diff --git a/ignite/metrics/hsic.py b/ignite/metrics/hsic.py
index e144ed8ac43..5e6adbbc084 100644
--- a/ignite/metrics/hsic.py
+++ b/ignite/metrics/hsic.py
@@ -18,14 +18,14 @@ class HSIC(Metric):
          + \frac{\mathbf{1}^\top \tilde{\mathbf{K}} \mathbf{11}^\top \tilde{\mathbf{L}} \mathbf{1}}{(B-1)(B-2)}
          -\frac{2}{B-2}\mathbf{1}^\top \tilde{\mathbf{K}}\tilde{\mathbf{L}} \mathbf{1}  \right]
 
-    where :math:`\tilde{\mathbf{K}}` and :math:`\tilde{\mathbf{L}}` are the Gram matrices of
+    where :math:`B` is the batch size, and :math:`\tilde{\mathbf{K}}` and :math:`\tilde{\mathbf{L}}` are the Gram matrices of
     the Gaussian RBF kernel with their diagonal entries being set to zero.
 
     HSIC measures non-linear statistical independence between features :math:`X` and :math:`Y`.
     HSIC becomes zero if and only if :math:`X` and :math:`Y` are independent.
 
     This metric computes the unbiased estimator of HSIC proposed in
-    `Song et al., 2012 <https://jmlr.csail.mit.edu/papers/v13/song12a.html>`_
+    `Song et al. (2012) <https://jmlr.csail.mit.edu/papers/v13/song12a.html>`_
     for each batch and accumulates the average.
 
     Each batch must contain at least four samples.