To implement the inference Burglary P(B| j,⥗m) in alarm problem by using Variable Elimination method in Python.
Step 1: Define the Bayesian Network structure for alarm problem with 5 random
variables, Burglary,Earthquake,John Call,Mary Call and Alarm.
Step 2: Define the Conditional Probability Distributions (CPDs) for each variable
using the TabularCPD class from the pgmpy library.
Step 3: Add the CPDs to the network.
Step 4: Initialize the inference engine using the VariableElimination class from
the pgmpy library.
Step 5: Define the evidence (observed variables) and query variables.
Step 6: Perform exact inference using the defined evidence and query variables.
Step 7: Print the results.
Developed by : G.Dhanasekar
Reg no. : 212220230009
from pgmpy.models import BayesianNetwork
from pgmpy.factors.discrete import TabularCPD
from pgmpy.inference import VariableElimination
network=BayesianNetwork([('Burglary','Alarm'),('Earthquake','Alarm'),
('Alarm','JohnCalls'),
('Alarm','MarryCalls')])
cpd_burglary=TabularCPD(variable='Burglary',variable_card=2,values=[[0.999],[0.001]])
cpd_earthquake=TabularCPD(variable='Earthquake',variable_card=2,values=[[0.998],[0.002]])
cpd_alarm=TabularCPD(variable='Alarm',variable_card=2,values=[[0.999,0.71,0.06,0.05],[0.001,0.29,0.94,0.95]],evidence=['Burglary','Earthquake'],evidence_card=[2,2])
cpd_john_calls=TabularCPD(variable='JohnCalls',variable_card=2,values=[[0.95,0.1],[0.05,0.9]],evidence=['Alarm'],evidence_card=[2])
cpd_marry_calls=TabularCPD(variable='MarryCalls',variable_card=2,values=[[0.99,0.3],[0.01,0.7]],evidence=['Alarm'],evidence_card=[2])
network.add_cpds(cpd_burglary,cpd_earthquake,cpd_alarm,cpd_john_calls,cpd_marry_calls)
inference=VariableElimination(network)
evidence={'JohnCalls':1,'MarryCalls':0}
query_variable='Burglary'
result=inference.query(variables=[query_variable],evidence=evidence)
print(result)
Hence the probability of burglary and phi(burglary) was successfully found with Bayesian network
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