diff --git a/GPy/core/parameterization/parameter_core.py b/GPy/core/parameterization/parameter_core.py
index 7bd00849..8c23e475 100644
--- a/GPy/core/parameterization/parameter_core.py
+++ b/GPy/core/parameterization/parameter_core.py
@@ -853,6 +853,9 @@ class OptimizationHandlable(Indexable):
             pi._propagate_param_grad(parray[pislice], garray[pislice])
             pi_old_size += pi.size
 
+    def _connect_parameters(self):
+        pass
+
 class Parameterizable(OptimizationHandlable):
     """
     A parameterisable class.
diff --git a/GPy/core/parameterization/parameterized.py b/GPy/core/parameterization/parameterized.py
index 896261e1..ceffbcb4 100644
--- a/GPy/core/parameterization/parameterized.py
+++ b/GPy/core/parameterization/parameterized.py
@@ -10,9 +10,15 @@ from parameter_core import HierarchyError, Parameterizable, adjust_name_for_prin
 
 class ParametersChangedMeta(type):
     def __call__(self, *args, **kw):
-        instance = super(ParametersChangedMeta, self).__call__(*args, **kw)
-        instance.parameters_changed()
-        return instance
+        self._in_init_ = True
+        #import ipdb;ipdb.set_trace()
+        self = super(ParametersChangedMeta, self).__call__(*args, **kw)
+        self._in_init_ = False
+        self._highest_parent_._connect_parameters()
+        self._highest_parent_._notify_parent_change()
+        self._highest_parent_._connect_fixes()
+        self.parameters_changed()
+        return self
 
 class Parameterized(Parameterizable):
     """
@@ -64,14 +70,12 @@ class Parameterized(Parameterizable):
     #===========================================================================
     def __init__(self, name=None, parameters=[], *a, **kw):
         super(Parameterized, self).__init__(name=name, *a, **kw)
-        self._in_init_ = True
         self.size = sum(p.size for p in self.parameters)
         self.add_observer(self, self._parameters_changed_notification, -100)
         if not self._has_fixes():
             self._fixes_ = None
         self._param_slices_ = []
-        self._connect_parameters()
-        del self._in_init_
+        #self._connect_parameters()
         self.add_parameters(*parameters)
 
     def build_pydot(self, G=None):
@@ -146,15 +150,16 @@ class Parameterized(Parameterizable):
                 parent.size += param.size
                 parent = parent._parent_
 
-            self._connect_parameters()
-            self._notify_parent_change()
+            if not self._in_init_:
+                self._connect_parameters()
+                self._notify_parent_change()
 
-            self._highest_parent_._connect_parameters(ignore_added_names=_ignore_added_names)
-            self._highest_parent_._notify_parent_change()
-            self._highest_parent_._connect_fixes()
+                self._highest_parent_._connect_parameters(ignore_added_names=_ignore_added_names)
+                self._highest_parent_._notify_parent_change()
+                self._highest_parent_._connect_fixes()
 
         else:
-            raise HierarchyError, """Parameter exists already and no copy made"""
+            raise HierarchyError, """Parameter exists already, try making a copy"""
 
 
     def add_parameters(self, *parameters):
@@ -209,19 +214,21 @@ class Parameterized(Parameterizable):
         old_size = 0
         self._param_slices_ = []
         for i, p in enumerate(self.parameters):
+            if not p.param_array.flags['C_CONTIGUOUS']:
+                raise ValueError, "This should not happen! Please write an email to the developers with the code, which reproduces this error. All parameter arrays must be C_CONTIGUOUS"
+
             p._parent_ = self
             p._parent_index_ = i
 
             pslice = slice(old_size, old_size + p.size)
+
             # first connect all children
             p._propagate_param_grad(self.param_array[pslice], self.gradient_full[pslice])
+
             # then connect children to self
             self.param_array[pslice] = p.param_array.flat  # , requirements=['C', 'W']).ravel(order='C')
             self.gradient_full[pslice] = p.gradient_full.flat  # , requirements=['C', 'W']).ravel(order='C')
 
-            if not p.param_array.flags['C_CONTIGUOUS']:
-                raise ValueError, "This should not happen! Please write an email to the developers with the code, which reproduces this error. All parameter arrays must be C_CONTIGUOUS"
-
             p.param_array.data = self.param_array[pslice].data
             p.gradient_full.data = self.gradient_full[pslice].data
 
diff --git a/GPy/inference/latent_function_inference/var_dtc.py b/GPy/inference/latent_function_inference/var_dtc.py
index 06a41d1d..bd42c1be 100644
--- a/GPy/inference/latent_function_inference/var_dtc.py
+++ b/GPy/inference/latent_function_inference/var_dtc.py
@@ -223,7 +223,7 @@ class VarDTCMissingData(LatentFunctionInference):
             inan = self._inan
             has_none = True
         if has_none:
-            print "caching missing data slices, this can take several minutes..."
+            print "caching missing data slices, this can take several minutes depending on the number of unique dimensions of the data..."
             from ...util.subarray_and_sorting import common_subarrays
             self._subarray_indices = []
             csa = common_subarrays(inan, 1)