likelihood = self.Likelihood(data, hypo)
self.Mult(hypo, likelihood)
self.Normalize()
def Likelihood(self, data, hypo):
proportion = self.proportions[hypo]
likelihood = proportion[data]
return likelihood
# Set up the hypotheses for the Cookie problem
hypos = ['Bowl 1', 'Bowl 2']
# Initialise the prior for the Cookie problem
pmf = Cookie(hypos)
print("Prior:")
for hypo, prob in pmf.Items():
print(hypo,
prob) # Type p14: print hypo, prob should read print(hypo, prob)
# Update the prior given one data point (we drew a vanilla cookie)
pmf.Update('vanilla')
print("Posterior:")
for hypo, prob in pmf.Items():
print(hypo, prob)
# Draw some more cookies (with replacement) and update the prior
dataset = ['vanilla', 'chocolate', 'vanilla']
for data in dataset:
pmf.Update(data)
print("Posterior:")
for hypo, prob in pmf.Items():
print(hypo, prob)
from thinkbayes2 import Suite, Pmf, SampleSum, MakeMixture
import thinkplot
from simulationDD02 import Die
pmf_dice = Pmf()
pmf_dice.Set(Die(4), 2)
pmf_dice.Set(Die(6), 3)
pmf_dice.Set(Die(8), 2)
pmf_dice.Set(Die(12), 1)
pmf_dice.Set(Die(20), 1)
pmf_dice.Normalize()
print(pmf_dice)
print("#################################################")
mix = Pmf()
for die, weight in pmf_dice.Items():
for outcome, prob in die.Items():
mix.Incr(outcome, weight * prob)
#Shorthand for above
#mix = MakeMixture(pmf_dice)
print(mix)
thinkplot.Hist(mix)
thinkplot.Save(root='bar',
xlabel='Mixture over a set of dice',
ylabel='Probability',
formats=['pdf'])
print("Program Finished")
