diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index a04ac8da..35a41cde 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -148,7 +148,7 @@ class GP(Model):
 
         return Ysim
 
-    def posterior_samples(self,X,size=10, full_cov=True, Y_metadata=None):
+    def posterior_samples(self, X, size=10, full_cov=False, Y_metadata=None):
         """
         Samples the posterior GP at the points X.
 
@@ -163,7 +163,7 @@ class GP(Model):
         :returns: Ysim: set of simulations, a Numpy array (N x samples).
         """
         Ysim = self.posterior_samples_f(X, size, full_cov=full_cov)
-        Ysim = self.likelihood.noise_model.samples(Ysim, Y_metadata)
+        Ysim = self.likelihood.samples(Ysim, Y_metadata)
 
         return Ysim
 
diff --git a/GPy/likelihoods/gaussian.py b/GPy/likelihoods/gaussian.py
index 032136a7..aaa356b6 100644
--- a/GPy/likelihoods/gaussian.py
+++ b/GPy/likelihoods/gaussian.py
@@ -94,7 +94,7 @@ class Gaussian(Likelihood):
         return self.variance + sigma**2
 
     def predictive_quantiles(self, mu, var, quantiles, Y_metadata):
-        return  [stats.norm.ppf(q)*np.sqrt(var) + mu for q in quantiles]
+        return  [stats.norm.ppf(q/100.)*np.sqrt(var) + mu for q in quantiles]
 
     def pdf_link(self, link_f, y, extra_data=None):
         """
diff --git a/GPy/likelihoods/likelihood.py b/GPy/likelihoods/likelihood.py
index 67c406df..3eafedb1 100644
--- a/GPy/likelihoods/likelihood.py
+++ b/GPy/likelihoods/likelihood.py
@@ -397,7 +397,7 @@ class Likelihood(Parameterized):
 
         return [np.percentile(ss_y ,q, axis=1)[:,None] for q in quantiles]
 
-    def samples(self, gp):
+    def samples(self, gp, Y_metadata=None):
         """
         Returns a set of samples of observations based on a given value of the latent variable.
 
diff --git a/GPy/likelihoods/student_t.py b/GPy/likelihoods/student_t.py
index ce86d9d6..15fd9fa0 100644
--- a/GPy/likelihoods/student_t.py
+++ b/GPy/likelihoods/student_t.py
@@ -263,7 +263,7 @@ class StudentT(Likelihood):
     def conditional_variance(self, gp):
         return self.deg_free/(self.deg_free - 2.)
 
-    def samples(self, gp):
+    def samples(self, gp, Y_metadata=None):
         """
         Returns a set of samples of observations based on a given value of the latent variable.
 
diff --git a/GPy/plotting/matplot_dep/models_plots.py b/GPy/plotting/matplot_dep/models_plots.py
index c87eb694..7507c376 100644
--- a/GPy/plotting/matplot_dep/models_plots.py
+++ b/GPy/plotting/matplot_dep/models_plots.py
@@ -86,8 +86,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
             upper = m + 2*np.sqrt(v)
         else:
             m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata)
-
-        lower, upper = model.predict_quantiles(Xgrid, Y_metadata=Y_metadata)
+            lower, upper = model.predict_quantiles(Xgrid, Y_metadata=Y_metadata)
 
 
         for d in which_data_ycols: