diff --git a/normalizing_flows/bijections/finite/residual/log_abs_det_estimators.py b/normalizing_flows/bijections/finite/residual/log_abs_det_estimators.py
index 5be083e..7c64e87 100644
--- a/normalizing_flows/bijections/finite/residual/log_abs_det_estimators.py
+++ b/normalizing_flows/bijections/finite/residual/log_abs_det_estimators.py
@@ -21,7 +21,7 @@ def hutchinson_log_abs_det_estimator(g: callable,
     assert n_iterations >= 2
 
     w = noise  # (batch_size, event_size, n_hutchinson_samples)
-    log_abs_det_jac_f = torch.zeros(size=(batch_size, n_hutchinson_samples))  # this will be averaged at the end
+    log_abs_det_jac_f = torch.zeros(size=(batch_size,))
     g_value = None
     for k in range(1, n_iterations + 1):
         # Compute VJP, reshape appropriately for hutchinson averaging
@@ -35,9 +35,9 @@ def hutchinson_log_abs_det_estimator(g: callable,
         ws = list(ws)
         # (batch_size, event_size, n_hutchinson_samples)
         w = torch.concatenate([ws[i][..., None] for i in range(n_hutchinson_samples)], dim=2)
-        log_abs_det_jac_f += (-1) ** (k + 1) / k * torch.sum(w * noise, dim=1)  # sum over event dim
-        assert log_abs_det_jac_f.shape == (batch_size, n_hutchinson_samples)
-    log_abs_det_jac_f = torch.mean(log_abs_det_jac_f, dim=1)  # hutchinson averaging over the many different series
+        log_abs_det_jac_f += (-1) ** (k + 1) / k * torch.sum(w * noise, dim=1).mean(
+            dim=1)  # sum over event dim, average over hutchinson dim
+        assert log_abs_det_jac_f.shape == (batch_size,)
     return g_value, log_abs_det_jac_f