diff --git a/nitorch/vb/mixtures.py b/nitorch/vb/mixtures.py
index a06eee7d..aea44df0 100644
--- a/nitorch/vb/mixtures.py
+++ b/nitorch/vb/mixtures.py
@@ -33,7 +33,7 @@ def __init__(self, num_class=2):
 
     # Functions
     def fit(self, X, verbose=1, max_iter=10000, tol=1e-8, fig_num=1, W=None,
-            show_fit=False):
+            show_fit=False, samp=1):
         """ Fit mixture model.
         Args:
             X (torch.tensor): Observed data (N, C).
@@ -50,17 +50,34 @@ def fit(self, X, verbose=1, max_iter=10000, tol=1e-8, fig_num=1, W=None,
             fig_num (int, optional): Defaults to 1.
             W (torch.tensor, optional): Observation weights (N, 1). Defaults to no weights.
             show_fit (bool, optional): Plot mixture fit, defaults to False.
+            samp (int, optional): Sub-sampling, defaults to 1.
         Returns:
             Z (torch.tensor): Responsibilities (N, K).
         """
+        if not isinstance(samp, int) or samp < 1:
+            raise ValueError(f"samp parameter needs to be an int >= 1, got {samp}")
+        
         if verbose:
             t0 = timer()  # Start timer
 
         # Set random seed
         torch.manual_seed(1)
 
+        if torch.is_floating_point(X) == False:
+            # Integer data type -> convert to float and add some noise
+            X = X.type(torch.float)
+            X += (0.001*X.max())*torch.randn_like(X)
+            
         self.dev = X.device
         self.dt = X.dtype
+        
+        if samp > 1:
+            # Sub-sample
+            X0 = X[::1, :]  # Ensures copy
+            X = X[::samp, :]
+            if W is not None:
+                W0 = W[::1, :]  # Ensures copy
+                W = W[::samp, :]
 
         if len(X.shape) == 1:
             X = X[:, None]
@@ -86,6 +103,16 @@ def fit(self, X, verbose=1, max_iter=10000, tol=1e-8, fig_num=1, W=None,
         # EM loop
         Z, lb = self._em(X, max_iter=max_iter, tol=tol, verbose=verbose, W=W)
 
+        if samp > 1:
+            # Create original resolution responsibilites
+            X = X0
+            N = X.shape[0]
+            Z = torch.zeros((N, K), dtype=self.dt, device=self.dev)
+            for k in range(K):
+                Z[:, k] = torch.log(self.mp[k]) + self._log_likelihood(X, k)
+            if W is not None:  W = W0
+            Z, _ = softmax_lse(Z, lse=True, weights=W)
+
         # Print algorithm info
         if verbose >= 1:
             print('Algorithm finished in {} iterations, '
@@ -277,7 +304,7 @@ def full_resp(Z, msk, dm=[]):
         """ Converts masked responsibilities to full.
         Args:
             Z (torch.tensor): Masked responsibilities (N, K).
-            msk (torch.tensor): Mask of original data (N0, 1).
+            msk (torch.tensor): Mask of original data (N0, ).
             dm (torch.Size, optional): Reshapes Z_full using dm. Defaults to [].
         Returns:
             Z_full (torch.tensor): Full responsibilities (N0, K).