Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
初等確率論の基礎
Search
Koga Kobayashi
August 17, 2020
Research
210
1
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
初等確率論の基礎
「ベイズ統計の理論と方法」勉強会の資料
Koga Kobayashi
August 17, 2020
More Decks by Koga Kobayashi
See All by Koga Kobayashi
第13回 Data-Centric AI勉強会, LLMのファインチューニングデータ
kajyuuen
4
1.9k
基礎数学の公式
kajyuuen
1
190
Deep Markov Model を数式で追う (+ Pyroでの追試)
kajyuuen
0
960
Fundamentals of Music Processing (Chapter 5)
kajyuuen
0
110
完全なアノテーションが得られない状況下での固有表現抽出
kajyuuen
3
3.7k
SecHack365 北海道会 LT
kajyuuen
0
550
専門用語抽出手法の研究と 抽出アプリケーションの開発
kajyuuen
1
1.3k
Other Decks in Research
See All in Research
SAKURAONE:An Open Ethernet-based AI HPC System And Its Observed Workload Dynamicsin a Single-Tenant LLM Development Environment
yuukit
1
590
研究室単位での自律的 IPv6接続性確立に向けたAS共同運用モデルの提案と実証
reokashiwa
PRO
0
210
Language and AI
ayaniwa
0
220
シングルチャネルマルチトーカー音声認識の進展
ryomasumura
0
260
Apache Gravitinoで実現する Icebergカタログ統合とアクセスの一元化
matsumooon
0
500
COMETAを用いたデータ民主化運動の歴史
sazimai
0
240
人間中心の意思決定支援AI
yukinobaba
PRO
7
4k
Visual SLAM未来予測 / Future Prediction in Visual SLAM
koide3
1
1k
Model Discovery and Graph Simulation: A Lightweight Gateway to Chaos Engineering
anatolykr
0
300
[CV勉強会@関東 CVPR2026] PSDesigner: Automated Graphic Design with a Human-Like Creative Workflow / kantocv 67th CVPR 2026
shunk031
0
290
LA-Bench 2025:実験指示から実行可能手順を生成するためのデータセット/LA-Bench 2025: A Dataset for Generating Executable Experimental Procedures from Experimental Instructions
stktu
0
150
マーケットストリート 社会実験2024 in 秋葉原ジャンク通り 調査報告書
izumiyama_lab
1
140
Featured
See All Featured
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
659
62k
YesSQL, Process and Tooling at Scale
rocio
174
15k
Color Theory Basics | Prateek | Gurzu
gurzu
0
460
Learning to Love Humans: Emotional Interface Design
aarron
275
41k
Introduction to Domain-Driven Design and Collaborative software design
baasie
1
970
Marketing Yourself as an Engineer | Alaka | Gurzu
gurzu
0
290
Public Speaking Without Barfing On Your Shoes - THAT 2023
reverentgeek
1
560
Lightning Talk: Beautiful Slides for Beginners
inesmontani
PRO
2
670
SEOcharity - Dark patterns in SEO and UX: How to avoid them and build a more ethical web
sarafernandez
0
270
Paper Plane (Part 1)
katiecoart
PRO
1
11k
From Legacy to Launchpad: Building Startup-Ready Communities
dugsong
0
320
Side Projects
sachag
455
43k
Transcript
ॳ֬ͷجૅ ϕΠζ౷ܭͷཧͱํ๏ษڧձ
֬ͱ֬ม
֬ ϢʔΫϦουۭؒ ͷݩ ͷؔ ͕ ℝN x = (x1 ,
…, xN ) q(x) ≥ 0 ∫ q(x)dx ≡ ∫ dx1 ∫ dx2 … ∫ dxN q(x1 , x2 , ⋯, xN ) = 1 Λຬͨ͢ͱ͖ Λ֬͋Δ͍֬ີؔͱ͍͏ɻ q(x) ू߹ ʹ͍ͭͯɺ ͷݩͰͷू߹ ͷ֬ A ⊂ ℝN q(x) A Q(A) = ∫ A q(x)dx ͜ͷͱ͖ɺؔ ֬ͱ͍͏ɻ Q( ⋅ )
֬ม ϢʔΫϦουۭؒ ͷ্ʹϥϯμϜʹΛऔΔม Λ ʮ ʹΛऔΔ֬มʯͱ͍͏ɻ ℝN X ℝN ʮ
ͱͳΔ֬ʯ͕ Ͱ͋Δͱ͖ ʮ֬ม ͷ֬ Ͱ͋Δʯ͋Δ͍ ʮ֬ม ͷ֬ ʹै͏ʯ͋Δ͍ ʮ֬ม ͷ֬ Ͱ͋Δʯͱ͍͏ɻ X ∈ A Q(A) X q(x) X q(x) X Q
۩ମྫਅͷ αϯϓϧ ͕͋Δ֬ ʹಠཱʹै͏ ֬มͷ࣮ݱʢ؍ଌʣͩͱ͢Δɻ A = xn = {x1
, …, xn } ⊂ ℝN q(x) ͢ͳΘͪ Λ ্ͷ xn (ℝN)n q(xn) = n ∏ i=1 q(xi ) = q(x1 )q(x2 )⋯q(xn ) Λ࣋ͭ֬ม ͷ࣮ݱͰ͋Δͱߟ͑Δɻ ͜ͷͱ͖֬ ΛਅͷͱݺͿɻ Xn = (X1 , X2 , …, Xn ) q(x)
ฏۉͱࢄ
ฏۉͱࢄ ʹΛͱΔ֬ม ͷ֬Λ ͱ͢Δɻ ℝN X q(x) [f(X)] ≡ ∫
f(x)q(x)dx [f(X)] ≡ [(f(X) − [f(X)])(f(X) − [f(X)])T] = [f(X)f(X)T] − [f(X)][f(X)T] ͱఆٛ͢Δɻ ͕༩͑ΒΕͨͱ͖ɺ֬ม ͷฏۉΛ f : ℝN → ℝM f(X) ·ͨࢄڞࢄΛ ͱఆٛ͢Δɻ֬มΛ໌ه͍ͨ͠ͱ͖ ͱॻ͘ɻ X [f(X)]
۩ମྫαϯϓϧͷฏۉ αϯϓϧ Λද֬͢มΛ ͱ͢Δɻ ͦͷؔ ͕༩͑ΒΕͨͱ͖ɺͦͷฏۉΛऔΔૢ࡞ Λ xn = {x1
, …, xn } Xn = (X1 , X2 , …, Xn ) f(Xn) [ ⋅ ] ͱදه͢Δɻ ͜ͷฏۉ ΛʮαϯϓϧͷݱΕํʹର͢ΔฏۉʯͱݺͿɻ [ ⋅ ] [f(XN)] = ∫ ∫ ⋯ ∫ f(x1 , …, xn ) n ∏ i=1 q(xi )dxi
۩ମྫਅͷͷฏۉ αϯϓϧͷ֬มΛ Λ༻͍ͯɺ ਅͷ ͷਪଌΛߦͬͨޙɺਅͷͷ֬ม Λൃੜͤͯ͞ ਪଌ݁ՌͷΑ͞ΛධՁ͍ͨ͠ɻ ͜ͷ֬ม ͷؔ ʹ͍ͭͯͷฏۉΛ
Xn = (X1 , X2 , …, Xn ) q(x) X X f(X) ͱදه͢Δɻ [f(X)]X = ∫ f(x)q(x)dx
X X−1 ۭ֬ؒ(Ω = ℝM, ℬ, p) w ∈ Ω
ٯ૾X−1(A) ֬ີؔ ֬ q(x) = p(X−1(x)) Մଌۭؒ(Ω′ = ℝN, ℬ′ ) A ∈ ℬ′ X(w) = X x ∈ Ω′ ֬Q(A) = ∫ A q(x)dx f(x) ฏۉ[f(X)] ≡ ∫ f(x)q(x)dx = ∫ f(x)p(X−1(x))dx = ∫ p(w)X(w)dw = ∫ pXdw ֬ม ֬มͱ֬ɺฏۉͷؔ ۭ֬ؒ(Ω′ = ℝN, ℬ′ , q)
ಉ࣌ͱ͖݅֬
ಉ࣌ͱ͖݅ ͭͷ֬ม ͱ ͕͋Δͱ͖ɺͦͷ ͷ͕֬ Ͱ͋Δͱ͖ɺ Λಉ࣌֬ͱ͍͏ɻ X Y (X,
Y) p(x, y) p(x, y) ·ͨ֬ม ͕༩͑ΒΕͨͱ͖ͷ ͷ͖݅֬Λ࣍ͷΑ͏ ʹఆٛ͢Δɻ X Y p(y|x) = p(x, y) p(x) पล֬࣍ͷΑ͏ʹఆٛ͢Δɻ p(x) = ∫ p(x, y)dy p(y) = ∫ p(x, y)dx
ճؼؔ ֬ม ͷ֬ ʹ͍ͭͯߟ͑Δɻ ͷͱ͖ͷ ͷฏۉΛ (X, Y) p(X, Y)
X = x Y ͱॻ͘ɻ͜ͷؔΛ ͔Β ͷճؼؔ ͖݅ظ ͱ͍͏ɻ x y [Y|x] = ∫ yp(y|x)dy ؔΛ ͕༩͑ΒΕͨͱ͖ͦͷೋޡࠩΛද͢൚ؔΛ y = f(x) [(Y − f(X))2] = ∫ ∫ (y − f(x))2p(y, x)dxdy ͱॻ͘ͱ͜Ε ͷͱ͖ʹ࠷খʹͳΔɻ f(x) = [Y|x]
ΧϧόοΫɾϥΠϒϥใྔ
ΧϧόοΫɾϥΠϒϥใྔ ্ʹೋͭͷ֬ ͕͋Δͱ͖ ℝN q(x), p(x) D(p∥q) = ∫
q(x)log q(x) p(x) dx ͷ͜ͱΛΧϧόοΫɾϥΠϒϥใྔ͋Δ͍૬ରΤϯτϩϐʔͱݺͿ ΧϧόοΫɾϥΠϒϥใྔ͕࣍Γཱͭɻ ʹ͍ͭͯ Ͱ͋Δɻ ͱͳΔͷ ͷͱ͖ʹݶΔɻ ∀q(x), p(x) D(q∥p) ≥ 0 D(q∥p) = 0 q(x) = p(x)
ΧϧόοΫɾϥΠϒϥใྔ ূ໌ ͱ͓͘ͱɺ Ͱ͋Γɺ F(t) = 0 ⇔ t
= 0 F(t) = t + et − 1 (−∞ < t < ∞) ΑΓ Ͱ͋Δ͔Β͕ࣔ͞Εͨɻ ∫ q(x)dx = 1 ∫ p(x)dx = 1 ∫ log q(x) p(x) dx = 0 ·ͨɺ ͷͱ͖ɺ Ͱ Ͱ͋Δ͜ͱΛ༻͍ͯ q(x) ≈ p(x) t ≈ 0 F′ ′ (t) ≃ t2/e D(p∥q) ≃ ∫ q(x)(log q(x) − log p(x))2dx ͕Γཱͭɻ
ۃݶఆཧ
֬มͷऩଋ
֬ऩଋ ֬ม ͕ఆ ʹ֬ऩଋ͢Δͱ ʹର͠ɺ ʹ͓͍ͯ {Xn }n∈ℕ c ∀ϵ,
∀δ > 0 ∃N ∈ ℕ n > N ⇒ P(∥Xn − c∥ > ϵ) < δ ⇔ P(∥Xn − c∥ < ϵ) = 1 ͱͳΔ͜ͱͰ͋Δɻ ͜Εେͷऑ๏ଇʹରԠ͍ͯ͠Δɻ Xn c ϵ ඪຊ͕ेʹେ͖͍ͱ͖ɺඪຊฏۉฏۉʹऩଋ͢Δ
๏ଇ ऩଋ ֬มͷྻ ͕֬ม ʹ๏ଇ ऩଋ͢Δͱ ͷ͕֬ Ͱ
ͷ͕֬ Ͱ͋Δͱ͖ɺ ҙͷ༗ք͔ͭ࿈ଓͳؔ ʹରͯ͠ {Xn }n∈ℕ X Xn qn (x) X q(x) F(x) lim n→∞ ∫ F(x)qn (x)dx = ∫ F(x)q(x)dx ⇔ lim n→∞ [F(Xn )] = [F(X)] ͕Γཱͭ͜ͱͰ͋Δɻ͜Εத৺ۃݶఆཧʹରԠ͍ͯ͠Δɻ ඪຊ͕ेʹେ͖͍ͱ͖ɺूஂͷʹؔΘΒͣඪຊฏۉͱฏۉͷࠩਖ਼نʹै͏
ܦݧաఔ
ϢʔΫϦουۭؒʹ͓͚ΔίϯύΫτੑ ϢʔΫϦουۭؒ ͷ෦ू߹ ͕։ू߹ͷ ʹ ͍ͭͯ ͳΒɺͦͷ༗ݶݸͷ։ू߹ Ͱ ℝN W
= {O}λ∈Λ W ⊂ ⋃ λ∈Λ Oλ O1 , …, On ∈ ͱͳΔͷ͕͋Δͱ͖ɺ ίϯύΫτͰ͋Δͱ͍͏ W ⊂ O1 ∪ … ∪ On W O1 , …, On ∈ W
্ۭؔؒͷେͷ๏ଇ ϢʔΫϦουۭؒ ʹΛऔΔ ͕֬ม ͱ ಉ֬͡ʹै͏ͱ͢Δɻ ύϥϝʔλͷू߹ ΛίϯύΫτͱ͢Δɻ ℝN X1
, X2 , …, Xn X w ∈ W ∈ ℝN f(x, w) : ℝN → ℝ1 X [ sup w∈W |f(X, w)|] < ∞, X [ sup w∈W |∇w f(X, w)|] < ∞ ݅ ͕ΓཱͭͱԾఆ͢Δɻ͜ͷͱ͖ɺ ʹ͍ͭͯ ∀ϵ > 0 P( sup w∈W 1 n n ∑ i=1 f(Xi , w) − X [f(X, w)] < ϵ) = 1 ͜ͷ͜ͱΛ্ۭؔؒͷେͷ๏ଇͱ͍͏
ਖ਼ن֬աఔ ू߹ ্ͷؔͰ֬తʹมಈ͢Δͷ ͕ɺ ฏۉؔ ͱ૬ؔؔ Λ࣋ͭਖ਼ن֬աఔͰ͋Δͱɺ ֤ ͝ͱʹ ͕ਖ਼نʹै͏֬มͰ͋Γɺ
W ξ(w) m(w) ρ(w, w′ ) w ξ(w) m(w) = ξ [ξ(w)], ρ(w, w′ ) = ξ [ξ(w)ξ(w′ )] ͕Γཱͭ͜ͱͰ͋Δɻ͜͜Ͱ ɺ֬աఔ ʹ͍ͭͯͷฏۉΛ ද͍ͯ͠ΔɻίϯύΫτू߹্Ͱͷਖ਼ن֬աఔɺ ξ [ ⋅ ] ξ ฏۉؔͱ૬͕ܾؔؔ·ΔͱҰҙʹఆ·Δ͜ͱ͕ΒΕ͍ͯΔɻ
ܦݧաఔ ͭ͗ʹ X[ sup w∈W |f(X, w) − X
[f(X, w)]|α ] < ∞ X[ sup w∈W |∇w (f(X, w) − X [f(X, w)])|α ] < ∞ ͕ ͰΓཱͭͱԾఆ͢Δɻ α = 2 Yn (w) = 1 n n ∑ i=1 (f(Xi , w) − X [f(X, w)]) ͜ͷ֬աఔ Λܦݧաఔͱ͍͏ɻ Yn (w)
ܦݧաఔ ֬աఔ ܦݧաఔ ฏۉ͕ Ͱ૬͕ؔؔ Yn (w) 0
ͷਖ਼ن֬աఔ ʹ๏ଇऩଋ͢Δɻ Y(w) ρ(w, w′ ) = X [f(X, w)f(X, w′ )] − X [f(X, w)]X [f(X, w′ )]
֬աఔͷ๏ଇऩଋ ֬աఔ ܦݧաఔ ͕֬աఔ ʹ๏ଇऩଋ͢Δͱɺ ༗ք࿈ଓͳ൚ؔ ʹ͍ͭͯ Yn (w)
Y(w) F( ⋅ ) ͕Γཱͭͱ͍͏͜ͱͰ͋Δɻͳ͓ɺ൚ؔ ͕࿈ଓͰ͋Δͱ F( ⋅ ) lim n→∞ [F(Yn )] = Y [F(Y)] lim n→∞ sup w∈W |fn (w) − f(w)| → 0 ⇒ lim n→∞ F(fn ) = F(f ) ͕Γཱͭ͜ͱͰ͋Δɻ ͜ͷΑ͏ͳܗͷఆཧΛ্ۭؔؒͷத৺ۃݶఆཧͱ͍͏ɻ
ࢀߟࢿྉ w ֬ೖ ล w ܦݧաఔͱ ล w ϕΠζ౷ܭͷཧͱํ๏ ล
w ଌɾ֬ɾϧϕʔάੵ ݪܒհ