diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py
index ac752c36..c7c9bb55 100644
--- a/GPy/core/sparse_gp.py
+++ b/GPy/core/sparse_gp.py
@@ -46,7 +46,7 @@ class SparseGP(GP):
         self.num_inducing = Z.shape[0]
 
         GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata)
-
+        self.logger.info("Adding Z as parameter")
         self.add_parameter(self.Z, index=0)
 
     def has_uncertain_inputs(self):
@@ -66,10 +66,10 @@ class SparseGP(GP):
             #gradients wrt Z
             self.Z.gradient = self.kern.gradients_X(dL_dKmm, self.Z)
             self.Z.gradient += self.kern.gradients_Z_expectations(
-                               self.grad_dict['dL_dpsi0'], 
-                               self.grad_dict['dL_dpsi1'], 
-                               self.grad_dict['dL_dpsi2'], 
-                               Z=self.Z, 
+                               self.grad_dict['dL_dpsi0'],
+                               self.grad_dict['dL_dpsi1'],
+                               self.grad_dict['dL_dpsi2'],
+                               Z=self.Z,
                                variational_posterior=self.X)
         else:
             #gradients wrt kernel
diff --git a/GPy/inference/latent_function_inference/var_dtc.py b/GPy/inference/latent_function_inference/var_dtc.py
index 059d1daa..e48c61be 100644
--- a/GPy/inference/latent_function_inference/var_dtc.py
+++ b/GPy/inference/latent_function_inference/var_dtc.py
@@ -9,7 +9,7 @@ import numpy as np
 from ...util.misc import param_to_array
 from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)
-import logging
+import logging, itertools
 logger = logging.getLogger('vardtc')
 
 class VarDTC(LatentFunctionInference):
@@ -228,18 +228,28 @@ class VarDTCMissingData(LatentFunctionInference):
             self._subarray_indices = []
             csa = common_subarrays(inan, 1)
             size = len(csa)
+            next_ten = 0
             for i, (v,ind) in enumerate(csa.iteritems()):
                 if not np.all(v):
-                    logger.info('preparing subarrays {:3.3%}'.format((i+1.)/size))
+                    if ((i+1.)/size) >= next_ten:
+                        logger.info('preparing subarrays {:3%}'.format((i+1.)/size))
+                        next_ten += max(.1, 1./size)
                     v = ~np.array(v, dtype=bool)
                     ind = np.array(ind, dtype=int)
                     if ind.size == Y.shape[1]:
                         ind = slice(None)
                     self._subarray_indices.append([v,ind])
-            logger.info('preparing subarrays Y')
+            #logger.info('preparing subarrays {:3.3%}'.format((i+1.)/size))
             #Ys = [Y[v, :][:, ind] for v, ind in self._subarray_indices]
             logger.info('preparing traces Y')
+
+            next_ten = [0.]
+            count = itertools.count()
             def trace(y, v, ind):
+                i = count.next()
+                if ((i+1.)/size) >= next_ten[0]:
+                    logger.info('preparing traces {:3%}'.format((i+1.)/size))
+                    next_ten[0] += .1
                 y = y[v,:][:,ind]
                 return np.einsum('ij,ij->', y,y)
             traces = [trace(Y, v, ind) for v, ind in self._subarray_indices]
diff --git a/GPy/models/bayesian_gplvm.py b/GPy/models/bayesian_gplvm.py
index 29478ed7..5d1cec12 100644
--- a/GPy/models/bayesian_gplvm.py
+++ b/GPy/models/bayesian_gplvm.py
@@ -67,6 +67,7 @@ class BayesianGPLVM(SparseGP):
                 inference_method = VarDTC()
 
         SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, name, **kwargs)
+        self.logger.info("Adding X as parameter")
         self.add_parameter(self.X, index=0)
 
     def set_X_gradients(self, X, X_grad):