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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160
■
13
章
シミュレーション
# d0
から
d1
に何回倍増が必要か
doublings = log2(d1 / d0)
#
これから倍増時間はどうなるか
dt = interval / doublings
#
体積倍増時間と
RDT
を計算する
vdt = dt / 3
rdt = 365 / vdt
dt
は径倍増時間、
vdt
は体積倍増時間、
rdt
は回帰倍増時間である。
径測定における倍増の回数は、
7.3
なので、
RDT
は
2.4
ということになる。
Zhang
等
のデータから、観察期間に
20%
の腫瘍だけが、このような速さで成長した。したがって、
再度、「どちらかと言えば」腫瘍が退役時より前にできたと結論できる。
この計算で、元の質問に答えるには十分であり、メッセージを書いた人のために、退役軍
人給付管理局（
Veterans Benefits Administration
）に私の結論を説明した手紙を送った。
後になって、腫瘍学者である友達にこの結果の話をした。
Zhang
らが成長率を観察
し、それが腫瘍の年齢を意味することについて驚き、結果は研究者や医者にとって興
味深いものだと言った。
これらを有用なものとするために、私は、年齢とサイズの関係についてより一般的な
モデルを作りたいと思った。
13.3
より一般的なモデル
診断時に腫瘍のサイズが与えられたとき、ある日付の前にその腫瘍ができた確率、
言い換えれば、腫瘍年齢の分布を知ることができれば有用だろう。
それを探すために、腫瘍成長のシミュレーションを実行し、年齢ごと
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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.