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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124
■
10
章
ベイズ計算を近似する
10.6
Log-Likelihoood
（対数尤度）
ここで必要なのは、
LogLikelihood
である。
# class Height
def LogLikelihood(self, data, hypo):
x = data
mu, sigma = hypo
loglike = scipy.stats.norm.logpdf(x, mu, sigma)
return loglike
norm.logpdf
は、正規分布（ガウス）
PDF
の対数尤度を計算する。
更新処理全体は次のようになる。
suite.Log()
suite.LogUpdateSet(xs)
suite.Exp()
suite.Normalize()
復習すると、
Log
がスイートを対数変換する。
LogUpdateSet
は、
LogUpdate
を呼び出
し、さらに、
LogLikelihood
を呼び出す。対数尤度の足し算が、尤度による乗算と同じ
ため、
LogUpdate
は
Pmf.Incr
を使う。
更新の後で、対数尤度は、大きな負数になっているので、
Exp
は変換を戻す前に、そ
れらを上にシフトする。これがアンダーフローを避けるやり方である。
スイートが元に変換されると、確率は再び「線形」となるが、これは、「対数ではない」
ということなので、再度
Normalize
を使う。
このアルゴリズムを用いることで、アンダーフローを起こさずにデータ集合全体を処
理できるが、まだ遅い。私のコンピュータだと、
1
時間はかかるだろ ...
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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.