diff --git a/GPy/models/bayesian_gplvm.py b/GPy/models/bayesian_gplvm.py
index 03cd361c..2bcbe0b2 100644
--- a/GPy/models/bayesian_gplvm.py
+++ b/GPy/models/bayesian_gplvm.py
@@ -42,7 +42,7 @@ class BayesianGPLVM(SparseGP):
         assert Z.shape[1] == X.shape[1]
 
         if kernel is None:
-            kernel = kern.RBF(input_dim, lengthscale=fracs, ARD=True) # + kern.white(input_dim)
+            kernel = kern.RBF(input_dim, lengthscale=1./fracs, ARD=True) # + kern.white(input_dim)
 
         if likelihood is None:
             likelihood = Gaussian()
diff --git a/GPy/util/initialization.py b/GPy/util/initialization.py
index 8d23b541..dd3b6ec7 100644
--- a/GPy/util/initialization.py
+++ b/GPy/util/initialization.py
@@ -13,11 +13,11 @@ def initialize_latent(init, input_dim, Y):
         p = pca(Y)
         PC = p.project(Y, min(input_dim, Y.shape[1]))
         Xr[:PC.shape[0], :PC.shape[1]] = PC
-        vars = p.fracs[:input_dim]
+        var = p.fracs[:input_dim]
     else:
-        vars = Xr.var(0)
+        var = Xr.var(0)
     
     Xr -= Xr.mean(0)
     Xr /= Xr.var(0)
     
-    return Xr, vars/vars.max()
\ No newline at end of file
+    return Xr, var/var.max()
\ No newline at end of file