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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90
■
8
章
観察者バイアス
「バイアス付き
z
」の
zb
を示す。
8.3
待ち時間
乗客が駅に到着してから次の電車が来るまでの待ち時間を
y
とする。前の電車の到
着から乗客の到着までの経過時間を
x
とする。私のこの定義では、
zb = x + y
となる。
zb
の分布がわかっていれば、
y
の分布を計算できる。単純な事例から始めて一般化
することにしよう。前の例と同様に、
zb
が確率
1/3
で
5
分であるか、確率
2/3
で
10
分で
あると仮定しよう。
5
分間にランダムに到着するとすれば、
y
は
0
分から
5
分の間に一様に分布する。
10
分間の間に到着するとすれば、
y
は
0
から
10
の間に一様に分布する。したがって、全体
の分布は、それぞれの間隔の確率にしたがって重み付けした一様分布の混合になる。
次の関数は、
zb
の分布を引数に取って、
y
の分布を計算する。
def PmfOfWaitTime(pmf_zb):
metapmf = thinkbayes.Pmf()
for gap, prob in pmf_zb.Items(
):
uniform = MakeUniformPmf(0, gap)
metapmf.Set(uniform, prob)
pmf_y = thinkbayes.MakeMixture(metapmf)
return pmf_y
PmfOfWaitTime
は、メタ
Pmf
で、各一様分布を確率に対応付ける。それから、「5.6
混合」で見た
MakeMixture
を使って混合を計算する。
PmfOfWaitTime
は
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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.