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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174
■
14
章
階層的モデル
14.3
階層化する
前節では、
r
が既知と仮定した。この仮定を緩めよう。
Emitter
と呼ぶ別のスイート
を定義して、放射源の振る舞いをモデル化し、
r
を推定する。
class Emitter(thinkbayes.Suite):
def
__
init
__
(self, rs, f=0.1):
detectors = [Detector(r, f) for r in rs]
thinkbayes.Suite.
__
init
__
(self, detectors)
rs
は、
r
の仮説値の列、
detectors
は、
r
の各値に対応する
Detector
オブジェクト
の列である。スイートの値が
Detector
オブジェクトなので、
Emitter
はメタスイート
（
metaSuite
）、すなわち値としてスイートを含むスイートとなる。
Emitter
の更新には、
r
の各仮説値の下でデータの尤度を計算する必要がある。
r
の
各値は、ある範囲の
n
の値を持つ
Detector
によって表される。
与えられた
Detector
からデータの尤度を計算するには、
n
の値をループして調べな
がら
k
の全確率を足し合わせる。
SuiteLikelihood
がそれを次のようにする。
# class Detector
def SuiteLikelihood(self, data):
total = 0
for hypo, prob in self.Items():
like = self.Likelihood(data, hypo) ...
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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.