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数理統計 : モデルを選ぶ
****専攻 **** ****
1
**********************
研究室のMy本棚
情報幾何 漸近論 数理統計 その他 印刷した論文
2
線形回帰 ≹ ∽≡∰ ∫≡∱≸
0.0 0.2 0.4 0.6 0.8 1.0
1.01.21.41.61.82.0
x
y
3
0.0 0.2 0.4 0.6 0.8 1.0
1.01.21.41.61.82.0
x
y
線形回帰 ≹ ∽≡∰ ∫≡∱≸
4
0.0 0.2 0.4 0.6 0.8 1.0
0.00.10.20.3
x
y
データが変わるとモデルも変える
5
0.0 0.2 0.4 0.6 0.8 1.0
0.00.10.20.3
x
y
直線では上手くフィットしない
6
0.0 0.2 0.4 0.6 0.8 1.0
-0.10.00.10.20.3
x
y
0.0 0.2 0.4 0.6 0.8 1.0
-0.10.00.10.20.3
x
cb(x)
2次曲線のほうが当てはまりが良い
7
データを予測するモデル
≹ ∽≡∰ ∫≡∱≸
≹ ∽≡∰ ∫≡∱≸∫≡∲≸∲
∮∮∮
≹ ∽≡∰ ∫≡∱≸∫⊢⊢⊢∫≡≮≸≮
どれを選ぶ?
8
高次のモデルは低次のモデルを含む
≦≡∰ ∫≡∱≸≧⊽≦≡∰ ∫≡∱≸∫≡∲≸∲≧
≡∰∻≡∱∻≡∲ ∲≒ について
高次のモデルほど(データの)表現力が豊か
(i.e. 2次のモデルは1次のモデルを含む)
⇒ 高次のモデルを使えばよい?
9
4次のモデルでやってみた
0.0 0.2 0.4 0.6 0.8 1.0
0.00.10.20.3
x
y
0.0 0.2 0.4 0.6 0.8 1.0
0.00.10.20.3
x
cb(x)2次のモデルとほとんど変わらないような・・・
10
余計な次元は使わない
同様のデータを説明する仮説が二つある場合、
より単純な方の仮説を選択せよ
[オッカムの剃刀]
(同等の性能なら)
シンプルなモデルのほうが良いよ
11
• Akaike Information Criterion 最小化:
出来るだけ次元を削減する
: モデルがデータに当てはまる度合い.≠∨≞⊵∩
が同じなら, 次数が小さいほうが選択される≠∨≞⊵∩
≁≉≃∺∽⊡∲≠∨≞⊵∩∫∲≰
: モデルの次数≰
12
他の基準もある
≁≉≃∺∽⊡∲≠∨≞⊵∩∫∲≰
≂≉≃∺∽⊡∲≠∨≞⊵∩∫≰≬≯≧≮
≍≄≌∺∽≂≉≃∽∲
≇≉≃∺∽⊡∲≠∨≞⊵∩∫∲≴≲≇≈⊡∱
∮∮∮
どれが良いかは宗教論争
13
• 新しい情報量基準を計算:
自分の卒論の宣伝
外れ値を自動的に無視.
≉≃≂ ∺∽≮⊢≤≂∨≞≱∻≞≰⊯∩∫≴≲≞≇⊯∻≂ ≞≈⊡∱
⊯
14
ところで最近よく言われること
ビッグデータ?
(統計やってます)
わたし
はじめてあう人
15
ビッグデータは統計ではない(?)
• 「少ないデータからいかに推測するか」が統計
• 数が多けりゃだいたいどうにかなる
• 「処理できるかどうか」という計算機の問題
いや・・・高級な処理は負荷かかるし・・・
どうせ簡単な統計処理しかできないし・・・
16
• ビッグデータ… ではなく
高次元データ
高次元データが注目されている(らしい)
ビッグデータ = データの数が多い
高次元 = データの種類が多い
17
• 色んな種類のデータがある(=高次元)けど
意味のあるデータは一部しかない
世の中のデータの大半は無意味
意味のあるデータを「選ぶ」
18
• 各個体は多様な遺伝子を持っている:
たとえば遺伝子
遺伝子 a b c d e f g h …
個体1 1 0 0 1 1 1 0 1 …
個体2 0 1 1 1 1 1 0 1 …
個体3 1 1 1 0 1 1 0 1 …
個体4 0 0 0 1 1 0 1 0 …
…
個体100 1 1 1 0 1 0 1 1 …
データの種類(多い)
※データの数=100 (少ない)
19
• 色々なモデルを考える:
どの遺伝子が病気に影響するか?
遺伝子aのみが影響すると仮定したモデル
遺伝子bのみが影響すると仮定したモデル
遺伝子a,bが影響すると仮定したモデル
遺伝子a,b,d,e,…が影響すると仮定したモデル
全ての遺伝子が影響すると仮定したモデル
…
モ
デ
ル
を
選
ぶ
20
あるモデルが選ばれた
遺伝子a,d,e,hが影響すると仮定したモデル
が選ばれたとする.
遺伝子a,d,e,hが病気に影響していると考えられる.
※ホントはこの表現はよくない
その他の遺伝子は無関係.
21
• (とりあえず)色んな種類のデータを用意する.
• 有用なデータを「選ぶ」.
どのデータが有用かは分からない
学生のテストの点数を予測したい
•身長
•体重
•視力
•塾に通った年数、…etc
有用なデータを
「選ぶ」
22
• 不要なデータを自動的に無視: スパース推定
実はモデルを選ぶまでもない
Least
Absolute
Shrinkage and
Selection
Operator
≌≁≓≓≏∺∽≡≲≧≭≩≮⊯
⊩≫≹⊡≘⊯≫∲∲ ∫⊸≫⊯≫∱
⊪
23
ディスカッションの時間があるらしい…
24
• 数理的なテクニックのイメージを掴むこと
• 証明には(あんまり)興味が無い
• プログラムは書か(け)ないが
アルゴリズムを考えるのは好き
• 工学系の話の数理的な裏付けとか大好き
自分の興味対象:
おわり.
25

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