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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9.8
議論
■
115
9.8
議論
本章は、これまでの章のベイズフレームワークを
2
次元パラメータ空間で扱えるよう
に拡張できることを示した。これまでとの唯一の違いは、各仮説がパラメータの組で表
されることである。
私は、ジョイント分布に適用される
Marginal
,
Conditional
,
MakeLikeInterval
と
いうメソッドを提供する親クラスの
Joint
も提示した。オブジェクト指向の用語で、
Joint
はミックスイン（
mixin
）である（
http://en.wikipedia.org/wiki/Mixin
参照）
＊
1
。
本章では多数の新用語が出てきた。それらは次のようになる。
ジョイント分布（Jointdistribution）
多次元空間におけるすべての取り得る値と確率を表す分布である。ジョイント
分布は、各
(
alpha, beta)
対の確率を表す。
周辺分布（marginaldistribution）
ジョイント分布で他方のパラメータを未知として扱う
1
つのパラメータの分布
である。例えば、図 9-3 は、
alpha
と
beta
の分布を独立に示す。
条件付き分布（conditionaldistribution）
ジョイント分布の
1
つのパラメータで、
1
つ以上の他のパラメータの条件下に
あるもの。図 9-4 は、
beta
の異なる値を条件として、
alpha
のいくつかの分布
を示している。
ジョイント分布が与えられたとき、周辺分布と条件付き分布とを計算できる。十分な
条件付き分布があれば、ジョイ
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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.