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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136
■
11
章
仮説検定
like = x**heads * (1-x)**tails
return like
Euro
スイートを作成して、
Likelihood
を呼び出せば、これを使うことができる。
suite = Euro()
likelihood = suite.Likelihood(data, 50)
p(
D
|
F
)
は、
5.5
・
10
−76
であり、特定のデータ集合を取る確率が非常に小さいことを
除いては、大して情報がない。比率を出すのに
2
つの尤度がいるので、
p(
D
|
B
)
も計算
しないといけない。
B
の尤度をどのように計算するかは、「偏っている」とは何を意味するかが明白ではな
いので、はっきりしない。
1
つの可能性は不正行為になるが、仮説を定義する前に、データに目を通すことであ
る。その場合、「偏っている」とは表の出る確率が
140/250
であるということを意味する。
actual_percent = 100.0 * 140 / 250
likelihood = suite.Likelihood(data, actual_percent)
今回の
B
を、
B_cheat
と呼ぶ。
b_cheat
の尤度は
34
・
10
−76
であり、尤度比は
6.1
とな
る。したがって、データは今回の
B
を支持する証拠であると言える。
しかし、仮説を定式化するためにデータを用いることは、明らかにインチキだ。この
定義では、表の出る観察されたパーセントが正確に
50%
でない限り、どんなデータも
B
の証拠となってしまう。
11.2
公正な比較を行
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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.