def main():
pmf = Pmf()
pmf.Set('Bowl 1', 0.5)
pmf.Set('Bowl 2', 0.5)
pmf.Mult('Bowl 1', 0.75)
pmf.Mult('Bowl 2', 0.5)
pmf.Normalize()
print(pmf.Prob('Bowl 1'))
print(pmf.Prob('Bowl 2'))
"""This file contains code for use with "Think Bayes",
by Allen B. Downey, available from greenteapress.com
Copyright 2012 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function, division
from thinkbayes2 import Pmf
pmf = Pmf()
pmf.Set('Bowl 1', 0.5)
pmf.Set('Bowl 2', 0.5)
pmf.Mult('Bowl 1', 0.75)
pmf.Mult('Bowl 2', 0.5)
pmf.Normalize()
print(pmf.Prob('Bowl 1'))
import numpy as np
import matplotlib.pyplot as plt
from thinkbayes2 import Pmf, Suite, CredibleInterval, Beta
# PMF for 6-sided die
pmf = Pmf()
for x in [1, 2, 3, 4, 5, 6]:
pmf.Set(x, 1 / 6)
print(pmf)
# How to build up a pmf from a list of strings
pmf2 = Pmf()
for word in ['a', 'in', 'or', 'to', 'a', 'me', 'in']:
pmf2.Incr(word, 1)
pmf2.Normalize()
print(pmf2)
print("Probability of letter a:", pmf2.Prob(
'a')) # Typo p12 print pmf.Prob('the') should read print(pmf.Prob('the'))
# PMF for the Cookie problem
pmf = Pmf()
# Prior:
pmf.Set("Bowl 1", 0.5)
pmf.Set("Bowl 2", 0.5)
# Posterior:
# First multiply prior by likelihood
pmf.Mult("Bowl 1", 0.75)
pmf.Mult("Bowl 2", 0.5)
# Then normalise (we can do this because the hypotheses are mutually exclusive and collectively exhaustive,
# i.e. only one of the hypotheses can be true and there can be no other hypothesis)
from __future__ import print_function, division
import sys
sys.path.append("../lib/ThinkBayes2/code/")
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'])
from thinkbayes2 import Pmf, Suite
import thinkplot
# Probability of each dice given that when rolled the output is 6
dice = Pmf(['4-sided', '6-sided', '8-sided', '12-sided'])
dice['4-sided'] *= 0
dice['6-sided'] *= 1 / 6
dice['8-sided'] *= 1 / 8
dice['12-sided'] *= 1 / 12
dice.Normalize()
print(dice)
suite = Suite([4, 6, 8, 12])
suite[4] *= 0
suite[6] *= 1 / 6
suite[8] *= 1 / 8
suite[12] *= 1 / 12
suite.Normalize()
print(suite)
class Dice(Suite):
# hypo is the number if sides in the die
# dat is the outcome
def Likelihood(self, data, hypo):
return 0 if data > hypo else 1 / hypo
from thinkbayes2 import Pmf
import thinkplot
d6 = Pmf()
for x in [1, 2, 3, 4, 5, 6]:
d6[x] = 1
d6.Normalize()
print(d6)
twice = d6 + d6
# If the sum of two dice is greater than 3, then we update the dictionary
twice[2] = 0
twice[3] = 0
twice.Normalize()
print(twice)
thinkplot.Hist(twice)
thinkplot.show()
#!/usr/env python
import sys
sys.path.append("../lib/ThinkBayes2/code/")
from thinkbayes2 import Pmf
pmf = Pmf()
for x in [1, 2, 3, 4, 5, 6]:
pmf.Set(x, 1 / 6.0)
print(pmf)
words = Pmf()
f = open('alice.txt', 'r')
for line in f:
for word in line.split():
words.Incr(word.strip(), 1)
#print(words)
words.Normalize()
print(words)
print("Finished")
from thinkbayes2 import Pmf # Cookie Problem # Prior 50%-50% cookie = Pmf(['Bowl 1', 'Bowl 2']) # p (Vanilla|Bowl1) = 30/40 cookie['Bowl 1'] *= 0.75 # p (Vanilla|Bowl2) = 20/40 cookie['Bowl 2'] *= 0.5 # Normalize, return values is p(D) cookie.Normalize() # Posteriors print(cookie) # Suppose we put the first cookie back, stir, choose again from the same bowl, and # get a chocolate cookie # p (Chocolate|Bowl1) = 10/40 cookie['Bowl 1'] *= 0.25 # p (Chocolate|Bowl2) = 20/40 cookie['Bowl 2'] *= 0.5 cookie.Normalize() print(cookie)
