diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index 75820407..6615b40c 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -176,12 +176,13 @@ def bgplvm_simulation_matlab_compare():
     Y = sim_data['Y']
     S = sim_data['S']
     mu = sim_data['mu']
-    M, [_, Q] = 20, mu.shape
+    M, (_, Q) = 30, mu.shape
 
     from GPy.models import mrd
     from GPy import kern
     reload(mrd); reload(kern)
-    k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
+    #k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
+    k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
     m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
 #                        X=mu,
 #                        X_variance=S,
diff --git a/GPy/models/sparse_GP.py b/GPy/models/sparse_GP.py
index 413d8a07..14c789b8 100644
--- a/GPy/models/sparse_GP.py
+++ b/GPy/models/sparse_GP.py
@@ -76,8 +76,13 @@ class sparse_GP(GP):
             assert self.likelihood.D == 1 #TODO: what if the likelihood is heterscedatic and there are multiple independent outputs?
             if self.has_uncertain_inputs:
                 self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision.flatten().reshape(self.N,1,1)/sf2)).sum(0)
-                tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,self.psi2_beta_scaled.T,lower=1)
-                self.A, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp.T),lower=1)
+                evals, evecs = linalg.eigh(self.psi2_beta_scaled)
+                clipped_evals = np.clip(evals,0.,1e6) # TODO: make clipping configurable
+                if not np.allclose(evals, clipped_evals):
+                    print "Warning: clipping posterior eigenvalues"
+                tmp = evecs*np.sqrt(clipped_evals)
+                tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp),lower=1)
+                self.A = tdot(tmp)
             else:
                 tmp = self.psi1*(np.sqrt(self.likelihood.precision.flatten().reshape(1,self.N))/sf)
                 self.psi2_beta_scaled = tdot(tmp)
@@ -86,8 +91,14 @@ class sparse_GP(GP):
         else:
             if self.has_uncertain_inputs:
                 self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision/sf2)).sum(0)
-                tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,self.psi2_beta_scaled.T,lower=1)
-                self.A, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp.T),lower=1)
+                evals, evecs = linalg.eigh(self.psi2_beta_scaled)
+                clipped_evals = np.clip(evals,0.,1e6) # TODO: make clipping configurable
+                if not np.allclose(evals, clipped_evals):
+                    print "Warning: clipping posterior eigenvalues"
+                tmp = evecs*np.sqrt(clipped_evals)
+                self.psi2_beta_scaled = tdot(tmp)
+                tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp),lower=1)
+                self.A = tdot(tmp)
             else:
                 tmp = self.psi1*(np.sqrt(self.likelihood.precision)/sf)
                 self.psi2_beta_scaled = tdot(tmp)
diff --git a/GPy/util/datasets/BGPLVMSimulation.mat b/GPy/util/datasets/BGPLVMSimulation.mat
new file mode 100644
index 00000000..c1cff0a0
Binary files /dev/null and b/GPy/util/datasets/BGPLVMSimulation.mat differ
diff --git a/GPy/util/linalg.py b/GPy/util/linalg.py
index b19aa2b6..42a98fea 100644
--- a/GPy/util/linalg.py
+++ b/GPy/util/linalg.py
@@ -72,7 +72,7 @@ def jitchol(A,maxtries=5):
             raise linalg.LinAlgError, "not pd: negative diagonal elements"
         jitter= diagA.mean()*1e-6
         for i in range(1,maxtries+1):
-            print '\rWarning: adding jitter of {:.10e}                        '.format(jitter),
+            print 'Warning: adding jitter of {:.10e}'.format(jitter)
             try:
                 return linalg.cholesky(A+np.eye(A.shape[0]).T*jitter, lower = True)
             except: