September 2014
Intermediate to advanced
236 pages
4h 17m
Japanese
Content preview from Think Bayes ―プログラマのためのベイズ統計入門
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7.4
ゴールの分布
■
79
7.4
ゴールの分布
各チームが次の試合に勝つ確率を計算するには、各チームについてゴールの分布を
計算する必要がある。
lam
の値が正 確にわかっているなら、ポワソン分 布を再度使うことができる。
thinkbayes
には、ポワソン分布の打ち切り近似を計算するメソッドが用意されている。
def MakePoissonPmf(lam, high):
pmf = Pmf()
for k in xrange(0, high+1):
p = EvalPoissonPmf(lam, k)
pmf.Set(k, p)
pmf.Normalize()
return pmf
計算された
Pmf
の値の範囲は、
0
から
high
までである。したがって、
lam
の値が正確
に
3.4
だとすれば、次のように計算できる。
lam = 3.4
goal_dist = thinkbayes.MakePoissonPmf(
lam, 10)
ここで、上限に
10
を選んだ。その理由は、試合で
10
以上ゴールを決める確率は極め
て低いからだ。
ここまでは単純そのものだ。しかし、
lam
の値を正確には把握できないという問題が
ある。その代わりに、
lam
の取り得る値の分布がある。
lam
の取り得る値それぞれについて、ゴールの分布はポワソン分布になる。したがっ
て、ゴールの分布全体は、
lam
の分布の確率に従った重み付けによるポワソン分布の混
合になる。
lam
の事後確率分布があれば、ゴールの分布を作るコードは次のようになる。
def MakeGoalPmf ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.