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'))
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)
pmf.Normalize()
print(pmf)
# Create a Cookie class that inherits from Pmf and represents the Cookie problem
class Cookie(Pmf):
proportions = {
'Bowl 1': dict(vanilla=0.75, chocolate=0.25),
'Bowl 2': dict(vanilla=0.5, chocolate=0.5),
}
# http://www.greenteapress.com/thinkbayes/thinkbayes.pdf
# http://thinkbayes.com/thinkbayes.py
# python -m pip install scipy numpy matplotlib pandas
from thinkbayes2 import Pmf
pmf = Pmf()
pmf.Set('tazon1', 0.5)
pmf.Set('tazon2', 0.5)
pmf.Mult('tazon1', 0.75)
pmf.Mult('tazon2', 0.5)
pmf.Normalize()
print(pmf.Prob('tazon1'))
