diff --git a/src/deepali/losses/functional.py b/src/deepali/losses/functional.py
index 0bff564..aa00444 100644
--- a/src/deepali/losses/functional.py
+++ b/src/deepali/losses/functional.py
@@ -1038,11 +1038,11 @@ def mi_loss(
 
     # Random image samples, optionally weighted by mask
     if sample_ratio is not None:
-        if num_samples is not None:
-            raise ValueError("mi_loss() 'num_samples' and 'sample_ratio' are mutually exclusive")
         if sample_ratio <= 0 or sample_ratio > 1:
             raise ValueError("mi_loss() 'sample_ratio' must be in open-closed interval (0, 1]")
-        num_samples = max(1, int(sample_ratio * target.shape[2:].numel()))
+        if num_samples is None:
+            num_samples = target.shape[2:].numel()
+        num_samples = min(max(1, int(sample_ratio * target.shape[2:].numel())), num_samples)
     if num_samples is not None:
         input, target = rand_sample([input, target], num_samples, mask=mask, replacement=True)
     elif mask is not None:
@@ -1066,12 +1066,12 @@ def parzen_window_fn(x: Tensor) -> Tensor:
 
     # calculate joint histogram
     hist_joint = pw_input.bmm(pw_target.transpose(1, 2))  # (N, #bins, #bins)
-    hist_norm = hist_joint.flatten(start_dim=1, end_dim=-1).sum(dim=1) + 1e-5
+    hist_norm = hist_joint.flatten(start_dim=1, end_dim=-1).sum(dim=1).add_(1e-5)
 
     # joint and marginal distributions
     p_joint = hist_joint / hist_norm.view(-1, 1, 1)  # (N, #bins, #bins) / (N, 1, 1)
-    p_input = torch.sum(p_joint, dim=2)
-    p_target = torch.sum(p_joint, dim=1)
+    p_input = p_joint.sum(dim=2)
+    p_target = p_joint.sum(dim=1)
 
     # calculate entropy
     ent_input = -torch.sum(p_input * torch.log(p_input + 1e-5), dim=1)  # (N,1)