diff --git a/GPy/core/model.py b/GPy/core/model.py
index 3a1d08e9..2acb9963 100644
--- a/GPy/core/model.py
+++ b/GPy/core/model.py
@@ -316,7 +316,10 @@ class model(parameterised):
     def __str__(self):
         s = parameterised.__str__(self).split('\n')
         # add priors to the string
-        strs = [str(p) if p is not None else '' for p in self.priors]
+        if self.priors is not None:
+            strs = [str(p) if p is not None else '' for p in self.priors]
+        else:
+            strs = ['']*len(self._get_params())
         width = np.array(max([len(p) for p in strs] + [5])) + 4
 
         log_like = self.log_likelihood()
diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py
index 2a9d2b00..3992085d 100644
--- a/GPy/examples/regression.py
+++ b/GPy/examples/regression.py
@@ -275,7 +275,7 @@ def sparse_GP_regression_1D(N = 400, M = 5, max_nb_eval_optim=100):
     # create simple GP model
     m = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
 
-    m.constrain_positive('(variance|lengthscale|precision)')
+    m.ensure_default_constraints()
 
     m.checkgrad(verbose=1)
     m.optimize('tnc', messages = 1, max_f_eval=max_nb_eval_optim)