diff --git a/GPy/examples/uncertain_input_GP_regression_demo.py b/GPy/examples/uncertain_input_GP_regression_demo.py
index 3d2c51f0..f0be5fe2 100644
--- a/GPy/examples/uncertain_input_GP_regression_demo.py
+++ b/GPy/examples/uncertain_input_GP_regression_demo.py
@@ -1,7 +1,6 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-
 import pylab as pb
 import numpy as np
 import GPy
@@ -9,18 +8,15 @@ pb.ion()
 pb.close('all')
 
 
-######################################
-## 1 dimensional example
-
 # sample inputs and outputs
 S = np.ones((20,1))
 X = np.random.uniform(-3.,3.,(20,1))
 Y = np.sin(X)+np.random.randn(20,1)*0.05
 
-k = GPy.kern.bias(1) + GPy.kern.white(1)
+k = GPy.kern.rbf(1) + GPy.kern.white(1)
 
 # create simple GP model
-m = GPy.models.uncertain_input_GP_regression(X,Y,S,kernel=k)
+m = GPy.models.sparse_GP_regression(X,Y,X_uncertainty=S,kernel=k)
 
 # contrain all parameters to be positive
 m.constrain_positive('(variance|prec)')
diff --git a/GPy/models/sparse_GPLVM.py b/GPy/models/sparse_GPLVM.py
index 96c3e530..c5125c85 100644
--- a/GPy/models/sparse_GPLVM.py
+++ b/GPy/models/sparse_GPLVM.py
@@ -54,5 +54,6 @@ class sparse_GPLVM(sparse_GP_regression, GPLVM):
 
     def plot(self):
         GPLVM.plot(self)
+        #passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
         mu, var = sparse_GP_regression.predict(self, self.Z+np.random.randn(*self.Z.shape)*0.0001)
         pb.plot(mu[:, 0] , mu[:, 1], 'ko')
diff --git a/GPy/models/sparse_GP_regression.py b/GPy/models/sparse_GP_regression.py
index da19d80a..f34819dc 100644
--- a/GPy/models/sparse_GP_regression.py
+++ b/GPy/models/sparse_GP_regression.py
@@ -1,7 +1,6 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-
 import numpy as np
 import pylab as pb
 from ..util.linalg import mdot, jitchol, chol_inv, pdinv
@@ -50,7 +49,7 @@ class sparse_GP_regression(GP_regression):
             self.has_uncertain_inputs=False
         else:
             assert X_uncertainty.shape==X.shape
-            self.has_uncertain_inputs=False
+            self.has_uncertain_inputs=True
             self.X_uncertainty = X_uncertainty
 
         GP_regression.__init__(self, X, Y, kernel=kernel, normalize_X=normalize_X, normalize_Y=normalize_Y)
@@ -99,8 +98,7 @@ class sparse_GP_regression(GP_regression):
 
         # Compute dL_dpsi
         self.dL_dpsi0 = - 0.5 * self.D * self.beta * np.ones(self.N)
-        dC_dpsi1 = (self.LLambdai.T[:,:, None, None] * self.V)
-        self.dL_dpsi1 = (dC_dpsi1*self.C[None,:,None,:]).sum(1).sum(-1)
+        self.dL_dpsi1 = (self.LLambdai.T[:,:,None,None]*self.V*self.C[None,:,None,:]).sum(1).sum(-1)
         self.dL_dpsi2 = - 0.5 * self.beta * (self.D*(self.LBL_inv - self.Kmmi) + self.G)
 
         # Compute dL_dKmm