def bayesian_inference():
'''
bayesian_inference
'''
A = Factor(name='A', values=[0, 1], cpd=[0.6, 0.4])
B = Factor(name='B', values=[0, 1], cpd=[0.7, 0.3])
C = Factor(name='C|A,B',
full='C',
values=[0, 1],
cond=[A, B],
cpd=[0.7, 0.3, 0.9, 0.1, 0.8, 0.2, 0.6, 0.4])
print C.joint().marginal(B.var[-1]).reduce(var=C.var[-1], value=0).norm()
def bayesian_inference():
'''
bayesian_inference
'''
A = Factor(name='A',
values=[0, 1],
cpd=[0.6, 0.4])
B = Factor(name='B',
values=[0, 1],
cpd=[0.7, 0.3])
C = Factor(name='C|A,B', full='C',
values=[0, 1],
cond=[A, B],
cpd=[0.7, 0.3, 0.9, 0.1, 0.8, 0.2, 0.6, 0.4])
print C.joint().marginal(B.var[-1]).reduce(var=C.var[-1], value=0).norm()
def cancer_example():
'''
Cancer example
'''
C = Factor(name='Cancer', values=["no", "yes"],
cpd=[0.999, 0.001])
T = Factor(name='Test', values=["pos", "neg"],
cond=[C], cpd=[0.2, 0.8, 0.9, 0.1])
print C
print T
print T.joint()
print T.uncond()
R = T.joint().query2(query=[C], evidence={T:'pos'})
R.name = "Tested once"
print R
def cancer_example():
'''
Cancer example
'''
C = Factor(name='Cancer', values=["no", "yes"], cpd=[0.999, 0.001])
T = Factor(name='Test',
values=["pos", "neg"],
cond=[C],
cpd=[0.2, 0.8, 0.9, 0.1])
print C
print T
print T.joint()
print T.uncond()
R = T.joint().query2(query=[C], evidence={T: 'pos'})
R.name = "Tested once"
print R
