diff --git a/02-Discrete-Bayes.ipynb b/02-Discrete-Bayes.ipynb
index 5d6687c..2073635 100644
--- a/02-Discrete-Bayes.ipynb
+++ b/02-Discrete-Bayes.ipynb
@@ -369,7 +369,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])"
@@ -462,7 +464,9 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "belief = np.array([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.])"
@@ -692,7 +696,9 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def scaled_update(hall, belief, z, z_prob): \n",
@@ -5489,7 +5495,9 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "class Train(object):\n",
@@ -5538,13 +5546,14 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def train_filter(iterations, kernel, sensor_accuracy, \n",
     "             move_distance, do_print=True):\n",
     "    track = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
-    "\n",
     "    prior = np.array([.9] + [0.01]*9)\n",
     "    normalize(prior)\n",
     "    \n",