Example #1
0
    def Update(self, data):
        a_sat, b_sat = data

        a_like = thinkbayes.PmfProbGreater(a_sat, b_sat)
        b_like = thinkbayes.PmfProbLess(a_sat, b_sat)
        c_like = thinkbayes.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()
Example #2
0
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 = thinkbayes.PmfProbGreater(a_pred, b_pred)
    b_like = thinkbayes.PmfProbLess(a_pred, b_pred)
    c_like = thinkbayes.PmfProbEqual(a_pred, b_pred)

    print 'Posterior predictive'
    print 'A', a_like
    print 'B', b_like
    print 'C', c_like
Example #3
0
def main():
    #ReadHockeyData()
    #return

    formats = ['pdf', 'eps']

    suite1 = Hockey('bruins')
    suite2 = Hockey('canucks')

    thinkplot.Clf()
    thinkplot.PrePlot(num=2)
    thinkplot.Pmf(suite1)
    thinkplot.Pmf(suite2)
    thinkplot.Save(root='hockey0',
                   xlabel='Goals per game',
                   ylabel='Probability',
                   formats=formats)

    suite1.UpdateSet([0, 2, 8, 4])
    suite2.UpdateSet([1, 3, 1, 0])

    thinkplot.Clf()
    thinkplot.PrePlot(num=2)
    thinkplot.Pmf(suite1)
    thinkplot.Pmf(suite2)
    thinkplot.Save(root='hockey1',
                   xlabel='Goals per game',
                   ylabel='Probability',
                   formats=formats)

    goal_dist1 = MakeGoalPmf(suite1)
    goal_dist2 = MakeGoalPmf(suite2)

    thinkplot.Clf()
    thinkplot.PrePlot(num=2)
    thinkplot.Pmf(goal_dist1)
    thinkplot.Pmf(goal_dist2)
    thinkplot.Save(root='hockey2',
                   xlabel='Goals',
                   ylabel='Probability',
                   formats=formats)

    time_dist1 = MakeGoalTimePmf(suite1)
    time_dist2 = MakeGoalTimePmf(suite2)

    print('MLE bruins', suite1.MaximumLikelihood())
    print('MLE canucks', suite2.MaximumLikelihood())

    thinkplot.Clf()
    thinkplot.PrePlot(num=2)
    thinkplot.Pmf(time_dist1)
    thinkplot.Pmf(time_dist2)
    thinkplot.Save(root='hockey3',
                   xlabel='Games until goal',
                   ylabel='Probability',
                   formats=formats)

    diff = goal_dist1 - goal_dist2
    p_win = diff.ProbGreater(0)
    p_loss = diff.ProbLess(0)
    p_tie = diff.Prob(0)

    print(p_win, p_loss, p_tie)

    p_overtime = thinkbayes.PmfProbLess(time_dist1, time_dist2)
    p_adjust = thinkbayes.PmfProbEqual(time_dist1, time_dist2)
    p_overtime += p_adjust / 2
    print('p_overtime', p_overtime)

    print(p_overtime * p_tie)
    p_win += p_overtime * p_tie
    print('p_win', p_win)

    # win the next two
    p_series = p_win**2

    # split the next two, win the third
    p_series += 2 * p_win * (1 - p_win) * p_win

    print('p_series', p_series)