diff --git a/nbs/losses.pytorch.ipynb b/nbs/losses.pytorch.ipynb
index d3ff48894..65dfc7605 100644
--- a/nbs/losses.pytorch.ipynb
+++ b/nbs/losses.pytorch.ipynb
@@ -1454,8 +1454,8 @@
     "    mu = F.softplus(mu) + 1e-8\n",
     "    alpha = F.softplus(alpha) + 1e-8    # alpha = 1/total_counts\n",
     "    if (loc is not None) and (scale is not None):\n",
-    "        mu *= loc\n",
-    "        alpha /= (loc + 1.)\n",
+    "        mu = mu * scale + loc\n",
+    "        alpha /= (scale + 1.)\n",
     "\n",
     "    # mu = total_count * (probs/(1-probs))\n",
     "    # => probs = mu / (total_count + mu)\n",
diff --git a/neuralforecast/losses/pytorch.py b/neuralforecast/losses/pytorch.py
index f24d2e5bc..03b1a1345 100644
--- a/neuralforecast/losses/pytorch.py
+++ b/neuralforecast/losses/pytorch.py
@@ -857,8 +857,8 @@ def nbinomial_scale_decouple(output, loc=None, scale=None):
     mu = F.softplus(mu) + 1e-8
     alpha = F.softplus(alpha) + 1e-8  # alpha = 1/total_counts
     if (loc is not None) and (scale is not None):
-        mu *= loc
-        alpha /= loc + 1.0
+        mu = mu * scale + loc
+        alpha /= scale + 1.0
 
     # mu = total_count * (probs/(1-probs))
     # => probs = mu / (total_count + mu)