diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py
index e227625d..9ce5c391 100644
--- a/GPy/core/sparse_gp.py
+++ b/GPy/core/sparse_gp.py
@@ -49,7 +49,7 @@ class SparseGP(GP):
             else:
                 #inference_method = ??
                 raise NotImplementedError("what to do what to do?")
-            print("defaulting to ", inference_method, "for latent function inference")
+            print(("defaulting to ", inference_method, "for latent function inference"))
 
         self.Z = Param('inducing inputs', Z)
         self.num_inducing = Z.shape[0]
@@ -159,7 +159,7 @@ class SparseGP(GP):
             mu = np.dot(psi1_star, la) # TODO: dimensions?
 
             if full_cov:
-                raise NotImplementedError, "Full covariance for Sparse GP predicted with uncertain inputs not implemented yet."
+                raise NotImplementedError("Full covariance for Sparse GP predicted with uncertain inputs not implemented yet.")
                 var = np.empty((Xnew.shape[0], la.shape[1], la.shape[1]))
                 di = np.diag_indices(la.shape[1])
             else:
diff --git a/GPy/inference/latent_function_inference/laplace.py b/GPy/inference/latent_function_inference/laplace.py
index 00a2c2b0..2f089141 100644
--- a/GPy/inference/latent_function_inference/laplace.py
+++ b/GPy/inference/latent_function_inference/laplace.py
@@ -171,7 +171,7 @@ class Laplace(LatentFunctionInference):
         #define the objective function (to be maximised)
         def obj(Ki_f, f):
             ll = -0.5*np.sum(np.dot(Ki_f.T, f)) + np.sum(likelihood.logpdf(f, Y, Y_metadata=Y_metadata))
-            print ll
+            print(ll)
             if np.isnan(ll):
                 import ipdb; ipdb.set_trace()  # XXX BREAKPOINT
                 return -np.inf
diff --git a/GPy/likelihoods/gaussian.py b/GPy/likelihoods/gaussian.py
index 424a7f5a..e1299f73 100644
--- a/GPy/likelihoods/gaussian.py
+++ b/GPy/likelihoods/gaussian.py
@@ -48,7 +48,7 @@ class Gaussian(Likelihood):
 
     def betaY(self,Y,Y_metadata=None):
         #TODO: ~Ricardo this does not live here
-        raise RuntimeError, "Please notify the GPy developers, this should not happen"
+        raise RuntimeError("Please notify the GPy developers, this should not happen")
         return Y/self.gaussian_variance(Y_metadata)
 
     def gaussian_variance(self, Y_metadata=None):