diff --git a/GPy/core/parameterised.py b/GPy/core/parameterised.py
index 007f1b25..b5d880a3 100644
--- a/GPy/core/parameterised.py
+++ b/GPy/core/parameterised.py
@@ -56,7 +56,7 @@ class parameterised(object):
         return copy.deepcopy(self)
 
 
-    def tie_param(self, which):
+    def tie_params(self, which):
         matches = self.grep_param_names(which)
         assert matches.size > 0, "need at least something to tie together"
         if len(self.tied_indices):
diff --git a/GPy/examples/classification.py b/GPy/examples/classification.py
index 77bd0b79..1d101d76 100644
--- a/GPy/examples/classification.py
+++ b/GPy/examples/classification.py
@@ -62,7 +62,7 @@ def oil():
 
     # Contrain all parameters to be positive
     m.constrain_positive('')
-    m.tie_param('lengthscale')
+    m.tie_params('lengthscale')
     m.update_likelihood_approximation()
 
     # Optimize
diff --git a/GPy/examples/tutorials.py b/GPy/examples/tutorials.py
index 9d892b8e..2bc9ba60 100644
--- a/GPy/examples/tutorials.py
+++ b/GPy/examples/tutorials.py
@@ -138,7 +138,7 @@ def tuto_kernel_overview():
 
     k.constrain_positive('var')
     k.constrain_fixed(np.array([1]),1.75)
-    k.tie_param('len')
+    k.tie_params('len')
     k.unconstrain('white')
     k.constrain_bounded('white',lower=1e-5,upper=.5)
     print k
diff --git a/GPy/kern/kern.py b/GPy/kern/kern.py
index b2970674..be45fa70 100644
--- a/GPy/kern/kern.py
+++ b/GPy/kern/kern.py
@@ -237,7 +237,7 @@ class kern(parameterised):
         for i in range(K1.Nparam + K2.Nparam):
             index = np.where(index_param==i)[0]
             if index.size > 1:
-                self.tie_param(index)
+                self.tie_params(index)
         for i in prev_constr_pos:
             self.constrain_positive(np.where(index_param==i)[0])
         for i in prev_constr_neg:
@@ -391,9 +391,13 @@ class kern(parameterised):
                 target += p2.variance*(p1._psi1[:,:,None]+p1._psi1[:,None,:])
             #linear X bias
             elif p1.name=='bias' and p2.name=='linear':
-                raise NotImplementedError
+                tmp = np.zeros((mu.shape[0],Z.shape[0]))
+                p2.psi1(Z,mu,S,tmp)
+                target += p1.variance*(tmp[:,:,None] + tmp[:,None,:])
             elif p2.name=='bias' and p1.name=='linear':
-                raise NotImplementedError
+                tmp = np.zeros((mu.shape[0],Z.shape[0]))
+                p1.psi1(Z,mu,S,tmp)
+                target += p2.variance*(tmp[:,:,None] + tmp[:,None,:])
             #rbf X linear
             elif p1.name=='linear' and p2.name=='rbf':
                 raise NotImplementedError #TODO
@@ -426,6 +430,11 @@ class kern(parameterised):
             elif p2.name=='bias' and p1.name=='rbf':
                 p1.dpsi1_dtheta(dL_dpsi2.sum(1)*p2.variance*2.,Z,mu,S,target[ps1])
                 p2.dpsi1_dtheta(dL_dpsi2.sum(1)*p1._psi1*2.,Z,mu,S,target[ps2])
+            #linear X bias
+            elif p1.name=='bias' and p2.name=='linear':
+                p2.dpsi1_dtheta(dL_dpsi2.sum(1)*p1.variance*2., Z, mu, S, target[ps1])
+            elif p2.name=='bias' and p1.name=='linear':
+                p1.dpsi1_dtheta(dL_dpsi2.sum(1)*p2.variance*2., Z, mu, S, target[ps1])
             #rbf X linear
             elif p1.name=='linear' and p2.name=='rbf':
                 raise NotImplementedError #TODO
@@ -451,6 +460,11 @@ class kern(parameterised):
                 p2.dpsi1_dX(dL_dpsi2.sum(1).T*p1.variance,Z,mu,S,target)
             elif p2.name=='bias' and p1.name=='rbf':
                 p1.dpsi1_dZ(dL_dpsi2.sum(1).T*p2.variance,Z,mu,S,target)
+            #linear X bias
+            elif p1.name=='bias' and p2.name=='linear':
+                p2.dpsi1_dZ(dL_dpsi2.sum(1).T*p1.variance, Z, mu, S, target)
+            elif p2.name=='bias' and p1.name=='linear':
+                p1.dpsi1_dZ(dL_dpsi2.sum(1).T*p2.variance, Z, mu, S, target)
             #rbf X linear
             elif p1.name=='linear' and p2.name=='rbf':
                 raise NotImplementedError #TODO
@@ -478,6 +492,11 @@ class kern(parameterised):
                 p2.dpsi1_dmuS(dL_dpsi2.sum(1).T*p1.variance*2.,Z,mu,S,target_mu,target_S)
             elif p2.name=='bias' and p1.name=='rbf':
                 p1.dpsi1_dmuS(dL_dpsi2.sum(1).T*p2.variance*2.,Z,mu,S,target_mu,target_S)
+            #linear X bias
+            elif p1.name=='bias' and p2.name=='linear':
+                p2.dpsi1_dmuS(dL_dpsi2.sum(1).T*p1.variance*2., Z, mu, S, target_mu, target_S)
+            elif p2.name=='bias' and p1.name=='linear':
+                p1.dpsi1_dmuS(dL_dpsi2.sum(1).T*p2.variance*2., Z, mu, S, target_mu, target_S)
             #rbf X linear
             elif p1.name=='linear' and p2.name=='rbf':
                 raise NotImplementedError #TODO
diff --git a/GPy/testing/bgplvm_tests.py b/GPy/testing/bgplvm_tests.py
index dda92b90..b182c1a8 100644
--- a/GPy/testing/bgplvm_tests.py
+++ b/GPy/testing/bgplvm_tests.py
@@ -58,7 +58,7 @@ class BGPLVMTests(unittest.TestCase):
         m.randomize()
         self.assertTrue(m.checkgrad())
 
-    @unittest.skip('psi2 cross terms are NotImplemented for this combination')
+    #@unittest.skip('psi2 cross terms are NotImplemented for this combination')
     def test_linear_bias_kern(self):
         N, M, Q, D = 10, 3, 2, 4
         X = np.random.rand(N, Q)
diff --git a/GPy/testing/kernel_tests.py b/GPy/testing/kernel_tests.py
index bb809ea6..f1762db8 100644
--- a/GPy/testing/kernel_tests.py
+++ b/GPy/testing/kernel_tests.py
@@ -8,7 +8,7 @@ import GPy
 class KernelTests(unittest.TestCase):
     def test_kerneltie(self):
         K = GPy.kern.rbf(5, ARD=True)
-        K.tie_param('[01]')
+        K.tie_params('[01]')
         K.constrain_fixed('2')
         X = np.random.rand(5,5)
         Y = np.ones((5,1))