diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index 22feb28d..bce009f7 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -263,7 +263,7 @@ def bgplvm_simulation(optimize='scg',
     # m.constrain('variance|noise', logexp_clipped())
     m.ensure_default_constraints()
     m['noise'] = Y.var() / 100.
-    m['linear_variance'] = .001
+    m['linear_variance'] = .01
 
     if optimize:
         print "Optimizing model:"
@@ -271,11 +271,8 @@ def bgplvm_simulation(optimize='scg',
                    max_f_eval=max_f_eval,
                    messages=True, gtol=1e-6)
     if plot:
-        import pylab
-        m.plot_X_1d()
-        pylab.figure('BGPLVM Simulation ARD Parameters');
-        pylab.axis();
-        m.kern.plot_ARD()
+        m.plot_X_1d("BGPLVM Latent Space 1D")
+        m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
     return m
 
 def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
diff --git a/GPy/models/Bayesian_GPLVM.py b/GPy/models/Bayesian_GPLVM.py
index b7f7b42b..7855c65e 100644
--- a/GPy/models/Bayesian_GPLVM.py
+++ b/GPy/models/Bayesian_GPLVM.py
@@ -241,22 +241,22 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
         x = np.arange(self.X.shape[0])
         for i in range(self.X.shape[1]):
             if ax is None:
-                ax = fig.add_subplot(self.X.shape[1], 1, i + 1)
+                a = fig.add_subplot(self.X.shape[1], 1, i + 1)
             elif isinstance(ax, (tuple, list)):
-                ax = ax[i]
+                a = ax[i]
             else:
                 raise ValueError("Need one ax per latent dimnesion Q")
-            ax.plot(self.X, c='k', alpha=.3)
-            plots.extend(ax.plot(x, self.X.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
-            ax.fill_between(x,
+            a.plot(self.X, c='k', alpha=.3)
+            plots.extend(a.plot(x, self.X.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
+            a.fill_between(x,
                             self.X.T[i] - 2 * np.sqrt(self.X_variance.T[i]),
                             self.X.T[i] + 2 * np.sqrt(self.X_variance.T[i]),
                             facecolor=plots[-1].get_color(),
                             alpha=.3)
-            ax.legend(borderaxespad=0.)
-            ax.set_xlim(x.min(), x.max())
+            a.legend(borderaxespad=0.)
+            a.set_xlim(x.min(), x.max())
             if i < self.X.shape[1] - 1:
-                ax.set_xticklabels('')
+                a.set_xticklabels('')
         pylab.draw()
         fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
         return fig
diff --git a/GPy/models/mrd.py b/GPy/models/mrd.py
index e1bfa947..6b713309 100644
--- a/GPy/models/mrd.py
+++ b/GPy/models/mrd.py
@@ -261,12 +261,12 @@ class MRD(model):
             fig = pylab.figure(num=fignum, figsize=(4 * len(self.bgplvms), 3))
         for i, g in enumerate(self.bgplvms):
             if axes is None:
-                axes = fig.add_subplot(1, len(self.bgplvms), i + 1)
+                ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
             elif isinstance(axes, (tuple, list)):
-                axes = axes[i]
+                ax = axes[i]
             else:
                 raise ValueError("Need one axes per latent dimension Q")
-            plotf(i, g, axes)
+            plotf(i, g, ax)
         pylab.draw()
         if axes is None:
             fig.tight_layout()
@@ -282,15 +282,15 @@ class MRD(model):
         return fig
 
     def plot_predict(self, fignum="MRD Predictions", ax=None, **kwargs):
-        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.predict(g.X)[0], **kwargs))
+        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g. predict(g.X)[0], **kwargs))
         return fig
 
     def plot_scales(self, fignum="MRD Scales", ax=None, *args, **kwargs):
-        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.kern.plot_ARD(axes=ax, *args, **kwargs))
+        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.kern.plot_ARD(ax=ax, *args, **kwargs))
         return fig
 
     def plot_latent(self, fignum="MRD Latent Spaces", ax=None, *args, **kwargs):
-        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(axes=ax, *args, **kwargs))
+        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
         return fig
 
     def _debug_plot(self):