def main():
# make a uniform prior
param = 1.2
prior = _make_uniform_suite(0.5, 1.5, 1000)
# try out the sample in the book
t = []
sample = [2.675, 0.198, 1.152, 0.787, 2.717, 4.269]
name = 'post%d' % len(sample)
posterior = _estimate_parameter(prior, sample, name)
t.append(posterior)
# try out a range of sample sizes
for n in [10, 20, 40]:
# generate a sample
sample = [random.expovariate(param) for _ in range(n)]
name = 'post%d' % n
# compute the posterior
posterior = _estimate_parameter(prior, sample, name)
t.append(posterior)
# plot the posterior distributions
for i, posterior in enumerate(t):
pyplot.subplot(2, 2, i + 1)
_05_myplot._pmf(posterior)
pyplot.xlabel('lambda')
pyplot.ylabel('Posterior probability')
pyplot.legend()
_05_myplot._save(root='posteriors')
def main():
results = _read_results()
speeds = _get_speeds(results)
pmf = _04_Pmf._make_pmf_from_list(speeds, 'speeds')
_05_myplot._pmf(pmf)
_05_myplot._show(title='PMF of running speed',
xlabel='speed (mph)',
ylabel='probability')
def main():
suite = _make_uniform_suite(0.0, 1.0, 1001)
evidence = 140, 110
_update(suite, evidence)
suite.name = 'posterior'
# plot the posterior distributions
_05_myplot._pmf(suite)
_05_myplot._show(title='Biased coin',
xlabel='P(heads)',
ylabel='Posterior probability')
def main():
upper_bound = 200
prior = _make_uniform_suite(1, upper_bound, upper_bound)
prior.name = 'prior'
evidence = 60
posterior = prior._copy()
_update(posterior, evidence)
posterior.name = 'posterior'
print(_credible_interval(posterior, 90))
# plot the posterior distribution
pyplot.subplots_adjust(wspace=0.4, left=0.15)
plot_options = dict(linewidth=2)
_05_myplot._pmf(posterior, **plot_options)
_05_myplot._save(root='locomotive',
title='Locomotive problem',
xlabel='Number of trains',
ylabel='Posterior probability')
