diff --git a/GPy/kern/periodic_Matern32.py b/GPy/kern/periodic_Matern32.py
index 898dff7b..662c1506 100644
--- a/GPy/kern/periodic_Matern32.py
+++ b/GPy/kern/periodic_Matern32.py
@@ -1,6 +1,7 @@
 from kernpart import kernpart
 import numpy as np
 from GPy.util.linalg import mdot, pdinv
+from GPy.util.decorators import silence_errors
 
 class periodic_Matern32(kernpart):
     """
@@ -39,12 +40,16 @@ class periodic_Matern32(kernpart):
         def f(x):
             return alpha*np.cos(omega*x+phase)
         return f
+
+    @silence_errors
     def _cos_factorization(self,alpha,omega,phase):
         r1 = np.sum(alpha*np.cos(phase),axis=1)[:,None]
         r2 = np.sum(alpha*np.sin(phase),axis=1)[:,None]
         r =  np.sqrt(r1**2 + r2**2)
         psi = np.where(r1 != 0, (np.arctan(r2/r1) + (r1<0.)*np.pi),np.arcsin(r2))
         return r,omega[:,0:1], psi
+
+    @silence_errors
     def _int_computation(self,r1,omega1,phi1,r2,omega2,phi2):
         Gint1 = 1./(omega1+omega2.T)*( np.sin((omega1+omega2.T)*self.upper+phi1+phi2.T) - np.sin((omega1+omega2.T)*self.lower+phi1+phi2.T)) + 1./(omega1-omega2.T)*( np.sin((omega1-omega2.T)*self.upper+phi1-phi2.T) - np.sin((omega1-omega2.T)*self.lower+phi1-phi2.T) )
         Gint2 = 1./(omega1+omega2.T)*( np.sin((omega1+omega2.T)*self.upper+phi1+phi2.T) - np.sin((omega1+omega2.T)*self.lower+phi1+phi2.T)) +  np.cos(phi1-phi2.T)*(self.upper-self.lower)
@@ -55,6 +60,7 @@ class periodic_Matern32(kernpart):
     def _get_params(self):
         """return the value of the parameters."""
         return np.hstack((self.variance,self.lengthscale,self.period))
+    
     def _set_params(self,x):
         """set the value of the parameters."""
         assert x.size==3
@@ -101,6 +107,7 @@ class periodic_Matern32(kernpart):
         FX  = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)
         np.add(target,np.diag(mdot(FX,self.Gi,FX.T)),target)
 
+    @silence_errors
     def dK_dtheta(self,dL_dK,X,X2,target):
         """derivative of the covariance matrix with respect to the parameters (shape is NxMxNparam)"""
         if X2 is None: X2 = X
@@ -172,6 +179,7 @@ class periodic_Matern32(kernpart):
         #np.add(target[:,:,2],dK_dper, target[:,:,2])
         target[2] += np.sum(dK_dper*dL_dK)
 
+    @silence_errors
     def dKdiag_dtheta(self,dL_dKdiag,X,target):
         """derivative of the diagonal covariance matrix with respect to the parameters"""
         FX  = self._cos(self.basis_alpha[None,:],self.basis_omega[None,:],self.basis_phi[None,:])(X)