diff --git a/GPy/models/bayesian_gplvm.py b/GPy/models/bayesian_gplvm.py
index 916c307d..68b0a133 100644
--- a/GPy/models/bayesian_gplvm.py
+++ b/GPy/models/bayesian_gplvm.py
@@ -24,8 +24,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
 
     """
     def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
-                 Z=None, kernel=None, oldpsave=10, _debug=False,
-                 **kwargs):
+                 Z=None, kernel=None, **kwargs):
         if type(likelihood_or_Y) is np.ndarray:
             likelihood = Gaussian(likelihood_or_Y)
         else:
@@ -117,7 +116,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
         return np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta))
 
     def plot_latent(self, *args, **kwargs):
-        return plot_latent.plot_latent_indices(self, *args, **kwargs)
+        return plot_latent.plot_latent(self, *args, **kwargs)
 
     def do_test_latents(self, Y):
         """
diff --git a/GPy/models/mrd.py b/GPy/models/mrd.py
index 0af0dede..327c198f 100644
--- a/GPy/models/mrd.py
+++ b/GPy/models/mrd.py
@@ -48,7 +48,7 @@ class MRD(Model):
                  kernels=None, initx='PCA',
                  initz='permute', _debug=False, **kw):
         if names is None:
-            self.names = ["{}".format(i + 1) for i in range(len(likelihood_or_Y_list))]
+            self.names = ["{}".format(i) for i in range(len(likelihood_or_Y_list))]
 
         # sort out the kernels
         if kernels is None:
@@ -281,12 +281,23 @@ class MRD(Model):
         self.Z = Z
         return Z
 
-    def _handle_plotting(self, fignum, axes, plotf):
+    def _handle_plotting(self, fignum, axes, plotf, sharex=False, sharey=False):
         if axes is None:
             fig = pylab.figure(num=fignum)
+        sharex_ax = None
+        sharey_ax = None
         for i, g in enumerate(self.bgplvms):
+            try:
+                if sharex:
+                    sharex_ax = ax # @UndefinedVariable
+                    sharex = False # dont set twice
+                if sharey:
+                    sharey_ax = ax # @UndefinedVariable
+                    sharey = False # dont set twice
+            except:
+                pass
             if axes is None:
-                ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
+                ax = fig.add_subplot(1, len(self.bgplvms), i + 1, sharex=sharex_ax, sharey=sharey_ax)
             elif isinstance(axes, (tuple, list)):
                 ax = axes[i]
             else:
@@ -306,16 +317,27 @@ class MRD(Model):
         fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.X))
         return fig
 
-    def plot_predict(self, fignum=None, ax=None, **kwargs):
-        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g. predict(g.X)[0], **kwargs))
+    def plot_predict(self, fignum=None, ax=None, sharex=False, sharey=False, **kwargs):
+        fig = self._handle_plotting(fignum,
+                                    ax,
+                                    lambda i, g, ax: ax.imshow(g. predict(g.X)[0], **kwargs),
+                                    sharex=sharex, sharey=sharey)
         return fig
 
-    def plot_scales(self, fignum=None, ax=None, *args, **kwargs):
-        fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.kern.plot_ARD(ax=ax, title=r'$Y_{}$'.format(i), *args, **kwargs))
+    def plot_scales(self, fignum=None, ax=None, titles=None, sharex=False, sharey=True, *args, **kwargs):
+        """
+        :param:`titles` :
+            titles for axes of datasets
+        """
+        if titles is None:
+            titles = [r'${}$'.format(name) for name in self.names]
+        def plotf(i, g, ax):
+            g.kern.plot_ARD(ax=ax, title=titles[i], *args, **kwargs)
+        fig = self._handle_plotting(fignum, ax, plotf, sharex=sharex, sharey=sharey)
         return fig
 
     def plot_latent(self, fignum=None, ax=None, *args, **kwargs):
-        fig = self.gref.plot_latent(*args, **kwargs) # self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
+        fig = self.gref.plot_latent(fignum=fignum, ax=ax, *args, **kwargs) # self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
         return fig
 
     def _debug_plot(self):
@@ -331,13 +353,4 @@ class MRD(Model):
         pylab.draw()
         fig.tight_layout()
 
-    def _debug_optimize(self, opt='scg', maxiters=5000, itersteps=10):
-        iters = 0
-        optstep = lambda: self.optimize(opt, messages=1, max_f_eval=itersteps)
-        self._debug_plot()
-        raw_input("enter to start debug")
-        while iters < maxiters:
-            optstep()
-            self._debug_plot()
-            iters += itersteps