diff --git a/GPy/models/state_space.py b/GPy/models/state_space.py
index 601be3b1..27ea7ca3 100644
--- a/GPy/models/state_space.py
+++ b/GPy/models/state_space.py
@@ -77,7 +77,7 @@ class StateSpace(Model):
         #return self.kf_likelihood_g(F,L,Qc,self.sigma2,H,Pinf,dF,dQc,dPinf,self.X,self.Y) 
         return False
 
-    def predict_raw(self, Xnew):
+    def predict_raw(self, Xnew, filteronly=False):
 
         # Make a single matrix containing training and testing points
         X = np.vstack((self.X, Xnew))
@@ -95,7 +95,8 @@ class StateSpace(Model):
         (M, P) = self.kalman_filter(F,L,Qc,H,self.sigma2,Pinf,X.T,Y.T)
 
         # Run the Rauch-Tung-Striebel smoother
-        (M, P) = self.rts_smoother(F,L,Qc,X.T,M,P)
+        if not filter:
+            (M, P) = self.rts_smoother(F,L,Qc,X.T,M,P)
 
         # Put the data back in the original order
         M = M[:,return_inverse]
@@ -114,10 +115,10 @@ class StateSpace(Model):
         # Return the posterior of the state
         return (m, V)
 
-    def predict(self, Xnew):
+    def predict(self, Xnew, filteronly=False):
 
         # Run the Kalman filter to get the state
-        (m, V) = self.predict_raw(Xnew)
+        (m, V) = self.predict_raw(Xnew,filteronly=filteronly)
 
         # Add the noise variance to the state variance
         V += self.sigma2
@@ -130,7 +131,7 @@ class StateSpace(Model):
         return (m, V, lower, upper)
 
     def plot(self, plot_limits=None, levels=20, samples=0, fignum=None,
-            ax=None, resolution=None, plot_raw=False,
+            ax=None, resolution=None, plot_raw=False, plot_filter=False,
             linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue']):
 
         # Deal with optional parameters
@@ -144,12 +145,12 @@ class StateSpace(Model):
 
         # Make a prediction on the frame and plot it
         if plot_raw:
-            m, v = self.predict_raw(Xgrid)
+            m, v = self.predict_raw(Xgrid,filteronly=plot_filter)
             lower = m - 2*np.sqrt(v)
             upper = m + 2*np.sqrt(v)
             Y = self.Y
         else:
-            m, v, lower, upper = self.predict(Xgrid)
+            m, v, lower, upper = self.predict(Xgrid,filteronly=plot_filter)
             Y = self.Y
 
         # Plot the values