August 2015
Intermediate to advanced
272 pages
5h 46m
Japanese
Content preview from Think Stats 第2版 ―プログラマのための統計入門
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59
5
章
分布をモデル化する
†
いままでに扱った分布は、経験分布（
empirical distributions
）と呼ばれています。
というのも、いままで登場したものが実際の観察に基づいた分布だったからです。当
然、標本サイズは有限になります。
この他に、数学的な関数として与えられた
CDF
で特徴付けられる、解析分布
（
analytic distribution
）
‡
があります。解析分布は、経験分布をモデル化するのに使え
ます。この文脈でモデル（
model
）とは、不必要な詳細を省いた単純化のことです。
本章では、よく使われる解析分布を示し、それらを使ってさまざまな情報源からのデー
タをモデル化します。
5.1
指数分布
比較的簡単なので指数分布（
exponential distributio
）から始めましょう。指数分
布の
CDF
は次のようになります。
CDF(x)
＝
1
−
e
−
λ
x
母数（パラメータ）
λ
が分布の形状を決定します。図
5-1
は、
λ
＝
0.5, 1, 2
のとき
に
CDF
がどのように見えるかを示しています。
†
本章のコードは、
analytic.py
にある。コードのダウンロードや扱い方については、
viii
ページの「コード
を使う」を参照してほしい。
‡
訳注：普通は、連続分布（
continuous distribution
）という用語を使う。本書初版も連続分布とした。解析
分布は、あまり使われていない。
60
5 章 分布をモデル化する
図
5-1
さまざまな母数での指数分布の
CDF
実世界において指数分布は、一連の事象を観測し ...
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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.