>>> cpt = burglary.variable_node('Alarm').cpt

>>> parents = ['Burglary', 'Earthquake']

>>> event = {'Burglary': True, 'Earthquake': True}

>>> print '%4.2f' % cpt.p(True, parents, event)

0.95

>>> event = {'Burglary': False, 'Earthquake': True}

>>> print '%4.2f' % cpt.p(False, parents, event)

0.71

>>> BoolCPT({T: 0.2, F: 0.625}).p(False, ['Burglary'], event)

0.375

>>> BoolCPT(0.75).p(False, [], {})

0.25

(fixme: The following test p_values which has been folded into p().)

>>> cpt = BoolCPT(0.25)

>>> cpt.p_values(F, ())

0.75

>>> cpt = BoolCPT({T: 0.25, F: 0.625})

>>> cpt.p_values(T, (T,))

0.25

>>> cpt.p_values(F, (F,))

0.375

>>> cpt = BoolCPT({(T, T): 0.2, (T, F): 0.31,

... (F, T): 0.5, (F, F): 0.62})

>>> cpt.p_values(T, (T, F))

0.31

>>> cpt.p_values(F, (F, F))

0.38

>>> cpt = BoolCPT({True: 0.2, False: 0.7})

>>> cpt.rand(['A'], {'A': True}) in [True, False]

True

>>> cpt = BoolCPT({(True, True): 0.1, (True, False): 0.3,

... (False, True): 0.5, (False, False): 0.7})

>>> cpt.rand(['A', 'B'], {'A': True, 'B': False}) in [True, False]

True

>>> enumeration_ask('Earthquake', {}, burglary).show_approx()

'False: 0.998, True: 0.002'

>>> s = prior_sample(burglary)

>>> s['Burglary'] in [True, False]

True

>>> s['Alarm'] in [True, False]

True

>>> s['JohnCalls'] in [True, False]

True

>>> len(s)

5

>>> s = {'A': True, 'B': False, 'C': True, 'D': False}

>>> consistent_with(s, {})

True

>>> consistent_with(s, s)

True

>>> consistent_with(s, {'A': False})

False

>>> consistent_with(s, {'D': True})

False

>>> seed(21); p = rejection_sampling('Earthquake', {}, burglary, 1000)

>>> [p[True], p[False]]

[0.001, 0.999]

>>> seed(71); p = likelihood_weighting('Earthquake', {}, burglary, 1000)

>>> [p[True], p[False]]

[0.002, 0.998]