diff --git a/GPy/kern/parts/ss_rbf.py b/GPy/kern/parts/ss_rbf.py
new file mode 100644
index 00000000..a234d428
--- /dev/null
+++ b/GPy/kern/parts/ss_rbf.py
@@ -0,0 +1,352 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import numpy as np
+from kernpart import Kernpart
+from ...util.linalg import tdot
+from ...util.misc import fast_array_equal, param_to_array
+from ...core.parameterization import Param
+
+class SS_RBF(Kernpart):
+    """
+    The RBF kernel for Spike-and-Slab GPLVM
+    Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel:
+
+    .. math::
+
+       k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg) \ \ \ \ \  \\text{ where  } r^2 = \sum_{i=1}^d \\frac{ (x_i-x^\prime_i)^2}{\ell_i^2}
+
+    where \ell_i is the lengthscale, \sigma^2 the variance and d the dimensionality of the input.
+
+    :param input_dim: the number of input dimensions
+    :type input_dim: int
+    :param variance: the variance of the kernel
+    :type variance: float
+    :param lengthscale: the vector of lengthscale of the kernel
+    :type lengthscale: array or list of the appropriate size (or float if there is only one lengthscale parameter)
+    :rtype: kernel object
+    """
+
+    def __init__(self, input_dim, variance=1., lengthscale=None, name='rbf'):
+        super(RBF, self).__init__(input_dim, name)
+        self.input_dim = input_dim
+
+        if lengthscale is not None:
+            lengthscale = np.asarray(lengthscale)
+            assert lengthscale.size == self.input_dim, "bad number of lengthscales"
+        else:
+            lengthscale = np.ones(self.input_dim)
+
+        self.variance = Param('variance', variance)
+        self.lengthscale = Param('lengthscale', lengthscale)
+        self.lengthscale.add_observer(self, self.update_lengthscale)
+        self.add_parameters(self.variance, self.lengthscale)
+        self.parameters_changed() # initializes cache
+
+    def on_input_change(self, X):
+        #self._K_computations(X, None)
+        pass
+
+    def update_lengthscale(self, l):
+        self.lengthscale2 = np.square(self.lengthscale)
+
+    def parameters_changed(self):
+        # reset cached results
+        self._X, self._X2 = np.empty(shape=(2, 1))
+        self._Z, self._mu, self._S = np.empty(shape=(3, 1)) # cached versions of Z,mu,S
+
+    def K(self, X, X2, target):
+        self._K_computations(X, X2)
+        target += self.variance * self._K_dvar
+
+    def Kdiag(self, X, target):
+        np.add(target, self.variance, target)
+
+    def psi0(self, Z, mu, S, target):
+        target += self.variance
+
+    def psi1(self, Z, mu, S, target):
+        self._psi_computations(Z, mu, S)
+        target += self._psi1
+
+    def psi2(self, Z, mu, S, target):
+        self._psi_computations(Z, mu, S)
+        target += self._psi2
+
+    def update_gradients_full(self, dL_dK, X):
+        self._K_computations(X, None)
+        self.variance.gradient = np.sum(self._K_dvar * dL_dK)
+        if self.ARD:
+            self.lengthscale.gradient = self._dL_dlengthscales_via_K(dL_dK, X, None)
+        else:
+            self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK)
+
+    def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
+        #contributions from Kdiag
+        self.variance.gradient = np.sum(dL_dKdiag)
+
+        #from Knm
+        self._K_computations(X, Z)
+        self.variance.gradient += np.sum(dL_dKnm * self._K_dvar)
+        if self.ARD:
+            self.lengthscales.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
+
+        else:
+            self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
+
+        #from Kmm
+        self._K_computations(Z, None)
+        self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
+        if self.ARD:
+            self.lengthscales.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
+        else:
+            self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
+
+    def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
+        self._psi_computations(Z, mu, S)
+
+        #contributions from psi0:
+        self.variance.gradient = np.sum(dL_dpsi0)
+
+        #from psi1
+        self.variance.gradient += np.sum(dL_dpsi1 * self._psi1 / self.variance)
+        d_length = self._psi1[:,:,None] * ((self._psi1_dist_sq - 1.)/(self.lengthscale*self._psi1_denom) +1./self.lengthscale)
+        dpsi1_dlength = d_length * dL_dpsi1[:, :, None]
+        if not self.ARD:
+            self.lengthscale.gradeint = dpsi1_dlength.sum()
+        else:
+            self.lengthscale.gradient = dpsi1_dlength.sum(0).sum(0)
+
+        #from psi2
+        d_var = 2.*self._psi2 / self.variance
+        d_length = 2.*self._psi2[:, :, :, None] * (self._psi2_Zdist_sq * self._psi2_denom + self._psi2_mudist_sq + S[:, None, None, :] / self.lengthscale2) / (self.lengthscale * self._psi2_denom)
+
+        self.variance.gradient += np.sum(dL_dpsi2 * d_var)
+        dpsi2_dlength = d_length * dL_dpsi2[:, :, :, None]
+        if not self.ARD:
+            self.lengthscale.gradient += dpsi2_dlength.sum()
+        else:
+            self.lengthscale.gradient += dpsi2_dlength.sum(0).sum(0).sum(0)
+
+        #from Kmm
+        self._K_computations(Z, None)
+        self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
+        if self.ARD:
+            self.lengthscales.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
+        else:
+            self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK)
+
+    def gradients_X(self, dL_dK, X, X2, target):
+        #if self._X is None or X.base is not self._X.base or X2 is not None:
+        self._K_computations(X, X2)
+        if X2 is None:
+            _K_dist = 2*(X[:, None, :] - X[None, :, :])
+        else:
+            _K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
+        dK_dX = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
+        target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
+
+    def dKdiag_dX(self, dL_dKdiag, X, target):
+        pass
+
+    #---------------------------------------#
+    #             PSI statistics            #
+    #---------------------------------------#
+
+    def dpsi0_dmuS(self, dL_dpsi0, Z, mu, S, target_mu, target_S):
+        pass
+
+    def dpsi1_dZ(self, dL_dpsi1, Z, mu, S, target):
+        self._psi_computations(Z, mu, S)
+        denominator = (self.lengthscale2 * (self._psi1_denom))
+        dpsi1_dZ = -self._psi1[:, :, None] * ((self._psi1_dist / denominator))
+        target += np.sum(dL_dpsi1[:, :, None] * dpsi1_dZ, 0)
+
+    def dpsi1_dmuS(self, dL_dpsi1, Z, mu, S, target_mu, target_S):
+        self._psi_computations(Z, mu, S)
+        tmp = self._psi1[:, :, None] / self.lengthscale2 / self._psi1_denom
+        target_mu += np.sum(dL_dpsi1[:, :, None] * tmp * self._psi1_dist, 1)
+        target_S += np.sum(dL_dpsi1[:, :, None] * 0.5 * tmp * (self._psi1_dist_sq - 1), 1)
+
+    def dpsi2_dZ(self, dL_dpsi2, Z, mu, S, target):
+        self._psi_computations(Z, mu, S)
+        term1 = self._psi2_Zdist / self.lengthscale2 # num_inducing, num_inducing, input_dim
+        term2 = self._psi2_mudist / self._psi2_denom / self.lengthscale2 # N, num_inducing, num_inducing, input_dim
+        dZ = self._psi2[:, :, :, None] * (term1[None] + term2)
+        target += (dL_dpsi2[:, :, :, None] * dZ).sum(0).sum(0)
+
+    def dpsi2_dmuS(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
+        """Think N,num_inducing,num_inducing,input_dim """
+        self._psi_computations(Z, mu, S)
+        tmp = self._psi2[:, :, :, None] / self.lengthscale2 / self._psi2_denom
+        target_mu += -2.*(dL_dpsi2[:, :, :, None] * tmp * self._psi2_mudist).sum(1).sum(1)
+        target_S += (dL_dpsi2[:, :, :, None] * tmp * (2.*self._psi2_mudist_sq - 1)).sum(1).sum(1)
+
+    #---------------------------------------#
+    #            Precomputations            #
+    #---------------------------------------#
+
+    def _K_computations(self, X, X2):
+        #params = self._get_params()
+        if not (fast_array_equal(X, self._X) and fast_array_equal(X2, self._X2)):# and fast_array_equal(self._params_save , params)):
+            #self._X = X.copy()
+            #self._params_save = params.copy()
+            if X2 is None:
+                self._X2 = None
+                X = X / self.lengthscale
+                Xsquare = np.sum(np.square(X), 1)
+                self._K_dist2 = -2.*tdot(X) + (Xsquare[:, None] + Xsquare[None, :])
+            else:
+                self._X2 = X2.copy()
+                X = X / self.lengthscale
+                X2 = X2 / self.lengthscale
+                self._K_dist2 = -2.*np.dot(X, X2.T) + (np.sum(np.square(X), 1)[:, None] + np.sum(np.square(X2), 1)[None, :])
+            self._K_dvar = np.exp(-0.5 * self._K_dist2)
+
+    def _dL_dlengthscales_via_K(self, dL_dK, X, X2):
+        """
+        A helper function for update_gradients_* methods
+
+        Computes the derivative of the objective L wrt the lengthscales via
+
+        dL_dl = sum_{i,j}(dL_dK_{ij} dK_dl)
+
+        assumes self._K_computations has just been called.
+
+        This is only valid if self.ARD=True
+        """
+        target = np.zeros(self.input_dim)
+        dvardLdK = self._K_dvar * dL_dK
+        var_len3 = self.variance / np.power(self.lengthscale, 3)
+        if X2 is None:
+            # save computation for the symmetrical case
+            dvardLdK = dvardLdK + dvardLdK.T
+            code = """
+            int q,i,j;
+            double tmp;
+            for(q=0; q<input_dim; q++){
+              tmp = 0;
+              for(i=0; i<num_data; i++){
+                for(j=0; j<i; j++){
+                  tmp += (X(i,q)-X(j,q))*(X(i,q)-X(j,q))*dvardLdK(i,j);
+                }
+              }
+              target(q) += var_len3(q)*tmp;
+            }
+            """
+            num_data, num_inducing, input_dim = X.shape[0], X.shape[0], self.input_dim
+            X, dvardLdK = param_to_array(X, dvardLdK)
+            weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'target', 'dvardLdK', 'var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
+        else:
+            code = """
+            int q,i,j;
+            double tmp;
+            for(q=0; q<input_dim; q++){
+              tmp = 0;
+              for(i=0; i<num_data; i++){
+                for(j=0; j<num_inducing; j++){
+                  tmp += (X(i,q)-X2(j,q))*(X(i,q)-X2(j,q))*dvardLdK(i,j);
+                }
+              }
+              target(q) += var_len3(q)*tmp;
+            }
+            """
+            num_data, num_inducing, input_dim = X.shape[0], X2.shape[0], self.input_dim
+            X, X2, dvardLdK = param_to_array(X, X2, dvardLdK)
+            weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'X2', 'target', 'dvardLdK', 'var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
+        return target
+
+
+
+    def _psi_computations(self, Z, mu, S):
+        # here are the "statistics" for psi1 and psi2
+        Z_changed = not fast_array_equal(Z, self._Z)
+        if Z_changed:
+            # Z has changed, compute Z specific stuff
+            self._psi2_Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q
+            self._psi2_Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q
+            self._psi2_Zdist_sq = np.square(self._psi2_Zdist / self.lengthscale) # M,M,Q
+
+        if Z_changed or not fast_array_equal(mu, self._mu) or not fast_array_equal(S, self._S):
+            # something's changed. recompute EVERYTHING
+
+            # psi1
+            self._psi1_denom = S[:, None, :] / self.lengthscale2 + 1.
+            self._psi1_dist = Z[None, :, :] - mu[:, None, :]
+            self._psi1_dist_sq = np.square(self._psi1_dist) / self.lengthscale2 / self._psi1_denom
+            self._psi1_exponent = -0.5 * np.sum(self._psi1_dist_sq + np.log(self._psi1_denom), -1)
+            self._psi1 = self.variance * np.exp(self._psi1_exponent)
+
+            # psi2
+            self._psi2_denom = 2.*S[:, None, None, :] / self.lengthscale2 + 1. # N,M,M,Q
+            self._psi2_mudist, self._psi2_mudist_sq, self._psi2_exponent, _ = self.weave_psi2(mu, self._psi2_Zhat)
+            # self._psi2_mudist = mu[:,None,None,:]-self._psi2_Zhat #N,M,M,Q
+            # self._psi2_mudist_sq = np.square(self._psi2_mudist)/(self.lengthscale2*self._psi2_denom)
+            # self._psi2_exponent = np.sum(-self._psi2_Zdist_sq -self._psi2_mudist_sq -0.5*np.log(self._psi2_denom),-1) #N,M,M,Q
+            self._psi2 = np.square(self.variance) * np.exp(self._psi2_exponent) # N,M,M,Q
+
+            # store matrices for caching
+            self._Z, self._mu, self._S = Z, mu, S
+
+    def weave_psi2(self, mu, Zhat):
+        N, input_dim = mu.shape
+        num_inducing = Zhat.shape[0]
+
+        mudist = np.empty((N, num_inducing, num_inducing, input_dim))
+        mudist_sq = np.empty((N, num_inducing, num_inducing, input_dim))
+        psi2_exponent = np.zeros((N, num_inducing, num_inducing))
+        psi2 = np.empty((N, num_inducing, num_inducing))
+
+        psi2_Zdist_sq = self._psi2_Zdist_sq
+        _psi2_denom = self._psi2_denom.squeeze().reshape(N, self.input_dim)
+        half_log_psi2_denom = 0.5 * np.log(self._psi2_denom).squeeze().reshape(N, self.input_dim)
+        variance_sq = float(np.square(self.variance))
+        if self.ARD:
+            lengthscale2 = self.lengthscale2
+        else:
+            lengthscale2 = np.ones(input_dim) * self.lengthscale2
+        code = """
+        double tmp;
+
+        #pragma omp parallel for private(tmp)
+        for (int n=0; n<N; n++){
+            for (int m=0; m<num_inducing; m++){
+               for (int mm=0; mm<(m+1); mm++){
+                   for (int q=0; q<input_dim; q++){
+                       //compute mudist
+                       tmp = mu(n,q) - Zhat(m,mm,q);
+                       mudist(n,m,mm,q) = tmp;
+                       mudist(n,mm,m,q) = tmp;
+
+                       //now mudist_sq
+                       tmp = tmp*tmp/lengthscale2(q)/_psi2_denom(n,q);
+                       mudist_sq(n,m,mm,q) = tmp;
+                       mudist_sq(n,mm,m,q) = tmp;
+
+                       //now psi2_exponent
+                       tmp = -psi2_Zdist_sq(m,mm,q) - tmp - half_log_psi2_denom(n,q);
+                       psi2_exponent(n,mm,m) += tmp;
+                       if (m !=mm){
+                           psi2_exponent(n,m,mm) += tmp;
+                       }
+                   //psi2 would be computed like this, but np is faster
+                   //tmp = variance_sq*exp(psi2_exponent(n,m,mm));
+                   //psi2(n,m,mm) = tmp;
+                   //psi2(n,mm,m) = tmp;
+                   }
+                }
+            }
+        }
+
+        """
+
+        support_code = """
+        #include <omp.h>
+        #include <math.h>
+        """
+        weave.inline(code, support_code=support_code, libraries=['gomp'],
+                     arg_names=['N', 'num_inducing', 'input_dim', 'mu', 'Zhat', 'mudist_sq', 'mudist', 'lengthscale2', '_psi2_denom', 'psi2_Zdist_sq', 'psi2_exponent', 'half_log_psi2_denom', 'psi2', 'variance_sq'],
+                     type_converters=weave.converters.blitz, **self.weave_options)
+
+        return mudist, mudist_sq, psi2_exponent, psi2