def __init__(self, exam, score):
self.exam = exam
self.score = score
# start with the Normal prior
efficacies = thinkbayes2.MakeNormalPmf(0, 1.5, 3)
thinkbayes2.Suite.__init__(self, efficacies)
# update based on an exam score
self.Update(score)
def main():
p1 = thinkbayes2.MakeNormalPmf(0, 1, 3, n=101)
p1.label = 'p1'
p2 = p1.Copy(label='p2')
q1 = thinkbayes2.MakeNormalPmf(0, 1, 3, n=101)
q1.label = 'q1'
q2 = q1.Copy(label='q2')
p1, q1 = Update(p1, q1, True)
p1, q2 = Update(p1, q2, True)
p2, q1 = Update(p2, q1, True)
p2, q2 = Update(p2, q2, False)
thinkplot.PrePlot(num=4, rows=2)
thinkplot.Pmfs([p1, p2])
thinkplot.Config(legend=True)
thinkplot.SubPlot(2)
thinkplot.Pmfs([q1, q2])
thinkplot.Show()
print('Prob p1 > p2', p1 > p2)
print('Prob q1 > q2', q1 > q2)
def __init__(self, label=None):
"""Initializes the Hockey object.
label: string
"""
if USE_SUMMARY_DATA:
# prior based on each team's average goals scored
mu = 2.8
sigma = 0.3
else:
# prior based on each pair-wise match-up
mu = 2.8
sigma = 0.85
pmf = thinkbayes2.MakeNormalPmf(mu, sigma, 4)
thinkbayes2.Suite.__init__(self, pmf, label=label)
def CalibrateDifficulty(self):
"""Make a plot showing the model distribution of raw scores."""
thinkplot.Clf()
thinkplot.PrePlot(num=2)
cdf = thinkbayes2.Cdf(self.raw, label='data')
thinkplot.Cdf(cdf)
efficacies = thinkbayes2.MakeNormalPmf(0, 1.5, 3)
pmf = self.MakeRawScoreDist(efficacies)
cdf = thinkbayes2.Cdf(pmf, label='model')
thinkplot.Cdf(cdf)
thinkplot.Save(root='sat_calibrate',
xlabel='raw score',
ylabel='CDF',
formats=['pdf', 'eps'])
def main():
# Data of time since last login for >100 observed females
femaleData = [
1, 1, 21, 2, 24, 2, 1, 2, 1, 2, 5, 1, 2, 1, 2, 2, 2, 2, 2.67, 2, 1, 2,
2, 0.08333333333, 3, 2, 24, 336, 11, 3, 10, 1, 0.1833333333,
0.7166666667, 3, 1, 1, 3, 3, 3, 0.15, 3, 3, 3, 48, 1, 72, 2, 2, 0.6, 1,
4, 5, 4, 0.6833333333, 120, 4, 0.4, 4, 4, 72, 3, 24, 0.65, 0.65, 96,
0.5333333333, 1, 2, 2, 6, 3, 1, 3, 720, 7, 6, 24, 0.06666666667, 0.9,
0.06666666667, 0.2, 0.4333333333, 7, 96, 22, 8, 1, 0.4, 48, 0.1, 2, 7,
3, 0.06666666667, 6, 24, 0.4166666667, 9, 720, 8, 1, 9
]
# make the priors (normal distribution)
normpmf = thinkbayes2.MakeNormalPmf(10, 10, 4)
lampmf = Lambda(label='Thresh=5')
for val, prob in normpmf.Items():
if (val >= 0 and val <= 100):
lampmf[val] = prob
lampmf.Normalize()
# thinkplot.Pmf(lampmf)
lampmf2 = copy.deepcopy(lampmf)
lampmf3 = lampmf.Copy(label='Thresh=10')
