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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2.3
ベイズ・フレームワーク
■
13
pmf = Pmf()
pmf.Set('Bowl 1', 0.5)
pmf.Set('Bowl 2', 0.5)
各仮説についての事前確率を含むこの分布は、事前確率分布（
prior distribution
）と
呼ばれる。
新しいデータ（バニラクッキー）に基づいて分布を更新するには、各事前確率に対応
する尤度を掛ける。ボウル
1
からバニラクッキーを取り出す確率は
3/4
となる。ボウル
2
の尤度は
1/2
となる。
pmf.Mult('Bowl 1', 0.75)
pmf.Mult('Bowl 2', 0.5)
Mult
は、読者が予想する通りの動きをする。すなわち、指定された仮説の確率を取
り出し、それに指定された尤度を掛ける。
この更新後、分布は正規化されていないが、これらの仮説は相互排他で全体網羅な
ので、再正規化（
renormalize
）できる。
pmf.Normalize()
結果は、各仮説に対する事後確率の分布で、事後確率分布（
posterior distribution
）
と呼ばれる。
最終的に、ボウル
1
の事後確率が得られる。
print pmf.Prob('Bowl 1')
答えは
0.6
である。この例は、
http://thinkbayes.com/cookie.py
からダウンロードで
きる。詳細については、まえがきの「コードについて」（
ix
ページ）を参照のこと。
2.3
ベイズ・フレームワーク
他の問題に移る前に、前節のコードを書き直して一般的なものにしておきたい。最
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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.