diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index 3acea54b..c2d2010c 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -236,7 +236,7 @@ class GP(Model):
            If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
            This is to allow for different normalizations of the output dimensions.
 
-        Note: If you want the predictive quantiles (e.g. 95% confidence interval) use :py:func:~GPy.core.gp.GP.predict_quantiles.
+        Note: If you want the predictive quantiles (e.g. 95% confidence interval) use :py:func:"~GPy.core.gp.GP.predict_quantiles".
         """
         #predict the latent function values
         mu, var = self._raw_predict(Xnew, full_cov=full_cov, kern=kern)