pgmpy

from pgmpy.models import BayesianNetwork from pgmpy.factors.discrete import TabularCPD from pgmpy.inference import VariableElimination # 1. Define the network structure # D: Difficulty (Easy, Hard), I: Intelligence (Low, High) # G: Grade (A, B, C), L: Letter (Good, Bad), S: SAT (Low, High) model = BayesianNetwork([('D', 'G'), ('I', 'G'), ('G', 'L'), ('I', 'S')]) # 2. Define Conditional Probability Distributions (CPDs) cpd_d = TabularCPD(variable='D', variable_card=2, values=[[0.6], [0.4]]) cpd_i = TabularCPD(variable='I', variable_card=2, values=[[0.7], [0.3]]) # G depends on I and D. Order of evidence: I, D cpd_g = TabularCPD(variable='G', variable_card=3, values=[[0.3, 0.05, 0.9, 0.5], [0.4, 0.25, 0.08, 0.3], [0.3, 0.7, 0.02, 0.2]], evidence=['I', 'D'], evidence_card=[2, 2]) cpd_l = TabularCPD(variable='L', variable_card=2, values=[[0.1, 0.4, 0.99], [0.9, 0.6, 0.01]], evidence=['G'], evidence_card=[3]) cpd_s = TabularCPD(variable='S', variable_card=2, values=[[0.95, 0.2], [0.05, 0.8]], evidence=['I'], evidence_card=[2]) # 3. Add CPDs to the model model.add_cpds(cpd_d, cpd_i, cpd_g, cpd_l, cpd_s) # 4. Check if the model is valid (optional but good practice) assert model.check_model(), "Model is not valid!" # 5. Perform inference inference = VariableElimination(model) # Query for P(L | D=0, I=1) result = inference.query(variables=['L'], evidence={'D': 0, 'I': 1}) print("P(L | D=0, I=1):") print(result) # Query for P(G | S=0) result_g_s = inference.query(variables=['G'], evidence={'S': 0}) print("\nP(G | S=0):") print(result_g_s)