Example #1
0
def _class_sizes():
    # start with the actual distribution of class sizes from the book
    d = {
        7: 8,
        12: 8,
        17: 14,
        22: 4,
        27: 6,
        32: 12,
        37: 8,
        42: 3,
        47: 2,
    }

    # form the pmf
    pmf = _04_Pmf._make_pmf_from_dict(d, 'actual')
    print('mean', pmf._mean())
    print('var', pmf._var())

    # compute the biased pmf
    biased_pmf = _bias_pmf(pmf, 'observed')
    print('mean', biased_pmf._mean())
    print('var', biased_pmf._var())

    # unbias the biased pmf
    unbiased_pmf = _unbias_pmf(biased_pmf, 'unbiased')
    print('mean', unbiased_pmf._mean())
    print('var', unbiased_pmf._var())

    # plot the Pmfs
    _05_myplot._pmfs([pmf, biased_pmf])
    _05_myplot._show(xlabel='Class size', ylabel='PMF')
Example #2
0
def main():
    print('pae', 0.3 / (0.3 + 3.0 / 13))

    doorA = _make_uniform_suite(0.0, 1.0, 101, name='Door A')
    evidence = 3, 2
    _update(doorA, evidence)

    doorC = _make_uniform_suite(0.0, 1.0, 101, name='Door C')
    evidence = 3, 10
    _update(doorC, evidence)

    print(_total_probability(doorA, doorC, _prob_winning))

    # plot the posterior distributions
    _05_myplot._pmfs([doorA, doorC])
    _05_myplot._save(root='blinky',
                     formats=['pdf', 'png'],
                     title='Probability of blinking',
                     xlabel='P(blink)',
                     ylabel='Posterior probability')