diff --git a/GPy/models/Bayesian_GPLVM.py b/GPy/models/Bayesian_GPLVM.py
index a18ec9bb..8f9759c3 100644
--- a/GPy/models/Bayesian_GPLVM.py
+++ b/GPy/models/Bayesian_GPLVM.py
@@ -41,7 +41,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
 
     def _get_param_names(self):
         X_names = sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
-        S_names = sum([['S_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
+        S_names = sum([['X_variance_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
         return (X_names + S_names + sparse_GP._get_param_names(self))
 
     def _get_params(self):
diff --git a/GPy/testing/bgplvm_tests.py b/GPy/testing/bgplvm_tests.py
index b182c1a8..b11b4532 100644
--- a/GPy/testing/bgplvm_tests.py
+++ b/GPy/testing/bgplvm_tests.py
@@ -15,7 +15,7 @@ class BGPLVMTests(unittest.TestCase):
         Y -= Y.mean(axis=0)
         k = GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
         m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k,  M=M)
-        m.constrain_positive('(rbf|bias|noise|white|S)')
+        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -28,7 +28,7 @@ class BGPLVMTests(unittest.TestCase):
         Y -= Y.mean(axis=0)
         k = GPy.kern.linear(Q) + GPy.kern.white(Q, 0.00001)
         m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k,  M=M)
-        m.constrain_positive('(linear|bias|noise|white|S)')
+        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -41,7 +41,7 @@ class BGPLVMTests(unittest.TestCase):
         Y -= Y.mean(axis=0)
         k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
         m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k,  M=M)
-        m.constrain_positive('(rbf|bias|noise|white|S)')
+        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -54,7 +54,7 @@ class BGPLVMTests(unittest.TestCase):
         Y -= Y.mean(axis=0)
         k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
         m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k,  M=M)
-        m.constrain_positive('(rbf|bias|noise|white|S)')
+        m.ensure_default_constraints()
         m.randomize()
         self.assertTrue(m.checkgrad())
 
@@ -68,9 +68,9 @@ class BGPLVMTests(unittest.TestCase):
         Y -= Y.mean(axis=0)
         k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
         m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k,  M=M)
-        m.constrain_positive('(linear|bias|noise|white|S)')
+        m.ensure_default_constraints()
         m.randomize()
-        self.assertTrue(m.checkgrad())        
+        self.assertTrue(m.checkgrad())
 
 
 if __name__ == "__main__":