diff --git a/GPy/core/parameterization/param.py b/GPy/core/parameterization/param.py
index 06718e8b..aa64e077 100644
--- a/GPy/core/parameterization/param.py
+++ b/GPy/core/parameterization/param.py
@@ -224,7 +224,7 @@ class Param(Parameterizable, ObsAr):
     def _ties_str(self):
         return ['']
     def _ties_for(self, ravi):
-        return [['N/A' if self.tie.flat[i]==0 else str(self.tie[i])] for i in xrange(ravi.size)]
+        return [['N/A' if self.tie.flat[i]==0 else str(self.tie.flat[i])] for i in xrange(ravi.size)]
     def __repr__(self, *args, **kwargs):
         name = "\033[1m{x:s}\033[0;0m:\n".format(
                             x=self.hierarchy_name())
diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index 842d0bf8..7ea18877 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -315,9 +315,9 @@ def bgplvm_simulation(optimize=True, verbose=1,
         m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
     return m
 
-def ssgplvm_simulation(optimize=True, verbose=1,
+def ssgplvm_simulation(optimize=True, verbose=1, group_spike=True,
                       plot=True, plot_sim=False,
-                      max_iters=2e4, useGPU=False
+                      max_iters=2e4
                       ):
     from GPy import kern
     from GPy.models import SSGPLVM
@@ -325,9 +325,9 @@ def ssgplvm_simulation(optimize=True, verbose=1,
     D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
     _, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
     Y = Ylist[0]
-    k = kern.Linear(Q, ARD=True, useGPU=useGPU)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
+    k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
     #k = kern.RBF(Q, ARD=True, lengthscale=10.)
-    m = SSGPLVM(Y, Q, init="pca", num_inducing=num_inducing, kernel=k)
+    m = SSGPLVM(Y, Q, init="init", num_inducing=num_inducing, kernel=k, group_spike=group_spike)
     m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
     m.likelihood.variance = .1
 
diff --git a/GPy/models/ss_gplvm.py b/GPy/models/ss_gplvm.py
index 4ea4f297..95fc037c 100644
--- a/GPy/models/ss_gplvm.py
+++ b/GPy/models/ss_gplvm.py
@@ -74,7 +74,7 @@ class SSGPLVM(SparseGP_MPI):
 #         self.X.variance.constrain_positive()
                 
         if self.group_spike:
-            [self.X.gamma[:,i].tie('tieGamma'+str(i)) for i in xrange(self.X.gamma.shape[1])] # Tie columns together
+            [self.X.gamma[:,i].tie_together() for i in xrange(self.X.gamma.shape[1])] # Tie columns together
         
     def set_X_gradients(self, X, X_grad):
         """Set the gradients of the posterior distribution of X in its specific form."""