diff --git a/GPy/kern/src/stationary.py b/GPy/kern/src/stationary.py
index 286edcc2..fa7c8bd3 100644
--- a/GPy/kern/src/stationary.py
+++ b/GPy/kern/src/stationary.py
@@ -489,18 +489,21 @@ class RatQuad(Stationary):
         self.link_parameters(self.power)
 
     def K_of_r(self, r):
-        r2 = np.power(r, 2.)
-        return self.variance*np.power(1. + r2/2., -self.power)
+        r2 = np.square(r)
+#         return self.variance*np.power(1. + r2/2., -self.power)
+        return self.variance*np.exp(-self.power*np.log1p(r2/2.))
 
     def dK_dr(self, r):
-        r2 = np.power(r, 2.)
-        return -self.variance*self.power*r*np.power(1. + r2/2., - self.power - 1.)
+        r2 = np.square(r)
+#         return -self.variance*self.power*r*np.power(1. + r2/2., - self.power - 1.)
+        return-self.variance*self.power*r*np.exp(-(self.power+1)*np.log1p(r2/2.))
 
     def update_gradients_full(self, dL_dK, X, X2=None):
         super(RatQuad, self).update_gradients_full(dL_dK, X, X2)
         r = self._scaled_dist(X, X2)
-        r2 = np.power(r, 2.)
-        dK_dpow = -self.variance * np.power(2., self.power) * np.power(r2 + 2., -self.power) * np.log(0.5*(r2+2.))
+        r2 = np.square(r)
+#        dK_dpow = -self.variance * np.power(2., self.power) * np.power(r2 + 2., -self.power) * np.log(0.5*(r2+2.))
+        dK_dpow = -self.variance * np.exp(self.power*(np.log(2.)-np.log1p(r2+1)))*np.log1p(r2/2.)
         grad = np.sum(dL_dK*dK_dpow)
         self.power.gradient = grad