_size"]
+            if "chunk_size" in kwargs and kwargs["chunk_size"] is not None
+            else y_num_samples
+        )
         y_chunks, y_chunks_copy = itertools.tee(  # noqa: N806
-            self._get_chunks(
+            MMD._get_chunks(
                 data=Y,
                 chunk_size=chunk_size_y,  # type: ignore
             ),
             2,
         )
+        x_chunks_combinations = itertools.product(  # noqa: N806
+            x_chunks,
+            repeat=2,
+        )
         y_chunks_combinations = itertools.product(  # noqa: N806
             y_chunks,
             repeat=2,
         )
         xy_chunks_combinations = itertools.product(  # noqa: N806
-            X_chunks,
+            x_chunks_copy,
             y_chunks_copy,
         )
 
         if kwargs.get("verbose", False):
+            num_chunks_x = math.ceil(x_num_samples / chunk_size_x)  # type: ignore
             num_chunks_y = math.ceil(y_num_samples / chunk_size_y)  # type: ignore
+            num_chunks_x_combinations = num_chunks_x**2
             num_chunks_y_combinations = num_chunks_y**2
             num_chunks_xy = (
-                math.ceil(len(X) / self._chunk_size_x) * num_chunks_y  # type: ignore
+                math.ceil(len(X) / chunk_size_x) * num_chunks_y  # type: ignore
+            )
+            x_chunks_combinations = tqdm.tqdm(
+                x_chunks_combinations,
+                total=num_chunks_x_combinations,
             )
             y_chunks_combinations = tqdm.tqdm(
                 y_chunks_combinations,
@@ -220,21 +200,29 @@ def _mmd(  # pylint: disable=too-many-locals
                 total=num_chunks_xy,
             )
 
+        k_xx_sum = (
+            MMD._compute_kernel(
+                chunk_combinations=x_chunks_combinations,  # type: ignore
+                kernel=kernel,
+            )
+            # Remove diagonal (j!=i case)
+            - x_num_samples  # type: ignore
+        )
         k_yy_sum = (
-            self._compute_kernel(
+            MMD._compute_kernel(
                 chunk_combinations=y_chunks_combinations,  # type: ignore
                 kernel=kernel,
             )
             # Remove diagonal (j!=i case)
             - y_num_samples  # type: ignore
         )
-        k_xy_sum = self._compute_kernel(
+        k_xy_sum = MMD._compute_kernel(
             chunk_combinations=xy_chunks_combinations,  # type: ignore
             kernel=kernel,
         )
         mmd = (
-            self.expected_k_xx  # type: ignore
+            +k_xx_sum / (x_num_samples * (x_num_samples - 1))
             + k_yy_sum / (y_num_samples * (y_num_samples - 1))
-            - 2 * k_xy_sum / (self.X_num_samples * y_num_samples)  # type: ignore
+            - 2 * k_xy_sum / (x_num_samples * y_num_samples)  # type: ignore
         )
         return mmd