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In [1]:

    
from pgmpy.models import BayesianModel
from pgmpy.factors import TabularCPD



In [2]:

    
student = BayesianModel()
# instantiates a new Bayesian Model called 'student'

student.add_nodes_from(['diff', 'intel', 'grade'])
# adds nodes labelled 'diff', 'intel', 'grade' to student

student.add_edges_from([('diff', 'grade'), ('intel', 'grade')])
# adds directed edges from 'diff' to 'grade' and 'intel' to 'grade'

diff cpd:

diff:
easy	0.2
hard	0.8



In [3]:

    
diff_cpd = TabularCPD('diff', 2, [[0.2], [0.8]])

intel cpd:

intel:
dumb	0.5
avg	0.3
smart	0.2



In [4]:

    
intel_cpd = TabularCPD('intel', 3, [[0.5], [0.3], [0.2]])

grade cpd:

diff:	easy	easy	easy	hard	hard	hard
intel:	dumb	avg	smart	dumb	avg	smart
gradeA	0.1	0.1	0.1	0.1	0.1	0.1
gradeB	0.1	0.1	0.1	0.1	0.1	0.1
gradeC	0.8	0.8	0.8	0.8	0.8	0.8



In [5]:

    
grade_cpd = TabularCPD('grade', 3,
                         [[0.1,0.1,0.1,0.1,0.1,0.1],
                         [0.1,0.1,0.1,0.1,0.1,0.1], 
                         [0.8,0.8,0.8,0.8,0.8,0.8]],
                         evidence=['intel', 'diff'],
                         evidence_card=[3, 2])



In [6]:

    
student.add_cpds(diff_cpd, intel_cpd, grade_cpd)

# Finding active trail
student.active_trail_nodes('diff')
{'diff', 'grade'}

# Finding active trail with observation
student.active_trail_nodes('diff', observed='grade')
{'diff', 'intel'}









    Out[6]:





{'diff', 'intel'}