diff --git a/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb b/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb
index de97125b..c458a545 100644
--- a/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb
+++ b/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb
@@ -29,18 +29,8 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Applied log-transform to poisson_param and added transformed poisson_param_log_ to model.\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import pymc3 as pm\n",
     "\n",
@@ -1212,21 +1202,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Applied interval-transform to freq_cheating and added transformed freq_cheating_interval_ to model.\n"
-     ]
-    }
-   ],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import pymc3 as pm\n",
+    "import numpy as np\n",
     "\n",
     "N = 100\n",
     "with pm.Model() as model:\n",
@@ -1242,14 +1223,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [],
    "source": [
     "with model:\n",
-    "    true_answers = pm.Bernoulli(\"truths\", p, shape=N, testval=np.random.binomial(1, 0.5, N))"
+    "    # true_answers = pm.Bernoulli(\"truths\", p, shape=N, testval=np.random.binomial(1, 0.5, N))\n",
+    "    true_answers = pm.Bernoulli(\"truths\", p, shape=N)"
    ]
   },
   {
@@ -1261,19 +1241,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "[0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 1 0 1 1\n",
-      " 1 1 0 1 0 0 1 1 1 1 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0 0 1 1\n",
-      " 1 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 1 0]\n"
-     ]
+     "text": "[1 1 0 0 1 1 1 1 0 0 0 1 1 0 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 1 1 1 1 0 1 0\n 0 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1\n 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 0 0]\n"
     }
    ],
    "source": [
@@ -1291,10 +1265,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [],
    "source": [
     "with model:\n",
@@ -1310,10 +1282,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import theano.tensor as tt\n",
@@ -1331,18 +1301,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "array(0.5600000023841858)"
-      ]
+      "text/plain": "array(0.23)"
      },
-     "execution_count": 36,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2660,9 +2626,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.8.1"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
\ No newline at end of file