Implementation of Bayes Belief Network

 Python Code:

import pgmpy.models

import pgmpy.inference

import networkx as nx

import pylab as plt

model = pgmpy.models.BayesianModel([('Guest', 'Monty'), ('Price', 'Monty')])

cpd_guest = pgmpy.factors.discrete.TabularCPD('Guest', 3, [[0.33, 0.33, 0.33]])

cpd_price = pgmpy.factors.discrete.TabularCPD('Price', 3, [[0.33, 0.33, 0.33]])

cpd_monty = pgmpy.factors.discrete.TabularCPD('Monty', 3, [[0, 0, 0, 0, 0.5, 1, 0, 1, 0.5], 

                                                           [0.5, 0, 1, 0, 0, 0, 1, 0, 0.5], 

                                                           [0.5, 1, 0, 1, 0.5, 0, 0, 0, 0]], 

                                              evidence=['Guest', 'Price'], 

                                              evidence_card=[3, 3])

model.add_cpds(cpd_guest, cpd_price, cpd_monty)

model.check_model()

print('Probability distribution, P(Guest)')

print(cpd_guest)

print()

print('Probability distribution, P(Price)')

print(cpd_price)

print()

print('Joint probability distribution, P(Monty | Guest, Price)')

print(cpd_monty)

print()

nx.draw(model, with_labels=True)

plt.savefig('Data.png')

plt.close()

infer = pgmpy.inference.VariableElimination(model)

posterior_probability = infer.query(['Price'], evidence={'Guest': 0, 'Monty': 2})

print('Posterior probability, Guest(0) and Monty(2)')

print(posterior_probability)

print()

Output: