def Main():
# make a redditor with some trustworthiness (mean_t = 0.67)
founder = Redditor(name='redditor')
beta = thinkbayes.Beta(2, 1)
for val, prob in beta.MakePmf().Items():
founder.Set(val * 100, prob)
# make a new item with unknown quality (mean_q = 0.5)
item = Item(range(0, 101), name='item')
# compute the means
mean_t = founder.Mean() / 100.0
mean_q = item.Mean() / 100.0
print mean_t
print mean_q
# perform simultaneous updates
founder.Update(('up', mean_q))
item.Update(('up', mean_t))
Summarize(item)
# display the posterior distributions
myplot.Pmf(founder)
myplot.Pmf(item)
myplot.Show()
def SampleCtr(n=1, preload=20):
"""Draw a sample from a beta distribution of CTRs.
n: sample size
preload: number of preloaded "no"
returns: sequence of CTR
"""
beta = thinkbayes.Beta(1, preload + 1)
ps = beta.Sample(n)
return ps
def MakePosterior(data, preload=20, steps=201, name=""):
"""Makes the posterior distribution of CTR given the data.
Where CTR is "click through rate"
data: sequence of values in [0, 1]
preload: number of preloaded "no"
steps: number of values to put in the posterior dist
name: name to give the pmf
returns: Pmf of CTR
"""
yes, no = data
pmf = thinkbayes.Beta(yes + 1, no + 1).MakePmf(steps, name)
return pmf
# plot the posteriors
PlotSuites([suite1], 'euro1')
PlotSuites([suite1, suite2], 'euro3')
if __name__ == '__main__':
main()
# Beta Distribution #
# This approach is another optimization for the same problem. Becaus the beta distribution is
# defined on an interval between 0 and 1 (inclusive). The beta distribution is a conjugate prior,
# meaning that if the prior distribution for x is a beta distribution, then the posterior is also
# a beta distribution.
# the ThinkBayes.py file has a Beta class. So we will just implement it here.
beta = thinkbayes.Beta()
beta.Update((140, 110))
print(beta.Mean())
## Odds and Addends ##
# 'Odds in favor' of an event are the ratio of the probability it will occur to the probability
# that it will not.
import random
FORMATS = ['pdf', 'eps', 'png']
class Die(thinkbayes.Pmf):
def __init__(self, sides, name=''):
def __init__(self):
thinkbayes.Suite.__init__(self)
beta = thinkbayes.Beta(1, 1)
for val, prob in beta.MakePmf().Items():
self.Set(val, prob)