def p(self, value, event):
        """Return the conditional probability
        P(X=value | parents=parent_values), where parent_values
        are the values of parents in event. (event must assign each
        parent a value.)
        >>> bn = BayesNode('X', 'Burglary', {T: 0.2, F: 0.625})
        >>> bn.p(False, {'Burglary': False, 'Earthquake': True})
        0.375"""
        assert isinstance(value, bool)
        ptrue = self.cpt[event_values(event, self.parents)]
        return if_(value, ptrue, 1 - ptrue)
def p(self, value, event):
        """Return the conditional probability
        P(X=value | parents=parent_values), where parent_values
        are the values of parents in event. (event must assign each
        parent a value.)
        >>> bn = BayesNode('X', 'Burglary', {T: 0.2, F: 0.625})
        >>> bn.p(False, {'Burglary': False, 'Earthquake': True})
        0.375"""
        assert isinstance(value, bool)
        ptrue = self.cpt[event_values(event, self.parents)]
        return if_(value, ptrue, 1 - ptrue)
def p ( self , value , event ) : assert isinstance ( value , bool ) ptrue = self . cpt [ event_values ( event , self . parents ) ] return if_ ( value , ptrue , 1 - ptrue )
Return the conditional probability
        P(X=value | parents=parent_values), where parent_values
        are the values of parents in event. (event must assign each
        parent a value.)
        >>> bn = BayesNode('X', 'Burglary', {T: 0.2, F: 0.625})
        >>> bn.p(False, {'Burglary': False, 'Earthquake': True})
        0.375
Return the conditional probability P ( X = value | parents = parent_values ) where parent_values are the values of parents in event . ( event must assign each parent a value . ) >>> bn = BayesNode ( X Burglary { T : 0 . 2 F : 0 . 625 } ) >>> bn . p ( False { Burglary : False Earthquake : True } ) 0 . 375