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')
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')
