{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from pylab import *\n",
"import matplotlib.pyplot as plt\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pyAgrum as gum\n",
"import pyAgrum.lib.notebook as gnb\n",
"\n",
"bn=gum.BayesNet('Fraud Detection')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the network. Here the causes are Fraud, Age and Sex. Afe is modeled as a range variable and can take three values 0, 1 and 2 depending on three age groups <30, between 30-50 and >50 respectively. All others are binary variables."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"F=bn.add(gum.LabelizedVariable(\"Fraud\",\"A binary variable\",2))\n",
"G=bn.add(gum.LabelizedVariable(\"Gas\",\"A binary variable\",2))\n",
"A=bn.add(gum.RangeVariable(\"Age\",\"A range variable\",0,2))\n",
"S=bn.add(gum.LabelizedVariable(\"Sex\",\"A binary variable\",2))\n",
"J=bn.add(gum.LabelizedVariable(\"Jewellery\",\"A binary variable\",2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add arcs and then add probabilities"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"bn.addArc(F,G)\n",
"bn.addArc(F,J)\n",
"bn.addArc(A,J)\n",
"bn.addArc(S,J)\n",
"#bn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The CPT tables are shown below:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
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Comprehension 1 Comprehension Questions 1. Determine whether the...