re-run almost all notebooks that use NUTS

Author: michaelosthege

docs/source/notebooks/AR.ipynb

@@ -105,7 +105,7 @@
        "            }\n",
        "        </style>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:02<00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 00:06<00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -120,8 +120,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 4 seconds.\n",
-      "The acceptance probability does not match the target. It is 0.6249748520956052, but should be close to 0.8. Try to increase the number of tuning steps.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 8 seconds.\n"
      ]
     }
    ],
@@ -250,11 +249,11 @@
     {
      "data": {
       "text/plain": [
-       "({'p_logodds__': array(0.), 'ni': array(0)},\n",
+       "({'p_logodds__': array(0.20397629), 'ni': array(0)},\n",
        " [{'tune': True,\n",
        "   'scaling': array([1.]),\n",
-       "   'accept': 0.012699688055762577,\n",
-       "   'accepted': False}])"
+       "   'accept': 0.7662261757775519,\n",
+       "   'accepted': True}])"
       ]
      },
      "execution_count": 6,
@@ -289,7 +288,7 @@
     {
      "data": {
       "text/plain": [
-       "{'p_logodds__': array(0.), 'ni': array(0)}"
+       "{'p_logodds__': array(0.20397629), 'ni': array(0)}"
       ]
      },
      "execution_count": 7,
@@ -324,8 +323,8 @@
     {
      "data": {
       "text/plain": [
-       "[<pymc3.step_methods.metropolis.Metropolis at 0x7f1bae476410>,\n",
-       " <pymc3.step_methods.metropolis.BinaryGibbsMetropolis at 0x7f1baa12c350>]"
+       "[<pymc3.step_methods.metropolis.Metropolis at 0x7fddc61d4910>,\n",
+       " <pymc3.step_methods.metropolis.BinaryGibbsMetropolis at 0x7fddc6095bd0>]"
       ]
      },
      "execution_count": 8,
@@ -366,8 +365,8 @@
     {
      "data": {
       "text/plain": [
-       "[<pymc3.step_methods.metropolis.BinaryGibbsMetropolis at 0x7f1baa12c350>,\n",
-       " <pymc3.step_methods.metropolis.Metropolis at 0x7f1bae476410>]"
+       "[<pymc3.step_methods.metropolis.BinaryGibbsMetropolis at 0x7fddc6095bd0>,\n",
+       " <pymc3.step_methods.metropolis.Metropolis at 0x7fddc61d4910>]"
       ]
      },
      "execution_count": 9,
@@ -413,10 +412,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "numpy  1.18.5\n",
       "pymc3  3.9.0\n",
       "theano 1.0.4\n",
-      "numpy  1.18.5\n",
-      "last updated: Fri Jun 12 2020 \n",
+      "last updated: Mon Jun 15 2020 \n",
       "\n",
       "CPython 3.7.7\n",
       "IPython 7.15.0\n",
@@ -446,7 +445,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.7"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,