def PlotCoefVariation(suites):
"""Plot the posterior distributions for CV.
suites: map from label to Pmf of CVs.
"""
thinkplot.Clf()
thinkplot.PrePlot(num=2)
pmfs = {}
for label, suite in suites.items():
pmf = CoefVariation(suite)
print('CV posterior mean', pmf.Mean())
cdf = thinkbayes2.MakeCdfFromPmf(pmf, label)
thinkplot.Cdf(cdf)
pmfs[label] = pmf
thinkplot.Save(root='variability_cv',
xlabel='Coefficient of variation',
ylabel='Probability')
print('female bigger',
thinkbayes2.PmfProbGreater(pmfs['female'], pmfs['male']))
print('male bigger',
thinkbayes2.PmfProbGreater(pmfs['male'], pmfs['female']))
def Update(self, data):
a_sat, b_sat = data
a_like = thinkbayes2.PmfProbGreater(a_sat, b_sat)
b_like = thinkbayes2.PmfProbLess(a_sat, b_sat)
c_like = thinkbayes2.PmfProbEqual(a_sat, b_sat)
a_like += c_like / 2
b_like += c_like / 2
self.Mult('A', a_like)
self.Mult('B', b_like)
self.Normalize()
def ComparePosteriorPredictive(a_sat, b_sat):
"""Compares the predictive distributions of raw scores.
a_sat: posterior distribution
b_sat:
"""
a_pred = a_sat.MakePredictiveDist()
b_pred = b_sat.MakePredictiveDist()
#thinkplot.Clf()
#thinkplot.Pmfs([a_pred, b_pred])
#thinkplot.Show()
a_like = thinkbayes2.PmfProbGreater(a_pred, b_pred)
b_like = thinkbayes2.PmfProbLess(a_pred, b_pred)
c_like = thinkbayes2.PmfProbEqual(a_pred, b_pred)
print('Posterior predictive')
print('A', a_like)
print('B', b_like)
print('C', c_like)