diff --git a/GPy/models/ss_gplvm.py b/GPy/models/ss_gplvm.py
index d1b07415..027f9783 100644
--- a/GPy/models/ss_gplvm.py
+++ b/GPy/models/ss_gplvm.py
@@ -24,7 +24,7 @@ class SSGPLVM(SparseGP):
 
     """
     def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
-                 Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, mpi_comm=None, **kwargs):
+                 Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, mpi_comm=None, pi=None, learnPi=True, **kwargs):
 
         self.mpi_comm = mpi_comm
         self.__IN_OPTIMIZATION__ = False
@@ -49,9 +49,6 @@ class SSGPLVM(SparseGP):
         if Z is None:
             Z = np.random.permutation(X.copy())[:num_inducing]
         assert Z.shape[1] == X.shape[1]
-
-        pi = np.empty((input_dim))
-        pi[:] = 0.5
         
         if likelihood is None:
             likelihood = Gaussian()
@@ -64,7 +61,10 @@ class SSGPLVM(SparseGP):
         if inference_method is None:
             inference_method = VarDTC_minibatch(mpi_comm=mpi_comm)
 
-        self.variational_prior = SpikeAndSlabPrior(pi=pi,learnPi=True) # the prior probability of the latent binary variable b
+        if pi is None:
+            pi = np.empty((input_dim))
+            pi[:] = 0.5
+        self.variational_prior = SpikeAndSlabPrior(pi=pi,learnPi=learnPi) # the prior probability of the latent binary variable b
         
         X = SpikeAndSlabPosterior(X, X_variance, gamma)