diff --git a/GPy/core/model.py b/GPy/core/model.py
index 25c10b42..ef05a2cb 100644
--- a/GPy/core/model.py
+++ b/GPy/core/model.py
@@ -416,9 +416,10 @@ class model(parameterised):
         """
         return an array describing the sesitivity of the model to each input
 
-        NB. Right now, we're basing this on the lengthscales (or variances) of the kernel.
-        TODO: proper sensitivity analysis
-        """
+        NB. Right now, we're basing this on the lengthscales (or
+        variances) of the kernel.  TODO: proper sensitivity analysis
+        where we integrate across the model inputs and evaluate the
+        effect on the variance of the model output.  """
 
         if not hasattr(self, 'kern'):
             raise ValueError, "this model has no kernel"
diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index 75820407..931e2eed 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -82,11 +82,11 @@ def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300):
         m.ensure_default_constraints()
 
     y = m.likelihood.Y[0, :]
-    fig,(latent_axes,hist_axes) = plt.subplots(1,2)
+    fig,(latent_axes,sense_axes) = plt.subplots(1,2)
     plt.sca(latent_axes)
     m.plot_latent()
     data_show = GPy.util.visualize.vector_show(y)
-    lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes, hist_axes=hist_axes)
+    lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
     raw_input('Press enter to finish')
     plt.close('all')
     # # plot