def main():
pmf_dice = Pmf()
pmf_dice.Set(Die(6),2)
pmf_dice.Set(Die(8),3)
pmf_dice.Set(Die(12),1)
pmf_dice.Set(Die(20),1)
mix = Pmf()
for die, weight in pmf_dice.Items():
for outcome, prob in die.Items():
mix.Incr(outcome, weight*prob)
mix.Normalize()
thinkplot.PrePlot(1)
thinkplot.Pmf(mix)
thinkplot.Save(root='dice_Mix_self3',xlabel='',ylabel='Probability',formats=['pdf'])
def Likelihood(self, data, hypo):
mix = self.mixes[hypo]
like = mix[data]
return like
hypos = ['Bowl 1', 'Bowl 2']
pmf = Cookie(hypos)
# Cookie provides an Update method that takes data as a parameter and updates the probabilities:
# Update loops through each hypothesis in the suite and muiltiplies its probability by the likelihood
# of the data under the hypothesis.
pmf.Update('vanilla')
for hypo, prob in pmf.Items():
print(hypo, prob)
print('\n')
dataset = ['vanilla', 'chocolate', 'vanilla']
for data in dataset:
pmf.Update(data)
# The lines below will show different probabilities.
for data, prob in pmf.Items():
print(data, prob)
print('\n')
print('The Monty Hall Problem:')
from thinkbayes import Pmf
pmf = Pmf()
pmf.Set('b1', 0.5)
pmf.Set('b2', 0.5)
print pmf.Items()
pmf.Mult('b1', 0.75)
pmf.Mult('b2', 0.5)
print pmf.Items()
pmf.Normalize()
print pmf.Items()
print pmf.Prob('b1')
