diff --git a/pgmpy.ipynb b/pgmpy.ipynb
index 1325805..5813f0d 100644
--- a/pgmpy.ipynb
+++ b/pgmpy.ipynb
@@ -1,701 +1,3050 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from pgmpy.factors import TabularCPD\n",
-    "from pgmpy.models import BayesianModel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Setting up your model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### First, set the structure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "olympic_model = BayesianModel([('Genetics', 'OlympicTrials'),\n",
-    "                             ('Practice', 'OlympicTrials'),\n",
-    "                             ('OlympicTrials', 'Offer')])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Then set up the relationships (the CPDs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "genetics_cpd = TabularCPD(\n",
-    "                variable = 'Genetics',\n",
-    "                variable_card = 2,\n",
-    "                values = [[.2,.8]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "practice_cpd = TabularCPD(\n",
-    "                variable = 'Practice',\n",
-    "                variable_card = 2,\n",
-    "                values = [[.7,.3]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "offer_cpd = TabularCPD(\n",
-    "                    variable = 'Offer',\n",
-    "                    variable_card = 2,\n",
-    "                    values = [[.95, .8, .5],\n",
-    "                             [.05, .2, .5]],\n",
-    "                    evidence = ['OlympicTrials'],\n",
-    "                    evidence_card = 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "olympic_trials_cpd = TabularCPD(\n",
-    "                        variable = 'OlympicTrials', \n",
-    "                        variable_card = 3,\n",
-    "                        values = [[.5, .8, .8, .9],\n",
-    "                                 [.3, .15, .1, .08],\n",
-    "                                 [.2, .05, .1, .02]],\n",
-    "                        evidence = ['Genetics', 'Practice'],\n",
-    "                        evidence_card = [2,2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Add the relationships to your models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Replacing existing CPD for Genetics\n",
-      "WARNING:root:Replacing existing CPD for Practice\n",
-      "WARNING:root:Replacing existing CPD for Offer\n",
-      "WARNING:root:Replacing existing CPD for OlympicTrials\n"
-     ]
-    }
-   ],
-   "source": [
-    "olympic_model.add_cpds (genetics_cpd, practice_cpd, offer_cpd, olympic_trials_cpd)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Examine the structure of your graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<TabularCPD representing P(Genetics:2) at 0x1169632e8>,\n",
-       " <TabularCPD representing P(Practice:2) at 0x1169636a0>,\n",
-       " <TabularCPD representing P(Offer:2 | OlympicTrials:3) at 0x116963940>,\n",
-       " <TabularCPD representing P(OlympicTrials:3 | Genetics:2, Practice:2) at 0x116963ef0>]"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "olympic_model.get_cpds()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Find active trail nodes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Genetics', 'Offer', 'OlympicTrials'}"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "olympic_model.active_trail_nodes('Genetics')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Genetics', 'Offer', 'OlympicTrials', 'Practice'}"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "olympic_model.active_trail_nodes('OlympicTrials')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Find local independencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(Genetics _|_ Practice)"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "olympic_model.local_independencies('Genetics')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "olympic_model.local_independencies('OlympicTrials')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get all independencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(Genetics _|_ Practice)\n",
-       "(Genetics _|_ Offer | OlympicTrials)\n",
-       "(Genetics _|_ Offer | Practice, OlympicTrials)\n",
-       "(Practice _|_ Genetics)\n",
-       "(Practice _|_ Offer | OlympicTrials)\n",
-       "(Practice _|_ Offer | Genetics, OlympicTrials)\n",
-       "(Offer _|_ Genetics, Practice | OlympicTrials)\n",
-       "(Offer _|_ Practice | Genetics, OlympicTrials)\n",
-       "(Offer _|_ Genetics | Practice, OlympicTrials)"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "olympic_model.get_independencies()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Making inferences"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### We can get probability distributions that are not explicitly spelled out in our graphs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from pgmpy.inference import VariableElimination"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "olympic_infer = VariableElimination(olympic_model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "╒═════════╤══════════════╕\n",
-      "│ Offer   │   phi(Offer) │\n",
-      "╞═════════╪══════════════╡\n",
-      "│ Offer_0 │       0.8898 │\n",
-      "├─────────┼──────────────┤\n",
-      "│ Offer_1 │       0.1102 │\n",
-      "╘═════════╧══════════════╛\n"
-     ]
-    }
-   ],
-   "source": [
-    "prob_offer = olympic_infer.query(variables = ['Offer'])\n",
-    "print(prob_offer['Offer'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### We can also get conditional probability distributions that take into account what we already know"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "prob_offer_good_genes = olympic_infer.query(\n",
-    "                                        variables = ['Offer', 'Genetics'])\n",
-    "print(prob_offer_good_genes['Genetics'])\n",
-    "print(prob_offer_good_genes['Offer'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "prob_offer_good_genes = olympic_infer.query(\n",
-    "                                        variables = ['Offer', 'OlympicTrials'])\n",
-    "print(prob_offer_good_genes['OlympicTrials'])\n",
-    "print(prob_offer_good_genes['Offer'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "╒═════════╤══════════════╕\n",
-      "│ Offer   │   phi(Offer) │\n",
-      "╞═════════╪══════════════╡\n",
-      "│ Offer_0 │       0.9017 │\n",
-      "├─────────┼──────────────┤\n",
-      "│ Offer_1 │       0.0983 │\n",
-      "╘═════════╧══════════════╛\n"
-     ]
-    }
-   ],
-   "source": [
-    "prob_offer_bad_genes = olympic_infer.query(\n",
-    "                                        variables = ['Offer'], \n",
-    "                                        evidence = {'Genetics':1})\n",
-    "print(prob_offer_bad_genes['Offer'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "╒═════════╤══════════════╕\n",
-      "│ Offer   │   phi(Offer) │\n",
-      "╞═════════╪══════════════╡\n",
-      "│ Offer_0 │       0.8420 │\n",
-      "├─────────┼──────────────┤\n",
-      "│ Offer_1 │       0.1580 │\n",
-      "╘═════════╧══════════════╛\n"
-     ]
-    }
-   ],
-   "source": [
-    "prob_offer_good_genes = olympic_infer.query(\n",
-    "                                        variables = ['Offer'], \n",
-    "                                        evidence = {'Genetics':0})\n",
-    "print(prob_offer_good_genes['Offer'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "╒═════════╤══════════════╕\n",
-      "│ Offer   │   phi(Offer) │\n",
-      "╞═════════╪══════════════╡\n",
-      "│ Offer_0 │       0.8150 │\n",
-      "├─────────┼──────────────┤\n",
-      "│ Offer_1 │       0.1850 │\n",
-      "╘═════════╧══════════════╛\n"
-     ]
-    }
-   ],
-   "source": [
-    "prob_offer_good_genes_did_practice = olympic_infer.query(\n",
-    "                                        variables = ['Offer'], \n",
-    "                                        evidence = {'Genetics':0, 'Practice':0})\n",
-    "print(prob_offer_good_genes_did_practice['Offer'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### You can also go upstream logically. For example, evidence about performance at the Olympic Trials affects the probabilities of Genetics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "╒════════════╤═════════════════╕\n",
-      "│ Genetics   │   phi(Genetics) │\n",
-      "╞════════════╪═════════════════╡\n",
-      "│ Genetics_0 │          0.3377 │\n",
-      "├────────────┼─────────────────┤\n",
-      "│ Genetics_1 │          0.6623 │\n",
-      "╘════════════╧═════════════════╛\n"
-     ]
-    }
-   ],
-   "source": [
-    "prob_good_genes_if_amazing_olympic_trials = olympic_infer.query(\n",
-    "                                        variables = ['Genetics'], \n",
-    "                                        evidence = {'OlympicTrials':2})\n",
-    "print(prob_good_genes_if_amazing_olympic_trials['Genetics'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Some variables are only informative about other variables given 'third' variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "╒════════════╤═════════════════╕\n",
-      "│ Genetics   │   phi(Genetics) │\n",
-      "╞════════════╪═════════════════╡\n",
-      "│ Genetics_0 │          0.2000 │\n",
-      "├────────────┼─────────────────┤\n",
-      "│ Genetics_1 │          0.8000 │\n",
-      "╘════════════╧═════════════════╛\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Practice does not inherently tell us something about Genetics\n",
-    "prob_good_genes_if_no_practice = olympic_infer.query(\n",
-    "                                        variables = ['Genetics'], \n",
-    "                                        evidence = {'Practice':1})\n",
-    "print(prob_good_genes_if_no_practice['Genetics'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "╒════════════╤═════════════════╕\n",
-      "│ Genetics   │   phi(Genetics) │\n",
-      "╞════════════╪═════════════════╡\n",
-      "│ Genetics_0 │          0.3846 │\n",
-      "├────────────┼─────────────────┤\n",
-      "│ Genetics_1 │          0.6154 │\n",
-      "╘════════════╧═════════════════╛\n"
-     ]
-    }
-   ],
-   "source": [
-    "# BUT Practice does tell us something about genetics IF we also \n",
-    "# know something about olympic trials performance\n",
-    "prob_good_genes_if_no_practice_good_olympic_trials = olympic_infer.query(\n",
-    "                                        variables = ['Genetics'], \n",
-    "                                        evidence = {'Practice':1,\n",
-    "                                                   'OlympicTrials':2})\n",
-    "print(prob_good_genes_if_no_practice_good_olympic_trials['Genetics'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### We can find out the most probable state for a variable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Genetics': 1}"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "olympic_infer.map_query(variables = ['Genetics'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Offer': 0}"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {
+        "id": "ItQU7eI-iz-V"
+      },
+      "outputs": [],
+      "source": [
+        "#https://www.youtube.com/watch?v=DEHqIxX1Kq4\n",
+        "#https://github.com/AileenNielsen/PyGotham_2016_Probabilistic_Graphical_Models\n",
+        "\n",
+        "from pgmpy.factors.discrete import TabularCPD\n",
+        "from pgmpy.models import BayesianNetwork"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x9SjxHlIiz-W"
+      },
+      "source": [
+        "# Setting up your model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "unyPS92siz-X"
+      },
+      "source": [
+        "### First, set the structure"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "id": "0yUdLCcriz-X"
+      },
+      "outputs": [],
+      "source": [
+        "olympic_model = BayesianNetwork([('Genetics', 'OlympicTrials'),\n",
+        "                             ('Practice', 'OlympicTrials'),\n",
+        "                             ('OlympicTrials', 'Offer')])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F_qfNItTiz-X"
+      },
+      "source": [
+        "### Then set up the relationships (the CPDs)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "collapsed": true,
+        "id": "DomgMbORiz-X"
+      },
+      "outputs": [],
+      "source": [
+        "genetics_cpd = TabularCPD(\n",
+        "                variable = 'Genetics',\n",
+        "                variable_card = 2,\n",
+        "                values = [[.2],[.8]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "collapsed": true,
+        "id": "BtcOYNL6iz-X"
+      },
+      "outputs": [],
+      "source": [
+        "practice_cpd = TabularCPD(\n",
+        "                variable = 'Practice',\n",
+        "                variable_card = 2,\n",
+        "                values = [[.7],[.3]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "collapsed": true,
+        "id": "ju7g-sVSiz-X"
+      },
+      "outputs": [],
+      "source": [
+        "offer_cpd = TabularCPD(\n",
+        "                    variable = 'Offer',\n",
+        "                    variable_card = 2,\n",
+        "                    values = [[.95, .8, .5],\n",
+        "                             [.05, .2, .5]],\n",
+        "                    evidence = ['OlympicTrials'],\n",
+        "                    evidence_card = [3])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "id": "DnuoZAlKiz-X"
+      },
+      "outputs": [],
+      "source": [
+        "olympic_trials_cpd = TabularCPD(\n",
+        "                        variable = 'OlympicTrials',\n",
+        "                        variable_card = 3,\n",
+        "                        values = [[.5, .8, .8, .9],\n",
+        "                                 [.3, .15, .1, .08],\n",
+        "                                 [.2, .05, .1, .02]],\n",
+        "                        evidence = ['Genetics', 'Practice'],\n",
+        "                        evidence_card = [2,2])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HKR3eAdIiz-X"
+      },
+      "source": [
+        "### Add the relationships to your models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
+      "metadata": {
+        "id": "Rf1_cPEGiz-Y"
+      },
+      "outputs": [],
+      "source": [
+        "olympic_model.add_cpds (genetics_cpd, practice_cpd, offer_cpd, olympic_trials_cpd)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tHDa2Y8Kiz-Y"
+      },
+      "source": [
+        "### Examine the structure of your graph"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wYxgSJf-iz-Y",
+        "outputId": "b4461e81-adf5-4f1b-979e-3e16aab6383c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<TabularCPD representing P(Genetics:2) at 0x7e9aca560610>,\n",
+              " <TabularCPD representing P(Practice:2) at 0x7e9aca562e00>,\n",
+              " <TabularCPD representing P(Offer:2 | OlympicTrials:3) at 0x7e9aca562ef0>,\n",
+              " <TabularCPD representing P(OlympicTrials:3 | Genetics:2, Practice:2) at 0x7e9aca560250>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 63
+        }
+      ],
+      "source": [
+        "olympic_model.get_cpds()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_vIBuc8oiz-Y"
+      },
+      "source": [
+        "### Find active trail nodes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3gY-Pb9Miz-Y",
+        "outputId": "4a526cd8-e75b-4575-e1e0-ecf5d29b33d3"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'Genetics': {'Genetics', 'Offer', 'OlympicTrials'}}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ],
+      "source": [
+        "olympic_model.active_trail_nodes('Genetics')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Hb5IWrG7iz-Y",
+        "outputId": "6e2a37b6-fdcd-4434-f0ef-3253c0cbcba7"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'OlympicTrials': {'Genetics', 'Offer', 'OlympicTrials', 'Practice'}}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 65
+        }
+      ],
+      "source": [
+        "olympic_model.active_trail_nodes('OlympicTrials')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "skT3UGyjiz-Y"
+      },
+      "source": [
+        "### Find local independencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JCJHnNeAiz-Z",
+        "outputId": "63cf97e1-211a-4583-bf82-c7bf3e234fa5"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(Genetics ⟂ Practice)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ],
+      "source": [
+        "olympic_model.local_independencies('Genetics')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0kkS4-ukiz-Z",
+        "outputId": "b8d8036e-32fd-4212-eed4-eb46ef5217e8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": []
+          },
+          "metadata": {},
+          "execution_count": 67
+        }
+      ],
+      "source": [
+        "olympic_model.local_independencies('OlympicTrials')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8tKqT8Iriz-Z"
+      },
+      "source": [
+        "### Get all independencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 68,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gBiE1mBTiz-Z",
+        "outputId": "addf06f9-0c5c-4604-c248-6d9f732276d2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(Practice ⟂ Genetics)\n",
+              "(Practice ⟂ Offer | OlympicTrials)\n",
+              "(Practice ⟂ Offer | Genetics, OlympicTrials)\n",
+              "(Genetics ⟂ Practice)\n",
+              "(Genetics ⟂ Offer | OlympicTrials)\n",
+              "(Genetics ⟂ Offer | Practice, OlympicTrials)\n",
+              "(Offer ⟂ Practice, Genetics | OlympicTrials)\n",
+              "(Offer ⟂ Genetics | Practice, OlympicTrials)\n",
+              "(Offer ⟂ Practice | Genetics, OlympicTrials)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 68
+        }
+      ],
+      "source": [
+        "olympic_model.get_independencies()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hRnqGrS3iz-Z"
+      },
+      "source": [
+        "# Making inferences"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GB7nXleHiz-Z"
+      },
+      "source": [
+        "### We can get probability distributions that are not explicitly spelled out in our graphs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 69,
+      "metadata": {
+        "collapsed": true,
+        "id": "W9A7fjYbiz-Z"
+      },
+      "outputs": [],
+      "source": [
+        "from pgmpy.inference import VariableElimination"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "id": "XWpT12vGiz-Z"
+      },
+      "outputs": [],
+      "source": [
+        "olympic_infer = VariableElimination(olympic_model)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "L1joJRjAiz-Z",
+        "outputId": "b207a3c1-2382-4702-9b55-0b6984ccdcf7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "+----------+--------------+\n",
+            "| Offer    |   phi(Offer) |\n",
+            "+==========+==============+\n",
+            "| Offer(0) |       0.8898 |\n",
+            "+----------+--------------+\n",
+            "| Offer(1) |       0.1102 |\n",
+            "+----------+--------------+\n"
+          ]
+        }
+      ],
+      "source": [
+        "prob_offer = olympic_infer.query(variables = ['Offer'])\n",
+        "print(prob_offer)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6QjriA7Qiz-Z"
+      },
+      "source": [
+        "### We can also get conditional probability distributions that take into account what we already know"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2bT7iyk-iz-Z",
+        "outputId": "b9e94ab8-6293-4641-f004-877f548e797a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "+----------+-------------+-----------------------+\n",
+            "| Offer    | Genetics    |   phi(Offer,Genetics) |\n",
+            "+==========+=============+=======================+\n",
+            "| Offer(0) | Genetics(0) |                0.1684 |\n",
+            "+----------+-------------+-----------------------+\n",
+            "| Offer(0) | Genetics(1) |                0.7214 |\n",
+            "+----------+-------------+-----------------------+\n",
+            "| Offer(1) | Genetics(0) |                0.0316 |\n",
+            "+----------+-------------+-----------------------+\n",
+            "| Offer(1) | Genetics(1) |                0.0786 |\n",
+            "+----------+-------------+-----------------------+\n"
+          ]
+        }
+      ],
+      "source": [
+        "prob_offer_good_genes = olympic_infer.query(\n",
+        "                                        variables = ['Offer', 'Genetics'])\n",
+        "print(prob_offer_good_genes)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tHPVqUyiiz-Z",
+        "outputId": "66a1fd1d-4b53-433f-a9ad-81726eedfa5e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "+----------+------------------+----------------------------+\n",
+            "| Offer    | OlympicTrials    |   phi(Offer,OlympicTrials) |\n",
+            "+==========+==================+============================+\n",
+            "| Offer(0) | OlympicTrials(0) |                     0.7429 |\n",
+            "+----------+------------------+----------------------------+\n",
+            "| Offer(0) | OlympicTrials(1) |                     0.1010 |\n",
+            "+----------+------------------+----------------------------+\n",
+            "| Offer(0) | OlympicTrials(2) |                     0.0459 |\n",
+            "+----------+------------------+----------------------------+\n",
+            "| Offer(1) | OlympicTrials(0) |                     0.0391 |\n",
+            "+----------+------------------+----------------------------+\n",
+            "| Offer(1) | OlympicTrials(1) |                     0.0252 |\n",
+            "+----------+------------------+----------------------------+\n",
+            "| Offer(1) | OlympicTrials(2) |                     0.0459 |\n",
+            "+----------+------------------+----------------------------+\n"
+          ]
+        }
+      ],
+      "source": [
+        "prob_offer_good_genes = olympic_infer.query(\n",
+        "                                        variables = ['Offer', 'OlympicTrials'])\n",
+        "print(prob_offer_good_genes)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 74,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7N1EBxJEiz-Z",
+        "outputId": "f6b4e167-a98d-41b3-b428-d77941a96c63"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "+----------+--------------+\n",
+            "| Offer    |   phi(Offer) |\n",
+            "+==========+==============+\n",
+            "| Offer(0) |       0.9017 |\n",
+            "+----------+--------------+\n",
+            "| Offer(1) |       0.0983 |\n",
+            "+----------+--------------+\n"
+          ]
+        }
+      ],
+      "source": [
+        "prob_offer_bad_genes = olympic_infer.query(\n",
+        "                                        variables = ['Offer'],\n",
+        "                                        evidence = {'Genetics':1})\n",
+        "print(prob_offer_bad_genes)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FVW53vYliz-Z",
+        "outputId": "6a1b6300-6450-43ab-cc99-74dcb874e0c3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "+----------+--------------+\n",
+            "| Offer    |   phi(Offer) |\n",
+            "+==========+==============+\n",
+            "| Offer(0) |       0.8420 |\n",
+            "+----------+--------------+\n",
+            "| Offer(1) |       0.1580 |\n",
+            "+----------+--------------+\n"
+          ]
+        }
+      ],
+      "source": [
+        "prob_offer_good_genes = olympic_infer.query(\n",
+        "                                        variables = ['Offer'],\n",
+        "                                        evidence = {'Genetics':0})\n",
+        "print(prob_offer_good_genes)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mNljwcXfiz-Z",
+        "outputId": "799293f4-8fd6-42f6-ec6d-a9832f85feec"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "+----------+--------------+\n",
+            "| Offer    |   phi(Offer) |\n",
+            "+==========+==============+\n",
+            "| Offer(0) |       0.8150 |\n",
+            "+----------+--------------+\n",
+            "| Offer(1) |       0.1850 |\n",
+            "+----------+--------------+\n"
+          ]
+        }
+      ],
+      "source": [
+        "prob_offer_good_genes_did_practice = olympic_infer.query(\n",
+        "                                        variables = ['Offer'],\n",
+        "                                        evidence = {'Genetics':0, 'Practice':0})\n",
+        "print(prob_offer_good_genes_did_practice)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0RKUqoiyiz-Z"
+      },
+      "source": [
+        "### You can also go upstream logically. For example, evidence about performance at the Olympic Trials affects the probabilities of Genetics."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 77,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uvPvW46Jiz-a",
+        "outputId": "17578e87-9902-4842-f1f4-66e52545bdda"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "+-------------+-----------------+\n",
+            "| Genetics    |   phi(Genetics) |\n",
+            "+=============+=================+\n",
+            "| Genetics(0) |          0.3377 |\n",
+            "+-------------+-----------------+\n",
+            "| Genetics(1) |          0.6623 |\n",
+            "+-------------+-----------------+\n"
+          ]
+        }
+      ],
+      "source": [
+        "prob_good_genes_if_amazing_olympic_trials = olympic_infer.query(\n",
+        "                                        variables = ['Genetics'],\n",
+        "                                        evidence = {'OlympicTrials':2})\n",
+        "print(prob_good_genes_if_amazing_olympic_trials)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iJYR4lroiz-a"
+      },
+      "source": [
+        "### Some variables are only informative about other variables given 'third' variables"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 78,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BV4Y9oYCiz-a",
+        "outputId": "b876fd61-a86f-4178-e26c-cecbe88639d5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "+-------------+-----------------+\n",
+            "| Genetics    |   phi(Genetics) |\n",
+            "+=============+=================+\n",
+            "| Genetics(0) |          0.2000 |\n",
+            "+-------------+-----------------+\n",
+            "| Genetics(1) |          0.8000 |\n",
+            "+-------------+-----------------+\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Practice does not inherently tell us something about Genetics\n",
+        "prob_good_genes_if_no_practice = olympic_infer.query(\n",
+        "                                        variables = ['Genetics'],\n",
+        "                                        evidence = {'Practice':1})\n",
+        "print(prob_good_genes_if_no_practice)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 79,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9PKikc92iz-a",
+        "outputId": "5f631541-5629-4159-c8b8-a199668080c4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "+-------------+-----------------+\n",
+            "| Genetics    |   phi(Genetics) |\n",
+            "+=============+=================+\n",
+            "| Genetics(0) |          0.3846 |\n",
+            "+-------------+-----------------+\n",
+            "| Genetics(1) |          0.6154 |\n",
+            "+-------------+-----------------+\n"
+          ]
+        }
+      ],
+      "source": [
+        "# BUT Practice does tell us something about genetics IF we also\n",
+        "# know something about olympic trials performance\n",
+        "prob_good_genes_if_no_practice_good_olympic_trials = olympic_infer.query(\n",
+        "                                        variables = ['Genetics'],\n",
+        "                                        evidence = {'Practice':1,\n",
+        "                                                   'OlympicTrials':2})\n",
+        "print(prob_good_genes_if_no_practice_good_olympic_trials)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fjURRD2biz-a"
+      },
+      "source": [
+        "### We can find out the most probable state for a variable"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 80,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 98,
+          "referenced_widgets": [
+            "98b77b32ccd449e3867670315d6ec8bf",
+            "156ed51aa28141dabc68ba275367f076",
+            "64a0afa2590a4df3a22c1ce15b975b8d",
+            "9fe61d9242f744088021c14a250f12ef",
+            "93f9d10c6cee4d8e9b38d36975b10e74",
+            "7f126230c2f84f5785e1f60bd7017c93",
+            "e6842a1e1a8f449a95fcab9eac654677",
+            "ca8fb14aaf4140709fde1ba94d49b5d8",
+            "ea03c55f07fe4ae1b716e2846b3e6137",
+            "b24922d75a9347af937b696a39c77982",
+            "01f0f59418aa44c6aad70776a2f9540f",
+            "03febd7031f14f9ab256c06222867f25",
+            "dda629137a0946c5a4dc4264d40ab798",
+            "84d1785ca847435d9d626cc63a777efb",
+            "012f175c553640b099de9f7a096b2b2c",
+            "fb8677c66e7a4678b0ba6d16bd5e38d9",
+            "a49813527eb14c60a3831a1d19aaa4f1",
+            "df17c24dd67844a599a0f9c34bce3925",
+            "f7248cc72b3a459da7dec3ad6c9de651",
+            "210ee404f4114b1995bff65a7b11079b",
+            "0570f3a5a2ad4a5c9e1bb327eb5ed410",
+            "945ce9316bcf4c598c9182a4db0f3a07"
+          ]
+        },
+        "id": "f-jcHD5uiz-a",
+        "outputId": "512f0265-5995-4b58-83d5-0a4f9d43b3df"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "0it [00:00, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "98b77b32ccd449e3867670315d6ec8bf"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "0it [00:00, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "03febd7031f14f9ab256c06222867f25"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'Genetics': 1}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 80
+        }
+      ],
+      "source": [
+        "olympic_infer.map_query(variables = ['Genetics'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 81,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 98,
+          "referenced_widgets": [
+            "adf5c1ab479c46628dc751b5595b3022",
+            "dd27afbc44404f3fbc9683be17254b80",
+            "594e4fe82de548c29b1c3f6c1da7ffd6",
+            "715f3c7828e34cb5a4e23d0830afc5d5",
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