def likelihood_weighting(X, e, bn, N):
    """Estimate the probability distribution of variable X given
    evidence e in BayesNet bn.  [Fig. 14.15]
    >>> seed(1017)
    >>> likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T),
    ...   burglary, 10000).show_approx()
    'False: 0.702, True: 0.298'
    """
    W = dict((x, 0) for x in bn.variable_values(X))
    for j in xrange(N):
        sample, weight = weighted_sample(bn, e) # boldface x, w in Fig. 14.15
        W[sample[X]] += weight
    return ProbDist(X, W)
def likelihood_weighting(X, e, bn, N):
    """Estimate the probability distribution of variable X given
    evidence e in BayesNet bn.  [Fig. 14.15]
    >>> seed(1017)
    >>> likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T),
    ...   burglary, 10000).show_approx()
    'False: 0.702, True: 0.298'
    """
    W = dict((x, 0) for x in bn.variable_values(X))
    for j in xrange(N):
        sample, weight = weighted_sample(bn, e) # boldface x, w in Fig. 14.15
        W[sample[X]] += weight
    return ProbDist(X, W)
def likelihood_weighting ( X , e , bn , N ) : W = dict ( ( x , 0 ) for x in bn . variable_values ( X ) ) for j in xrange ( N ) : sample , weight = weighted_sample ( bn , e ) # boldface x, w in Fig. 14.15 W [ sample [ X ] ] += weight return ProbDist ( X , W )
Estimate the probability distribution of variable X given
    evidence e in BayesNet bn.  [Fig. 14.15]
    >>> seed(1017)
    >>> likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T),
    ...   burglary, 10000).show_approx()
    'False: 0.702, True: 0.298'
Estimate the probability distribution of variable X given evidence e in BayesNet bn . [ Fig . 14 . 15 ] >>> seed ( 1017 ) >>> likelihood_weighting ( Burglary dict ( JohnCalls = T MaryCalls = T ) ... burglary 10000 ) . show_approx () False : 0 . 702 True : 0 . 298