diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index ef6e37fb..5e0c77cb 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -18,16 +18,18 @@ def BGPLVM(seed=default_seed):
     D = 10
     # generate GPLVM-like data
     X = np.random.rand(N, Q)
-    k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.01)
+    lengthscales = np.random.rand(Q)
+    k = GPy.kern.rbf(Q, .5, lengthscales, ARD=True) + GPy.kern.white(Q, 0.01)
     K = k.K(X)
     Y = np.random.multivariate_normal(np.zeros(N), K, Q).T
     lik = Gaussian(Y, normalize=True)
 
-    k = GPy.kern.rbf_inv(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q)
+    k = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
     # k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
     # k = GPy.kern.rbf(Q, ARD = False)  + GPy.kern.white(Q, 0.00001)
 
     m = GPy.models.BayesianGPLVM(lik, Q, kernel=k, num_inducing=num_inducing)
+    m.lengthscales = lengthscales
     # m.constrain_positive('(rbf|bias|noise|white|S)')
     # m.constrain_fixed('S', 1)
 
@@ -38,8 +40,8 @@ def BGPLVM(seed=default_seed):
     # m.optimize(messages = 1)
     # m.plot()
     # pb.title('After optimisation')
-    m.randomize()
-    m.checkgrad(verbose=1)
+    # m.randomize()
+    # m.checkgrad(verbose=1)
 
     return m
 
@@ -143,6 +145,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=150, plot=
 
     # create simple GP model
     kernel = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
+    kernel += GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
     Y = data['X'][:N]
     Yn = Y - Y.mean(0)
     Yn /= Yn.std(0)
@@ -151,7 +154,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=150, plot=
     m.data_labels = data['Y'][:N].argmax(axis=1)
 
     # m.constrain('variance|leng', logexp_clipped())
-    m['.*lengt'] = m.X.var(0).max() / m.X.var(0)
+    # m['.*lengt'] = m.X.var(0).max() / m.X.var(0)
     m['noise'] = Yn.var() / 100.
 
 
@@ -267,7 +270,7 @@ def bgplvm_simulation(optimize='scg',
                       max_iters=2e4,
                       plot_sim=False):
 #     from GPy.core.transformations import logexp_clipped
-    D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 300, 23, 6
+    D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 300, 30, 6
     slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
 
     from GPy.models import mrd