Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
PRML(分類編)
Search
gucchi
September 06, 2019
Science
530
2
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
PRML(分類編)
gucchi
September 06, 2019
More Decks by gucchi
See All by gucchi
PRML(ニューラルネット編)
gucchi
1
340
PRML(回帰編)
gucchi
2
610
PRML第10章
gucchi
1
360
PRMLセミナー(第9章)
gucchi
3
450
PRMLセミナー
gucchi
2
350
PRML第11章
gucchi
1
370
PRMLセミナー
gucchi
1
420
PRMLセミナー
gucchi
1
610
PRML第6章
gucchi
1
74
Other Decks in Science
See All in Science
presen_司法書士学員会.pdf
tagtag
PRO
1
110
データベース05: SQL(2/3) 結合質問
trycycle
PRO
0
1.3k
共生概念の整理と AIアライメントの構想
hiroakihamada
0
340
CVPR2026_VGGTとその仲間たち
mickey_0226
0
1.1k
O(log n)-Approximation Algorithms for Bipartiteness Ratio
tasusu
0
200
J-STAGE全文XML登載必須化について
xspa2012
0
1.5k
白金鉱業Meetup Vol.25 【初学者向け発表枠】「で、この施策って効いてるの」に答える効果検証の基礎 ~ATE / ATT / CATE / LATEを現場の問いに翻訳する~
brainpadpr
0
240
Leitner Inauguration Lecture Chalmers University of Technology
xleitix
0
360
AlgorithAlgorihms for Decision Making
mickey_kubo
0
150
Inside the Mind of an LLM
baggiponte
0
340
機械学習 - K近傍法 & 機械学習のお作法
trycycle
PRO
1
1.7k
(CVPR2026) Back to Basics: Let Denoising Generative Models Denoise
shumpei777
0
350
Featured
See All Featured
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
287
14k
New Earth Scene 8
popppiees
4
2.6k
Navigating Team Friction
lara
192
16k
Building Experiences: Design Systems, User Experience, and Full Site Editing
marktimemedia
1
610
Fashionably flexible responsive web design (full day workshop)
malarkey
409
67k
Skip the Path - Find Your Career Trail
mkilby
1
240
The Curse of the Amulet
leimatthew05
3
15k
Bridging the Design Gap: How Collaborative Modelling removes blockers to flow between stakeholders and teams @FastFlow conf
baasie
0
700
Dominate Local Search Results - an insider guide to GBP, reviews, and Local SEO
greggifford
PRO
0
340
4 Signs Your Business is Dying
shpigford
187
23k
30 Presentation Tips
portentint
PRO
1
400
XXLCSS - How to scale CSS and keep your sanity
sugarenia
250
1.3M
Transcript
PRML ΛࡐʹػցֶशΛਂ͘ཧղ͢Δηϛφʔ ʲྨฤʳ ࡔޱ ྒี 1 / 47
0. ࠓճͷηϛφʔʹ͍ͭͯ ࠓճͷηϛφʔͰɺPRML ͷୈ 4 ষͷઢܗࣝผϞσϧΛத৺ʹ͓ ͍ͨ͠͠ͱࢥ͍·͢ɻ ͳ͓ҙͱͯ͠ɺຊεϥΠυͷࣜ൪߸ͱ PRML ͷࣜ൪߸ҟͳΓ·
͢ͷͰɺ͝ҙ͍ͩ͘͞ɻ 2 / 47
࣍ 1. ༧උࣝ 1-1. ྨͷΞϓϩʔν 2. ϑΟογϟʔͷઢܗผ (PRML 4.1.4) 3.
ϩδεςΟοΫճؼ (PRML 4.3, 4.4, 4.5) 3-1. ϩδεςΟοΫճؼ 3-2. ϩδεςΟοΫճؼͷ࠷ਪఆ 3-3. ϕΠζϩδεςΟοΫճؼ 3 / 47
1. ༧උࣝ ػցֶशɺಛʹͦͷதͰڭࢣ͋ΓֶशͰɺ·ͣೖྗσʔλͷू߹ {x1 , x2 , · · ·
, xN } ͱͦΕͧΕʹରԠ͢ΔඪϕΫτϧͷू߹ {t1 , t2 , · · · , tN } Λ༻ҙ͢Δɻ(܇࿅σʔλɺ·ͨڭࢣσʔλ) ༻ҙͨ͠܇࿅σʔλΛ༻͍ͯɺೖྗσʔλ͔ΒඪϕΫτϧΛ༧ଌ͢Δ ؔ y(x) Λ࡞Δɻ(ֶश) ֶशऴྃޙɺະͷσʔλ x ͷඪϕΫτϧΛ y(x) Ͱ༧ଌ͢Δ ֤ೖྗϕΫτϧΛ༗ݶݸͷࢄతͳΧςΰϦʹׂΓͯΔ߹ (ྫ͑ ɺखॻ͖ࣈͷೝࣝ) ΛΫϥεྨͱ͍͍ɺग़ྗ͕࿈ଓมͷ߹ Λճؼͱ͍͏ɻ ࠓճΫϥεྨͷΛߟ͑Δɻ 4 / 47
1-1. ྨͷΞϓϩʔν ·ͣɺ܇࿅σʔλ͕༩͑ΒΕͨ࣌ͷྨͷΞϓϩʔνΛ 2 ͭհ ͢Δɻ 1 ͭɺ܇࿅σʔλ͔Βࣝผؔ y(x) Λ࡞Δํ๏Ͱ͋Δɻ
͜͜Ͱ x D ࣍ݩͷೖྗϕΫτϧ (ྫ͑ɺn ݸͷ܇࿅σʔλͷೖྗ Ͱ͋Δ xn ) Ͱ͋Δɻ ͜ͷΞϓϩʔνͰɺy(x) ͷΛ༻͍ͯɺ܇࿅σʔλʹͳ͍ະͷ σʔλ x ͕ͲͷΫϥεʹྨ͞ΕΔ͖ͳͷ͔Λ༧ଌ͢Δɻ(ޙͰྫΛ ͋͛Δ͕ɺྫ͑ x y(x) ≥ 0 ͳΒΫϥε 1, y(x) < 0 ͳΒΫϥε 2 ʹଐ͢ΔͳͲ) 5 / 47
1-1. ྨͷΞϓϩʔν ࣮ͨͩ͠ࡍʹྨΛղ͘ͱ͖ɺ܇࿅σʔλΛͬͯؔ y(x) Λ Ұ͔Β࡞Γ্͛Δ͜ͱ͠ͳ͍ɻ Α͘ߦ͏ํ๏ɺೖྗϕΫτϧͱಉ࣍͡ݩͷύϥϝʔλϕΫτϧ (ॏ Έ)w =
(w1 , w2 , · · · , wD )T ͱεΧϥʔύϥϝʔλ (όΠΞε)w0 Λ༻ҙ ͠ɺؔ y(x, w, w0 ) y(x, w, w0 ) = f ( wTx + w0 ) (1.1) Λ༻ҙ͢Δɻ ͜͜Ͱɺf ඇઢܗؔͰ͋Γɺ׆ੑԽؔͱݺΕΔɻ(ྫ͑ϩδ εςΟοΫγάϞΠυؔ) ͦͯ͠ɺڭࢣσʔλʹΑΓؔ y(x, w, w0 ) ͷύϥϝʔλ w, w0 Λਪ ఆ͠ɺਪఆ͞ΕͨύϥϝʔλͷΛ w⋆, w⋆ 0 ͱ͢Δͱɺؔ y(x, w = w⋆, w0 = w⋆ 0 ) ͕ࣝผؔͰ͋Δɻ(ύϥϝʔλͷௐํ๏ ͨ͘͞Μ͋Δɻ) ͜Ε͕ࣝผؔΛ࡞͢Δํ๏Ͱ͋ΓɺࠓճͦͷதͰϑΟογϟʔ ͷઢܗผͱ͍͏ํ๏Λհ͢Δɻ 6 / 47
1-1. ྨͷΞϓϩʔν ͏ҰͭͷΞϓϩʔνͱͯ͠ɺࣝผؔ y(x) ͷ࡞Ͱͳ͘ɺ͖݅ ֬ p(Ck |x) Λ܇࿅σʔλ͔Βܾఆ͢Δํ๏͕͋Δɻ ͜͜ͰɺྨΫϥε
K ݸ (Ϋϥε 1, Ϋϥε 2, · · · , Ϋϥε K) ͋Δ ͱͯ͠ɺCk k ݸͷΫϥεΛද͢ɻ ͳͷͰɺ͖݅֬ p(Ck |x) ɺԿ͔͋Δೖྗ x ͕༩͑ΒΕͨͱ͖ʹ ͦͷೖྗ͕ k ݸͷΫϥεʹଐ͢Δ֬Λ༩͑Δɻ ͜Ε࣮ࡍɺ܇࿅σʔλ͔Β p(Ck |x) ͷܗΛҰ͔ΒܾΊΔͷͰͳ ͘ɺܗܾΊ͓͍ͯͯύϥϝʔλΛ܇࿅σʔλ͔ΒܾΊΔɻ(࠷ਪఆ Λ͢Δɻ) 7 / 47
1-1. ྨͷΞϓϩʔν ͖ͨͩ݅֬͠ͷࣝผؔͱҟͳΔͱ͜Ζɺ͖݅֬ͱͯ͠ Ծఆ͢Δؔͷܗͱͯ͠ɺن֨Խ݅ ∑ k p(Ck |x) = 1
(1.2) Λຬͨ͢Α͏ͳؔΛԾఆ͠ͳ͍͚ͯ͘ͳ͍ɻ ͜ͷΑ͏ʹ͖݅֬ΛϞσϧԽ͢Δ͜ͱʹΑͬͯɺ࠷ਪఆ͔Βϕ ΠζਪఆʹࣗવʹҠߦͰ͖Δɻ ࠓճ۩ମྫͱͯ͠ɺK = 2 ͷ࣌ͷϩδεςΟοΫճؼͷ࠷ਪఆͱ ϕΠζਪఆΛऔΓѻ͏ɻ 8 / 47
2. ϑΟογϟʔͷઢܗผ ͦΕͰɺࣝผؔΛ܇࿅σʔλ͔ΒܾΊΔํ๏ͷ۩ମྫͱͯ͠ϑΟο γϟʔͷઢܗผΛઆ໌͢Δɻ ·ͣ 2 ΫϥεྨΛߟ͑Δɻ(ΫϥεͷϥϕϧΛ C1 ͱ C2
ͱ͢Δɻ) ࣝผؔͱͯ͠ɺ(1.1) ͷ׆ੑؔ f Λ߃ؔͱͨ͠ͷΛߟ͑Δɻ y(x, w, w0 ) = wTx + w0 (2.1) ͦͯ͠ɺN ݸͷ܇࿅σʔλͷೖྗσʔλͱͯ͠ {x1 , x2 , · · · , xN } ͱ͠ɺ ͦΕΒʹରԠ͢Δඪมͱͯ͠ {t1 , t2 , · · · , tN } ͱ͠ɺศ্ٓΫϥε C1 ͕ t = 1 ʹରԠ͠ɺΫϥε C2 ͕ t = 0 ʹରԠ͢Δͱ͢Δɻ ·ͨɺೖྗϕΫτϧ͕ॴଐ͢ΔΫϥεͷఆํ๏Ͱ͋Δ͕ɺ͋Δೖྗϕ Ϋτϧ xi ͕༩͑ΒΕͨ࣌ʹ y(xi , w, w0 ) ≥ 0 Ͱ͋Ε xi Ϋϥε C1 ʹॴଐ͠ (ͭ·Γ ti = 1)ɺy(xi , w, w0 ) < 0 Ͱ͋Ε xi Ϋϥε C2 ʹ ॴଐ͢Δ (ͭ·Γ ti = 0) ͱ͢Δɻ 9 / 47
2. ϑΟογϟʔͷઢܗผ ͦͦɺD ࣍ݩͷೖྗσʔλ x Λ 1 ࣍ݩͷؔ y(x, w,
w0 ) ʹࣹӨ͠ ͯ y ͷਖ਼ෛͰॴଐ͢ΔΫϥεΛܾΊΔͷͰɺ࣍ݩͷݮগʹΑΓஅ͢Δ ͨΊͷใ͕େ͖͘ݮͬͨྔ (ͭ·Γ y) ͰΫϥεΛஅ͢Δ͜ͱʹ ͳΔɻ ͭ·ΓɺD ۭؒͰೖྗσʔλ͕ଐ͢ΔΫϥεͷྖҬಉ͕࢜Α͘ ͞Ε͍͕ͯͨɺ1 ࣍ݩͷࣹӨͷํʹΑͬͯɺͦͷ͕ͳ͘ͳΔ (ॏͳͬͯ͠·͏) ͜ͱ͕͋Δɻ D = 2 ͷ࣌ͷ۩ମྫΛ࣍ͷεϥΠυͰࣔ͢ɻ 10 / 47
2. ϑΟογϟʔͷઢܗผ ੨ͷϓϩοτ͕Ϋϥε C1 ʹॴଐ͢Δ܇࿅σʔλɺͷϓϩοτ͕Ϋϥ ε C2 ʹॴଐ͢Δ܇࿅σʔλͱ͠ɺը૾ 2 ຕͱ܇࿅σʔλશ͘ಉ͡
Ͱ͋Δɻ y(x, w, w0 ) = 0 ͷઢύϥϝʔλ w, w0 ΛมԽͤ͞Δ͜ͱʹΑͬͯɺ D = 2 ͷೖྗۭؒΛॎԣແਚʹҠಈ͢Δɻ ੨ͷϓϩοτ͕ଟ͍ํΛ y(x, w, w0 ) ≥ 0 ͷྖҬͱͯ͠ɺͷϓϩοτ ͕ଟ͍ํΛ y(x, w, w0 ) < 0 ͷྖҬͱ͢Δɻ ྆ํͷΫϥϑͰڭࢣσʔλ͘͠ɺೖྗۭؒͰΑ͘͞Ε͍ͯΔ ͕ɺࠨଆͷࣹӨͦͷΛҰ෦ফͯ͠͠·͍ͬͯΔ͜ͱ͕Θ͔Δɻ 11 / 47
2. ϑΟογϟʔͷઢܗผ ϑΟογϟʔͷઢܗผͰɺ܇࿅σʔλΛ࠷͢ΔΑ͏ͳࣹӨɺ ͭ·Γύϥϝʔλ w, w0 ΛٻΊΔ͜ͱΛߟ͑Δɻ ͦ͜Ͱ·ͣɺ܇࿅σʔλͷೖྗ {x1 ,
x2 , · · · , xN } ͷΫϥεผͷฏۉϕ ΫτϧΛ m1 , m2 ͱ͠ɺ m1 = 1 N1 ∑ n∈C1 xn , m2 = 1 N2 ∑ n∈C2 xn (2.2) ͱͳΔɻ ͜͜ͰɺN1 ͱ N2 ͦΕͧΕɺΫϥε C1 ·ͨ C2 ʹଐ͍ͯ͠Δ܇࿅ σʔλͷͰ͋Δɻ(ͪΖΜɺN1 + N2 = N Λຬͨ͢ɻ) 12 / 47
2. ϑΟογϟʔͷઢܗผ ҰํɺࣹӨޙͷΫϥεผͷฏۉ y(x, w, w0 )(2.1) ͷઢܗੑΑΓɺҎԼ ͷΑ͏ʹͳΔɻ m1
= 1 N1 ∑ n∈C1 y(xn , w, w0 ) = wTm1 + w0 m2 = 1 N2 ∑ n∈C2 y(xn , w, w0 ) = wTm2 + w0 (2.3) Α͘͞ΕࣹͨӨͰɺ͜ΕΒͷࣹӨޙͷΫϥεผͷฏۉେ͖͘ҟ ͳ͍ͬͯΔͱߟ͑ΒΕΔɻ ͭ·Γɺ m2 − m1 = wT(m2 − m1 ) (2.4) ͷ͕େ͖͍΄ͲɺࣹӨޙྑ͘͞Ε͍ͯΔͩΖ͏ɻ 13 / 47
2. ϑΟογϟʔͷઢܗผ ͨͩ͠ɺࣹӨޙͷΫϥεؒͷฏۉͷ͕ࠩେ͖ͯ͘ɺࣹӨޙͷΫϥε ผͷࢄ͕େ͖͔ͬͨΒɺ̍࣍ݩʹࣹӨ͞ΕͨΫϥεͷྖҬ͕ॏͳΓ ߹ͬͯ͠·͏ɻ ͦ͜ͰɺҎԼͷΫϥεผͷࢄͷখ͘͞ͳ͍ͬͯͯ΄͍͠ɻ s2 1 = ∑
n∈C1 {y(xn , w, w0 ) − m1 }2 s2 2 = ∑ n∈C2 {y(xn , w, w0 ) − m2 }2 (2.5) ΑͬͯɺҎԼͷϑΟογϟʔͷผج४Λ࠷େʹ͢ΔΑ͏ͳύϥϝʔλ w, w0 ͕ࣹӨޙΛ࠷େʹอͭΑ͏ͳύϥϝʔλͰ͋Δ͜ͱ͕Θ ͔Δɻ J(w, w0 ) = (m2 − m1 )2 s2 1 + s2 2 (2.6) 14 / 47
2. ϑΟογϟʔͷઢܗผ ͦΕͰɺ(2.6) ͷӈลΛ w, w0 Ͱॻ͖Լͦ͏ɻ ·ͣࢠ (2.4) ΑΓɺ
(m2 − m1 )2 = { wT(m2 − m1 ) }2 = wT(m2 − m1 )(m2 − m1 )Tw = wTSB w (2.7) ͱͳΔɻ ͜͜ͰɺSB Ϋϥεؒڞࢄߦྻͱ͍͍ɺ SB = (m2 − m1 )(m2 − m1 )T (2.8) Ͱఆٛ͞ΕΔɻ 15 / 47
2. ϑΟογϟʔͷઢܗผ ࣍ʹɺ(2.6) ͷ s2 1 + s2 2 Ͱ͋Δ͕ɺͱΓ͋͑ͣ
s2 1 ͚ͩ w, w0 Ͱॻ ͍ͯΈΔͱɺ(2.5) ΑΓɺ s2 1 = ∑ n∈C1 {wTxn + w0 − m1 }2 = ∑ n∈C1 {wTxn − wTm1 }2 = ∑ n∈C1 {wT(xn − m1 )}2 = ∑ n∈C1 wT(xn − m1 )(xn − m1 )Tw =wT [ ∑ n∈C1 (xn − m1 )(xn − m1 )T ] w (2.9) ͱͳΔɻ 16 / 47
2. ϑΟογϟʔͷઢܗผ ಉ༷ͷมܗΛ s2 2 ʹ͍ͭͯߦ͏ͱɺ(2.6) ͷ s2 1 +
s2 2 ҎԼͷΑ͏ ʹͳΔɻ s2 1 + s2 2 = wTSW w (2.10) ͜͜ͰɺSW ૯Ϋϥεڞࢄߦྻͱ͍͍ɺҎԼͰఆٛ͞ΕΔɻ SW = ∑ n∈C1 (xn − m1 )(xn − m1 )T + ∑ n∈C2 (xn − m2 )(xn − m2 )T (2.11) ͜ΕΑΓɺϑΟογϟʔͷผج४ (2.6) w Λ༻͍ͯҎԼͷΑ͏ʹॻ ͚Δɻ J(w, w0 ) = J(w) = wTSB w wTSW w (2.12) ͜͜Ͱɺ݁ՌతʹϑΟογϟʔͷผج४ w ʹͷΈґଘ͠ɺw0 ʹ ґଘ͠ͳ͍͜ͱ͕Θ͔Δɻ 17 / 47
2. ϑΟογϟʔͷઢܗผ ͜Ε͔ΒϑΟογϟʔͷผج४ J(w) Λ࠷େʹ͢Δ w ΛٻΊ͍ͯ͘ ͜ͱʹͳΔ͕ɺ͜͜Ͱ 1 ͭ
J(w) ʹॏཁͳੑ࣭͕͋Δɻ θϩͰͳ͍ఆ α Λ༻ҙ͠ɺJ(αw) Λܭࢉ͢Δͱɺ J(αw) = (αw)TSB (αw) (αw)TSW (αw) = wTSB w wTSW w = J(w) (2.13) ͱͳΓɺϑΟογϟʔͷผج४ J(w) w ͷఆഒʹରͯ͠ෆมͰ ͋Δɻ(εέʔϧෆมੑ) ͜ͷੑ࣭ΑΓɺJ(w) Λ࠷େʹ͢Δ w(= w⋆) ͕ݟ͔ͭΔͱɺͦͷఆ ഒͷ αw⋆ J(w) Λ࠷େʹ͢Δͱ͍͏͜ͱ͕Θ͔Δɻ ͜ΕΑΓɺզʑ J(w) Λ࠷େʹ͢Δ w ΛٻΊΔࡍɺ࠷େʹ͢Δ w ͲͷํΛ͍͍ͯΔ͔͚ͩΛΕྑ͍ࣄʹͳΔɻ 18 / 47
2. ϑΟογϟʔͷઢܗผ ͦΕͰ࣮ࡍʹ J(w) Λ࠷େʹ͢Δ w ΛٻΊΔҝʹɺJ(w) Λ w Ͱඍ
͢Δͱɺ ∂J(w) ∂w = 2 (wTSW w)2 { SB w(wTSW w) − SW w(wTSB w) } (2.14) ͱͳΓɺ͜ΕΛθϩʹ͢Δ w ҎԼͷࣜΛຬͨ͢ɻ SB w(wTSW w) − SW w(wTSB w) = 0 (2.15) ྆ลɺࠨ͔Β S−1 W Λ͔͚ͯɺগࣜ͠มܗΛ͢Δͱɺ S−1 W SB w(wTSW w) − w(wTSB w) = 0 →w = (wTSW w) (wTSB w) S−1 W SB w w ∝ S−1 W SB w (2.16) ͱͳΔ͜ͱ͕Θ͔Δɻ 19 / 47
2. ϑΟογϟʔͷઢܗผ ͜͜ͰɺSB w SB w = (m2 −
m1 )(m2 − m1 )Tw ∝ m2 − m1 (2.17) ͱͳΔ͜ͱΛ༻͍ΔͱɺϑΟογϟʔͷผج४ J(w) Λ࠷େʹ͢Δϕ Ϋτϧ w w ∝ S−1 W (m2 − m1 ) (2.18) ͱͳΓɺS−1 W (m2 − m1 ) ͱಉ͡ํΛ͍͍ͯΔϕΫτϧͰ͋Δ͜ͱ͕ Θ͔Δɻ Αͬͯɺߴ࣍ݩͷೖྗσʔλΛ (2.1) Λͬͯ̍࣍ݩʹࣹӨ͠ɺΫϥε Λஅ͢ΔࡍɺϑΟογϟʔͷઢܗผΛ༻͍ΔͱɺS−1 W (m2 − m1 ) ํ ͷύϥϝʔλ w Λ༻͍ࣹͯӨΛߦ͏ͱɺೖྗσʔλͷΛ࠷େݶ ʹอͪͳ͕ΒࣹӨ͢Δ͜ͱ͕Ͱ͖Δ͜ͱ͕Θ͔ͬͨɻ 20 / 47
3. ϩδεςΟοΫճؼ ͜Ε·Ͱࣝผؔ y(x) Λڭࢣσʔλ͔Βܾఆ͢Δํ๏ΛऔΓѻͬͨ ͕ɺ͔͜͜Β͖݅֬ p(Ck |x) Λܾఆ͢Δํ๏ΛऔΓѻ͏ɻ ࣝผؔͷ࣌ͱಉ͡Α͏ʹɺ܇࿅σʔλͱͯ͠ೖྗσʔλͷू߹
{x1 , x2 , · · · , xN } ͱͦΕͧΕʹରԠ͢Δඪมͷू߹ {t1 , t2 , · · · , tN } Λ༻ҙ͢Δɻ ࠓճΫϥε K ͕ 2 ͷ࣌ (ೋྨ) Λѻ͏ͨΊɺతม tn 0 ͔ 1 ͷࢄతͳΛͱΔɻ ࠓճྨͷϞσϧͱͯ͠ɺϩδεςΟοΫճؼϞσϧΛհ͢Δɻ (ʮճؼʯͱ͍͍ͯΔ͕ɺྨͷϞσϧͰ͋Δɻ) 21 / 47
3-1. ϩδεςΟοΫճؼ ࣝผؔͷ࣌ͱಉ༷ʹɺ܇࿅σʔλΛ༻͍͖ͯ݅֬ p(Ck |x) ΛҰ ͔Β࡞Δ͜ͱͤͣɺ͖݅֬ p(Ck |x) ʹύϥϝʔλ
w Λಋೖ͠ ͯɺp(Ck |x, w) Λߟ͑Δɻ ࠓճͷϩδεςΟοΫճؼͰɺΫϥε 1 ʹରͯ͠ɺҎԼͷΑ͏ͳؔ p(C1 |x, w) Λߟ͑Δɻ p(C1 |x, w) = σ(wTϕ(x)) (3.1) 22 / 47
3-1. ϩδεςΟοΫճؼ ·ͣɺ(3.1) ͷӈลͷҾͷதʹ͋ΔϕΫτϧؔ ϕ(·) ඇઢܗͳؔ ϕj (x) (j =
0, · · · , M − 1) ΛॎʹฒͨϕΫτϧؔ ϕ(x) = (ϕ0 (x), ϕ1 (x), · · · , ϕM−1 (x))T Ͱ͋Δɻ ྫ͑ɺجఈؔ ϕj (x) ͱͯ͠ҎԼͷΨεجఈ͕ؔ͋Δɻ ϕj (x) = exp { − (x − µj )2 2s2 } (3.2) ͜ͷجఈؔ x = µj Λத৺ʹͯ͠ɺࢄ s2 ʹΑͬͯࢧ͞ΕΔ͕ ΓΛ࣋ͭΨεجఈؔͰ͋Δɻ Ҏ߱Ұൠͷجఈؔ ϕj (x) Λ༻͍ͯٞ͢Δɻ 23 / 47
3-1. ϩδεςΟοΫճؼ ·ͣɺؔ σ(·) ϩδεςΟοΫγάϞΠυؔͱݺΕɺҎԼͰఆ ٛ͞ΕΔɻ σ(x) = 1 1
+ e−x (3.3) ਤͰॻ͘ͱҎԼͷΑ͏ʹͳΔɻ ϩδεςΟοΫγάϞΠυؔఆٛʹΑΓɺ0 ͔Β 1 ·ͰͷΛͱΔ ͷͰɺ(3.1) ΑΓ p(C1 |x, w) ҎԼͷΑ͏ʹ֬ͷຬ͖ͨ͢ͷൣғ ʹΛͱΔɻ 0 < p(C1 |x, w) < 1 (3.4) 24 / 47
3-1. ϩδεςΟοΫճؼ ҰํɺΫϥε 2 ʹରͯ͠ɺp(C2 |x, w) ҎԼͷΑ͏ʹԾఆ͢Δɻ p(C2 |x,
w) = 1 − p(C1 |x, w) = 1 − σ(wTϕ(x)) (3.5) (3.4) ΑΓɺp(C2 |x, w) ҎԼͷΑ͏ʹ֬ͷຬ͖ͨ͢ͷൣғʹ ΛͱΔɻ 0 < p(C2 |x, w) < 1 (3.6) ͜ΕΒͷఆٛʹΑΓɺԾఆͨؔ͠ p(Ck |x, w) ن֨Խ݅ (1.2) Λຬ ͨ͢ɻ ∑ k p(Ck |x, w) = p(C1 |x, w) + 1 − p(C1 |x, w) = 1 (3.7) t = 1 ͷΫϥεΛ C1 ͱ͠ɺt = 0 ͷΫϥεΛ C2 ͱ͍ͯ͠ΔͷͰɺؔ p(t|x, w) p(t|x, w) = σ(wTϕ(x))t(1 − σ(y(wTϕ(x))))1−t (3.8) ͱͳΔɻ (͜ͷΑ͏ͳΛϕϧψʔΠͱ͍͏) 25 / 47
3-2. ϩδεςΟοΫճؼͷ࠷ਪఆ ࣍ʹ࠷ਪఆΛߦ͏͜ͱΛߟ͑Δɻ ͍ͭͷΑ͏ʹɺ܇࿅σʔλͱͯ͠ೖྗσʔλͷू߹ X = {x1 , x2 ,
· · · , xN } ͱͦΕͧΕʹରԠ͢Δඪมͷू߹ {t1 , t2 , · · · , tN } Λ༻ҙ͢Δɻ ڭࢣσʔλҰͭҰ͕ͭ (3.8) ͔Βಠཱʹੜ͞Ε͍ͯΔͱ͢Δͱɺ ؔ p(t|X, w) ҎԼͷΑ͏ʹͳΔɻ p(t|X, w) = N ∏ n=1 ytn n (1 − yn )1−tn (3.9) ͱͳΔɻ ͜͜Ͱ yn ҎԼͰఆٛ͞ΕΔɻ yn = σ(wTϕ(xn )) (3.10) 26 / 47
3-2. ϩδεςΟοΫճؼͷ࠷ਪఆ ؔ (3.9) Λ࠷େʹ͢Δ w ΛٻΊΔ͜ͱҎԼͷෛͷରΛ ࠷খʹ͢Δ w ΛٻΊΔ͜ͱͱՁͰ͋Δɻ
E(w) = − ln p(t|X, w) = − N ∑ n=1 ln { ytn n (1 − yn )1−tn } = − N ∑ n=1 { tn ln yn + (1 − tn ) ln (1 − yn ) } (3.11) ͜ΕަࠩΤϯτϩϐʔޡࠩͱݺΕΔޡࠩؔͰɺྨͰΑ͘ ΘΕΔޡࠩؔͰ͋Δɻ ྨʹ͓͚ΔަࠩΤϯτϩϐʔޡࠩͷ࠷খԽɺ֬Λ༻͍Δͱ ؔΛϕϧψʔΠ (3.8) ͱԾఆͨ͠ͱ͖ͷ࠷ਪఆͷ݁ՌͰ͋ Δࣄ͕Θ͔Δɻ 27 / 47
3-2. ϩδεςΟοΫճؼͷ࠷ਪఆ ࣍ʹෛͷର (3.11) Λ࠷খʹ͢Δ w ΛٻΊΔͨΊʹ (3.11) ͷ w
ʹର͢ΔޯΛٻΊΔͱҎԼͷΑ͏ʹͳΔɻ(PRML ͷԋश 4.13 ࢀর) ∇E(w) = N ∑ n=1 (yn − tn )ϕ(xn ) (3.12) ͜ͷޯͷܗਖ਼ղϥϕϧ tn ͱ༧ଌ yn ͷࠩ (ͭ·Γޡࠩ) ͱجఈؔ ϕΫτϧ ϕ(xn ) ͷͷܗΛ͍ͯ͠Δɻ 28 / 47
3-2. ϩδεςΟοΫճؼͷ࠷ਪఆ ͜ͷޯ ∇E(w) Λθϩʹ͢Δ w ΛղੳతʹٻΊΔ͜ͱͰ͖ͳ͍ɻ ͦͷཧ༝༧ଌ y =
σ(wTϕ(x)) ͕ϩδεςΟοΫؔΛ׆ੑԽؔ ʹ͔࣋ͭΒͰ͋Δɻ ͜ͷΑ͏ʹޯ ∇E(w) Λθϩʹ͢Δ w ΛղੳతʹٻΊΔ͜ͱ͕Ͱ͖ ͳ͍࣌ޯ߱Լ๏Λ༻͍Δ͜ͱ͕͋Δɻ(χϡʔϥϧωοτͰ͜ͷ ํ๏͕Α͘༻͍ΒΕΔɻ) ޯ߱Լ๏Ͱɺ·ͣϥϯμϜʹܾΊͨύϥϝʔλͷॳظΛ w(0) ͱ ͠ɺޡࠩؔͷޯΛ༻͍ͯύϥϝʔλΛҎԼͷΑ͏ʹߋ৽͢Δɻ w(1) = w(0) − η∇E(w(0)) (3.13) ͜͜Ͱ η > 0 ֶशύϥϝʔλͱݺͿɻ ͜ΕΛ܁Γฦ͢͜ͱͰύϥϝʔλ͕ޯ ∇E(w) ͕খ͘͞ͳΔํʹߋ ৽͞ΕɺE(w) Λ࠷খʹ͢Δύϥϝʔλʹऩଋ͢Δɻ 29 / 47
ϕΠζਪఆʹ͍ͭͯ ͜Ε·Ͱ (࠷ਪఆ) ͰɺؔΛ࠷େʹ͢ΔΑ͏ͳύϥϝʔλ w Λਪఆ͖ͯͨ͠ɻ ϕΠζਪఆͰɺڭࢣσʔλΛ༻͍ͯύϥϝʔλ w ͷ֬ (Ͱͳ
͘෯ΛͭɺࣄޙͱݺΕΔ) ΛٻΊΔɻ ͦͷࣄޙΛ༻͍ͯɺະͷσʔλͷೖྗ x ͕༩͑ΒΕͨ࣌ͷग़ྗ t ͷ༧ଌ p(t|x, t, X) ΛٻΊΔɻ ͜ͷ༧ଌ p(t|x, t, X) ɺະͷσʔλʹର͢Δ͖݅֬ p(t|x, w) ͱࣄޙ p(w|t, X) Λ༻͍ͯ p(t|x, t, X) = ∫ p(t|x, w)p(w|t, X) dw (3.14) ͱॻ͚Δɻ(PRML 1.68 ࣜࢀর) ϕΠζਪఆʹ͍ͭͯɺPRML 1.2.3 ࢀরɻ 30 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ࣍ϩδεςΟοΫճؼΛϕΠζతʹѻ͏͜ͱΛߟ͑Δɻ ϕΠζਪఆͰະͷೖྗ x ʹର͢Δग़ྗ t ͷ༧ଌ p(t|x, t,
X) Λ ٻΊΔ͜ͱ͕తͱͳΔɻ (3.14) ΑΓɺͦͷ༧ଌ p(t|x, t, X) ॏΈ w ͷੵͰҎԼͷΑ͏ʹ ͔͚Δɻ p(t|x, t, X) = ∫ p(t|x, w)p(w|t, X) dw (3.15) ͜͜Ͱɺp(t|x, w) ؔͰ͋Γɺp(w|t, X) ύϥϝʔλͷࣄޙ Ͱ͋Δɻ ಛʹࠓճೋྨΛߟ͍͑ͯΔͷͰɺ֬ p(C1 |x, t, X) = ∫ p(C1 |x, w)p(w|t, X) dw (3.16) ͚ͩΛੵͯ͠ٻΊͯɺp(C2 |x, t, X) p(C2 |x, t, X) = 1 − p(C1 |x, t, X) (3.17) ͷΑ͏ʹن֨Խ͔݅ΒٻΊΔ͜ͱΛߟ͑Δɻ 31 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ͨͩ͠ɺੵ (3.16) Λղੳతʹղ͘ͷෆՄೳͰ͋Δɻ(͜Εϩδε ςΟοΫγάϞΠυؔͷӨڹͰ͋Δ) ͕ͨͬͯ͠ɺੵΛۙࣅతʹٻΊΔ͜ͱΛߟ͑Δɻ ࠓճ (PRML 4
ষͰ) ϥϓϥεۙࣅΛ༻͍ͯੵΛۙࣅతʹٻΊͯ ͍Δɻ ۩ମతʹ (3.16) ͷύϥϝʔλͷࣄޙ p(w|t, X) ʹϥϓϥεۙࣅ Λద༻ͯ͠ɺΨεʹۙࣅ͢Δɻ ͜͜Ͱϥϓϥεۙࣅͷઆ໌Λগ͠ߦ͏ɻ 32 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ·ͣ֬ม͕Ұ࣍ݩͷม z ͷ߹Λߟ͑ɺҎԼͷΑ͏ͳ֬ p(z) Λߟ͑Δɻ p(z) = 1
Z f(z) (3.18) ͜͜ͰɺZ ҎԼͰఆٛ͞ΕΔن֨ԽఆͰ͋Δɻ Z = ∫ f(z) dz (3.19) ϥϓϥεۙࣅͷత p(z) ΛϞʔυ (dp(z)/dz = 0 ͱͳΔ z) Λத ৺ͱ͢ΔΨεʹۙࣅ͢Δ͜ͱͰ͋Δɻ ·ͣϞʔυ z = z0 Λݟ͚ͭΔɻϞʔυ (3.18) ΑΓ df(z) dz z=z0 = 0 (3.20) ͳΔ z0 Ͱ͋Δɻ 33 / 47
3-3. ϕΠζϩδεςΟοΫճؼ Ϟʔυ͕ٻ·ͬͨΒɺؔ ln f(z) Λ z = z0 पΓͰҎԼͷΑ͏ʹςΠ
ϥʔల։ͷ 2 ࣍·ͰͰۙࣅ͢Δɻ ln f(z) ∼ ln f(z0 ) − 1 2 A(z − z0 )2 (3.21) ͜͜Ͱɺ A = − d2 dz2 ln f(z) z=z0 (3.22) Ͱ͋Δɻ ͜͜Ͱɺ(3.20) ʹΑΓ (3.21) ͷӈลͰҰ࣍ͷ߲͕ଘࡏ͠ͳ͍ɻ (3.21) ͷ྆ลͷࢦΛͱΔͱ f(z) ∼ f(z0 ) exp { − 1 2 A(z − z0 )2 } (3.23) ͱͳΔɻ 34 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ن֨ԽΛ͢Δͱɺ p(z) p(z) ∼ ( A 2π
)1/2 exp { − 1 2 A(z − z0 )2 } (3.24) ͱۙࣅͰ͖Δɻ͜Ε͕ϥϓϥεۙࣅͰ͋Δɻ ͨͩ͠ҙͱͯ͠ɺA > 0 Ͱͳ͍ͱΨε͕ఆٛͰ͖ͳ͍ɻ 35 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ࣍Ұ࣍ݩͷ֬ม͔ΒɺϕΫτϧʹ֦ு͠Α͏ɻ ͭ·ΓɺҎԼͷ֬ p(z) Λఆٛ͢Δɻ p(z) = 1 Z
f(z) (3.25) ͜͜Ͱɺ Z = ∫ f(z) dz (3.26) Ͱ͋Δɻ Ұ࣍ݩͷ֬มͱಉ͡Α͏ʹޯ ∇f(z) ͕θϩʹͳΔ z0 Λٻ ΊΔɻ 36 / 47
3-3. ϕΠζϩδεςΟοΫճؼ Ϟʔυ͕ٻ·ͬͨΒɺln f(z) Λ z0 पΓͰςΠϥʔల։Ͱۙࣅ͢Δɻ ln f(z) ∼
ln f(z0 ) − 1 2 (z − z0 )TA(z − z0 ) (3.27) ͜͜ͰɺA ҎԼͰఆٛ͞ΕΔ M × M ͷϔοηߦྻͰ͋Δɻ A = −∇∇ ln f(z) z=z0 (3.28) ࣍ʹ (3.27) ͷ྆ลͷࢦΛͱΔͱҎԼͷΑ͏ʹͳΔɻ f(z) ∼ f(z0 ) exp { − 1 2 (z − z0 )TA(z − z0 ) } (3.29) ͜ΕΑΓن֨ԽΛ͢Δͱɺ p(z) p(z) ∼ |A|1/2 (2π)M/2 exp { − 1 2 (z − z0 )TA(z − z0 ) } = N(z|z0 , A−1) (3.30) ͱΨεʹۙࣅͰ͖Δɻ 37 / 47
3-3. ϕΠζϩδεςΟοΫճؼ Ҏ্Ͱઆ໌ͨ͠ϥϓϥεۙࣅΛ༻͍ͯҎԼͷੵ (3.16) Λۙࣅ͍ͨ͠ɻ p(C1 |x, t, X) =
∫ p(C1 |x, w)p(w|t, X) dw (3.31) ·ͣɺࣄޙ p(w|t, X) ΛٻΊΔͨΊʹࣄલΛಋೖ͢Δɻ p(w) = N(w|m0 , S0 ) (3.32) (3.18) ΑΓɺؔ p(t|X, w) p(t|X, w) = N ∏ n=1 ytn n (1 − yn )1−tn (3.33) Ͱ͋ͬͨɻ 38 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ͜ΕΑΓɺࣄޙ p(w|t, X) ϕΠζͷఆཧΑΓɺҎԼͰ͋Δɻ p(w|t, X) ∝ p(w)p(t|X,
w) (3.34) ͱͳΔͷͰɺln p(w|t, X) ҎԼͱͳΔɻ ln p(w|t, X) = − 1 2 (w − m0 )TS−1 0 (w − m0 ) + N ∑ n=1 { tn ln yn + (1 − tn ) ln (1 − yn ) } + const. (3.35) ͜ͷࣄޙͷର ln p(w|t, X) Λ࠷େʹ͢Δύϥϝʔλ wMAP Λ (ͨ ͱ͑ޯ߱Լ๏ͳͲͰ) ٻΊͯɺͦͷ wMAP ͰͷϔοηߦྻΛٻΊ ΔͱɺҎԼͷΑ͏ʹͳΔɻ S−1 N = − ∇∇ ln p(w|t, X) w=wMAP =S−1 0 + N ∑ n=1 yn (1 − yn )ϕn ϕT n w=wMAP (3.36) 39 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ΑͬͯɺϥϓϥεۙࣅΛ༻͍Δͱࣄޙ p(w|t, X) ҎԼͷΑ͏ʹۙ ࣅͰ͖Δɻ p(w|t, X) ∼
N(w|wMAP , SN ) (3.37) ͜ΕΑΓɺ(3.31) ͷੵҎԼͷΑ͏ʹۙࣅͰ͖Δɻ p(C1 |x, t, X) ∼ ∫ σ(wTϕ) N(w|wMAP , SN ) dw (3.38) ͜͜Ͱɺp(C1 |x, w) = σ(wTϕ) Λར༻ͨ͠ɻ ࣍ʹɺϩδεςΟοΫγάϞΠυؔΛҎԼͷΑ͏ʹॻ͖͢ɻ σ(wTϕ) = ∫ δ(a − wTϕ)σ(a) da (3.39) ͜͜Ͱɺδ(·) σϡϥοΫͷσϧλؔͰ͋Δɻ 40 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ͜ΕΑΓɺ(3.38) ҎԼͷΑ͏ʹॻ͖ͤΔɻ ∫ σ(wTϕ) N(w|wMAP , SN )
dw = ∫ ∫ δ(a − wTϕ)σ(a) N(w|wMAP , SN ) da dw = ∫ ∫ δ(a − wTϕ) N(w|wMAP , SN ) dw σ(a) da = ∫ p(a) σ(a) da (3.40) ͜͜Ͱɺ p(a) = ∫ δ(a − wTϕ) N(w|wMAP , SN ) dw (3.41) Ͱ͋Δɻ 41 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ੵ (3.41) ʹ͓͍ͯɺϕ ʹฏߦͳͯ͢ͷํͷ w ੵͦΕΒͷύ ϥϝʔλʹઢܗ੍Λ༩͑ɺ·ͨ ϕ
ʹߦ͢Δͯ͢ͷํͷ w ੵ Ψε N(w|wMAP , SN ) ͷपลԽΛ༩͑Δɻ ͨͱ͑ɺw = (w1 , w2 )T ͱ͠ɺϕ = (ϕ, 0)T Ͱ͋Δͱ͖Λߟ͑Δͱɺ ੵ (3.41) ҎԼͷΑ͏ʹ͔͚Δɻ p(a) = ∫ ∫ δ(a − w1 ϕ) N(w|wMAP , SN ) dw1 dw2 = ∫ δ(a − w1 ϕ) [ ∫ N(w|wMAP , SN ) dw2 ] dw1 (3.42) (??) ΑΓɺΨεΛपลԽͨ͠पล࠶ͼΨεͰ͋Δ ͜ͱ͕Θ͔͍ͬͯΔͷͰɺϕ ʹߦ͢Δ w2 ํͷੵΨεͷ पลԽΛ༩͑ɺͦͷपลԽ͞ΕͨΨε N(w1 |(wMAP )1 , (SN )11 ) ͱͳΔɻ 42 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ·ͨɺw1 ͷੵΛ͢Δͱɺੵ (3.41) ҎԼͷΑ͏ʹͳΔɻ p(a) = ∫ δ(a
− w1 ϕ) N(w1 |(wMAP )1 , (SN )11 ) dw1 = 1 |ϕ| N(a/ϕ|(wMAP )1 , (SN )11 ) =N(a|(ϕwMAP )1 , (ϕ2SN )11 ) (3.43) ͭ·Γɺϕ ʹฏߦͳ w1 ͷํͷੵ w1 ʹ w1 = a/ϕ ͳΔઢܗ੍ Λ༩͑Δ͜ͱ͕Θ͔Δɻ ͜ΕΑΓɺp(a) ֬ม͕ a ͷΨεʹͳΔ͜ͱ͕Θ͔Δɻ 43 / 47
3-3. ϕΠζϩδεςΟοΫճؼ Ψεฏۉͱࢄ͕ܾ·Εɺܗ͕Ұҙʹఆ·Γɺฏۉ µa ͱ ࢄ σ2 a ҎԼͷΑ͏ʹͳΔɻ µa
= ∫ p(a)a da = ∫ ∫ aδ(a − wTϕ) N(w|wMAP , SN ) dwda = ∫ wTϕ N(w|wMAP , SN ) dw = wT MAP ϕ (3.44) σ2 a = ∫ p(a)(a2 − µ2 a ) da = ∫ ∫ (a2 − µ2 a )δ(a − wTϕ) N(w|wMAP , SN ) dwda = ∫ ((wTϕ)2 − (wT MAP ϕ)2) N(w|wMAP , SN ) dw =ϕT [ ∫ (wwT − wMAP wT MAP ) N(w|wMAP , SN ) dw ] ϕ =ϕTSN ϕ (3.45) 44 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ͢Δͱɺ༧ଌ p(C1 |x, t, X) (3.40) ΑΓɺҎԼͷΑ͏ʹͳΔ͜ͱ
͕Θ͔Δɻ p(C1 |x, t, X) ∼ ∫ σ(a)N(a|µa , σ2 a ) da (3.46) ͜͜Ͱɺµa ͱ σ2 a (3.44) ͱ (3.45) Ͱܭࢉͨ͠ฏۉͱࢄͷύϥϝʔ λͰ͋Δɻ ͜ͷੵ (3.46) ·ͨղੳతʹੵͰ͖ͳ͍ɻ ͦ͜ͰҎԼͷϓϩϏοτؔͷٯؔ Φ(a) Λಋೖ͢Δɻ Φ(a) = 1 2 { 1 + erf ( a √ 2 )} (3.47) ͜͜Ͱɺޡࠩؔ erf(a) ҎԼͰఆٛ͞ΕΔɻ erf(a) = 2 √ π ∫ a 0 exp (−θ2) dθ (3.48) 45 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ϓϩϏοτؔͷٯؔ Φ (√ π 8 a ) ʹΑͬͯϩδεςΟοΫγάϞΠ
υؔ σ(a) Λۙࣅ͢Δ͜ͱ͕Ͱ͖Δɻ ҎԼϩδεςΟοΫγάϞΠυؔ σ(a)(ͷ࣮ઢ) ͱϓϩϏοτؔ ͷٯؔ Φ (√ π 8 a ) (੨ͷઢ) Λൺֱͨ͠ਤͰ͋Δɻ 46 / 47
3-3. ϕΠζϩδεςΟοΫճؼ ͞ΒʹϓϩϏοτؔͷٯؔʹҎԼͷੑ࣭͕͋Δɻ(PRML ͷԋश 4.26 ࢀর) ∫ Φ(λa)N(a|µ, σ2) da
= Φ ( µ (λ−2 + σ2)1/2 ) (3.49) ͜ΕΒͷੑ࣭Λ༻͍ͯɺੵ (3.46) ΛҎԼͷΑ͏ʹۙࣅͯ͠ٻΊΔɻ p(C1 |x, t, X) ∼ ∫ σ(a)N(a|µa , σ2 a ) da ∼ ∫ Φ (√ π 8 a ) N(a|µa , σ2 a ) da =Φ ( µa (8/π + σ2 a )1/2 ) ∼ σ (√ 8 π µa (8/π + σ2 a )1/2 ) =σ ( µa (1 + πσ2 a /8)1/2 ) (3.50) ͜͜Ͱɺµa ͱ σ2 a (3.44) ͱ (3.45) Ͱ͋Δɻ 47 / 47