probability notebook: populate outputs in the car-insurance section

Ran just the new section's cells and embedded their results (the rain mini-net
query, the Insurance net load, the multi-valued domains, and the two exact-inference
queries) so the demo renders with output. Other cells untouched.

Author: dmeoli

notebooks/probability.ipynb

@@ -3029,9 +3029,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'heavy: 0.308, light: 0.462, none: 0.231'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "rain_net = DiscreteBayesNet([\n",
     "    ('Rain', '', ['none', 'light', 'heavy'], {(): [0.6, 0.3, 0.1]}),\n",
@@ -3053,9 +3064,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(27,\n",
+       " ['Age',\n",
+       "  'SocioEcon',\n",
+       "  'GoodStudent',\n",
+       "  'RiskAversion',\n",
+       "  'VehicleYear',\n",
+       "  'MakeModel',\n",
+       "  'Antilock',\n",
+       "  'Mileage'])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "insurance_net = insurance()\n",
     "len(insurance_net.variables), insurance_net.variables[:8]"
@@ -3070,9 +3100,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(['Adolescent', 'Adult', 'Senior'],\n",
+       " ['Thousand', 'TenThou', 'HundredThou', 'Million'])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "insurance_net.variable_values('Age'), insurance_net.variable_values('MedCost')"
    ]
@@ -3086,9 +3128,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Adolescent: 0.2, Adult: 0.6, Senior: 0.2'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "elimination_ask('Age', dict(), insurance_net).show_approx()"
    ]
@@ -3102,9 +3155,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'False: 0.6, True: 0.4'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "elimination_ask('GoodStudent', dict(Age='Adolescent', SocioEcon='Wealthy'), insurance_net).show_approx()"
    ]
@@ -6619,7 +6683,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.12.13"
   }
  },
  "nbformat": 4,