: (GLMM) (pseudo replication) ( ) ( ) & Markov Chain Monte Carlo (MCMC)? /30

Size: px
Start display at page:

Download ": (GLMM) (pseudo replication) ( ) ( ) & Markov Chain Monte Carlo (MCMC)? /30"

Transcription

1 PlotNet 6 ( ) TOEF( ), AM, growth6 DBH growth (mm) DBH (cm) : kubo@ees.hokudai.ac.jp kubo/show/2006/plotnet/ /30

2 : (GLMM) (pseudo replication) ( ) ( ) & Markov Chain Monte Carlo (MCMC)? /30

3 ( ) TOEF( ), AM, growth6 DBH growth (mm) DBH (cm) ( ) ( ) /30

4 ( ) ( ) : : (TOEF) : /30

5 : 6 6? year growth6 TOEF( ), AM, growth6 DBH (cm) DBH growth (mm) (5 ) /30

6 ? (1) TOEF( ), AM, growth6 TOEF( ), AP, growth6 DBH growth (mm) DBH growth (mm) DBH (cm) DBH (cm) TOEF( ), OJ, growth6 DBH growth (mm) ( ) : β 1 log(dbh) + β 2 log(dbh) DBH (cm) /30

7 ? (2) year growth year growth year growth /30

8 ( )? /30

9 : Temperature VPD Temperature Precipitation PPFD : 6-9 ( ) VPD PPFD : ( ) ( ) ( ) /30

10 y1 A A "#%$# W y1 & B y2 B W y2 C C!! & #%$(') & : *,+(-. #%/ /30

11 ? (3) DBH growth (mm) TOEF( ), AM, growth DBH (cm) ; ( ) /30

12 : = (fixed effects) (random effects) TOEF( ), AM, growth6 DBH growth (mm) DBH (cm) fixed effects ; Poisson random effects ; 1 Gamma (mixed model) Poission Gamma /30

13 : ( ) L(β j, γ k, s {G i }) = i T r=0 [ R(r s) y Y ] P (G 6,y r, λ i,y ) dr, random effects R(r s) ( 1 s 2 Gamma ) fixed effects P (G 6,y λ i,y ) ( λ i,y Poisson ) P (G 6,y λ i,y ) = λg6,y i,y exp( λ i,y ), G 6,y! λ i,y exp( ) λ i,y = exp( X j β j x i,j,y + X k γ k w i,k,y ), /30

14 : R Random effects s fixed effects Akaike s Information Criteria (AIC) /30

15 : (Acer mono) DBH growth (mm) TOEF( ), AM, growth6 ( ): DBH (cm) temp.ps: best=16.4/width=4.6 ppfd: factor=0.02 vpd: factor=0.05 ( ): temp.mb: center=14.2/slope=1.0 rain: rbest=0.9/time= density posterior AM /30

16 : (Acer amoenum) DBH growth (mm) TOEF( ), AP, growth6 ( ): DBH (cm) temp.ps: best=16.4/width=5.8 ppfd: factor=0.05 vpd: factor=0.05 ( ): temp.mb: center=14.8/slope=2.0 rain: rbest=0.7/time= density posterior AP /30

17 : (Ostrya japonica) DBH growth (mm) TOEF( ), OJ, growth6 ( ): DBH (cm) temp.ps: best=16.0/width=5.8 ppfd: factor=0.04 vpd: factor=0.05 ( ): temp.mb: center=14.2/slope=2.0 rain: rbest=0.7/time= density posterior OJ /30

18 : TOEF( ), AM, growth6 DBH growth (mm) DBH growth (mm) DBH (cm) TOEF( ), AP, growth DBH (cm) ( ) /30

19 - - ( ) /30

20 {open, close} : 22 : 5m : ,,,, CN,, : 1 ( ) , 7, /30

21 :? :????? :? logistic p: ( 1 ) q: ( 1 ) p = exp(β CD + β LD L)/Z, L : 0, 1. q = exp(β CL + β LL L)/Z, Z = 1 + exp(β CD + β LD L) + exp(β CL + β LL L) /30

22 : : 1 ( )!?? /30

23 Nest : - - : β x = β Hyperspecies x (Hyperspecies) (Species) (Individual) + β Species x + β Individual x /30

24 : Markov Chain Monte Carlo (MCMC) Gibbs /30

25 : β CD = β Hyperspecies CD + β Species CD + β Individual CD /30

26 : 0.31 (close), 0.09 (open), /30

27 ( )? p: ( 1 ) A: ( ; ) L: N: ( % ) p = exp( (β C + (exp(β A ) + exp(β L )L)A + β N N)) /30

28 : /30

29 : average life (years) Caj Clj Ms Sb St EjSl Rt Pe Sp Cs Pn Vo Sg Pt Cc Ia Qs Na Pj Ir La nitrogen (%, spc mean) /30

30 : Random effects ( ) plot data? MCMC TOEF( ), AM, growth6 DBH growth (mm) DBH (cm) /30

12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? ( :51 ) 2/ 71

12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? ( :51 ) 2/ 71 2010-12-02 (2010 12 02 10 :51 ) 1/ 71 GCOE 2010-12-02 WinBUGS kubo@ees.hokudai.ac.jp http://goo.gl/bukrb 12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? 2010-12-02 (2010 12

More information

/22 R MCMC R R MCMC? 3. Gibbs sampler : kubo/

/22 R MCMC R R MCMC? 3. Gibbs sampler :   kubo/ 2006-12-09 1/22 R MCMC R 1. 2. R MCMC? 3. Gibbs sampler : kubo@ees.hokudai.ac.jp http://hosho.ees.hokudai.ac.jp/ kubo/ 2006-12-09 2/22 : ( ) : : ( ) : (?) community ( ) 2006-12-09 3/22 :? 1. ( ) 2. ( )

More information

60 (W30)? 1. ( ) 2. ( ) web site URL ( :41 ) 1/ 77

60 (W30)? 1. ( ) 2. ( ) web site URL ( :41 ) 1/ 77 60 (W30)? 1. ( ) kubo@ees.hokudai.ac.jp 2. ( ) web site URL http://goo.gl/e1cja!! 2013 03 07 (2013 03 07 17 :41 ) 1/ 77 ! : :? 2013 03 07 (2013 03 07 17 :41 ) 2/ 77 2013 03 07 (2013 03 07 17 :41 ) 3/ 77!!

More information

kubostat2017j p.2 CSV CSV (!) d2.csv d2.csv,, 286,0,A 85,0,B 378,1,A 148,1,B ( :27 ) 10/ 51 kubostat2017j (http://goo.gl/76c4i

kubostat2017j p.2 CSV CSV (!) d2.csv d2.csv,, 286,0,A 85,0,B 378,1,A 148,1,B ( :27 ) 10/ 51 kubostat2017j (http://goo.gl/76c4i kubostat2017j p.1 2017 (j) Categorical Data Analsis kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 15 : 2017 11 08 17:11 kubostat2017j (http://goo.gl/76c4i) 2017 (j) 2017 11 15 1 / 63 A B C D E F G

More information

講義のーと : データ解析のための統計モデリング. 第3回

講義のーと :  データ解析のための統計モデリング. 第3回 Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20

More information

講義のーと : データ解析のための統計モデリング. 第5回

講義のーと :  データ解析のための統計モデリング. 第5回 Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20

More information

一般化線形 (混合) モデル (2) - ロジスティック回帰と GLMM

一般化線形 (混合) モデル (2) - ロジスティック回帰と GLMM .. ( ) (2) GLMM kubo@ees.hokudai.ac.jp I http://goo.gl/rrhzey 2013 08 27 : 2013 08 27 08:29 kubostat2013ou2 (http://goo.gl/rrhzey) ( ) (2) 2013 08 27 1 / 74 I.1 N k.2 binomial distribution logit link function.3.4!

More information

O1-1 O1-2 O1-3 O1-4 O1-5 O1-6

O1-1 O1-2 O1-3 O1-4 O1-5 O1-6 O1-1 O1-2 O1-3 O1-4 O1-5 O1-6 O1-7 O1-8 O1-9 O1-10 O1-11 O1-12 O1-13 O1-14 O1-15 O1-16 O1-17 O1-18 O1-19 O1-20 O1-21 O1-22 O1-23 O1-24 O1-25 O1-26 O1-27 O1-28 O1-29 O1-30 O1-31 O1-32 O1-33 O1-34 O1-35

More information

kubostat2015e p.2 how to specify Poisson regression model, a GLM GLM how to specify model, a GLM GLM logistic probability distribution Poisson distrib

kubostat2015e p.2 how to specify Poisson regression model, a GLM GLM how to specify model, a GLM GLM logistic probability distribution Poisson distrib kubostat2015e p.1 I 2015 (e) GLM kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2015 07 22 2015 07 21 16:26 kubostat2015e (http://goo.gl/76c4i) 2015 (e) 2015 07 22 1 / 42 1 N k 2 binomial distribution logit

More information

LCR e ix LC AM m k x m x x > 0 x < 0 F x > 0 x < 0 F = k x (k > 0) k x = x(t)

LCR e ix LC AM m k x m x x > 0 x < 0 F x > 0 x < 0 F = k x (k > 0) k x = x(t) 338 7 7.3 LCR 2.4.3 e ix LC AM 7.3.1 7.3.1.1 m k x m x x > 0 x < 0 F x > 0 x < 0 F = k x k > 0 k 5.3.1.1 x = xt 7.3 339 m 2 x t 2 = k x 2 x t 2 = ω 2 0 x ω0 = k m ω 0 1.4.4.3 2 +α 14.9.3.1 5.3.2.1 2 x

More information

0.45m1.00m 1.00m 1.00m 0.33m 0.33m 0.33m 0.45m 1.00m 2

0.45m1.00m 1.00m 1.00m 0.33m 0.33m 0.33m 0.45m 1.00m 2 24 11 10 24 12 10 30 1 0.45m1.00m 1.00m 1.00m 0.33m 0.33m 0.33m 0.45m 1.00m 2 23% 29% 71% 67% 6% 4% n=1525 n=1137 6% +6% -4% -2% 21% 30% 5% 35% 6% 6% 11% 40% 37% 36 172 166 371 213 226 177 54 382 704 216

More information

10 117 5 1 121841 4 15 12 7 27 12 6 31856 8 21 1983-2 - 321899 12 21656 2 45 9 2 131816 4 91812 11 20 1887 461971 11 3 2 161703 11 13 98 3 16201700-3 - 2 35 6 7 8 9 12 13 12 481973 12 2 571982 161703 11

More information

kubostat7f p GLM! logistic regression as usual? N? GLM GLM doesn t work! GLM!! probabilit distribution binomial distribution : : β + β x i link functi

kubostat7f p GLM! logistic regression as usual? N? GLM GLM doesn t work! GLM!! probabilit distribution binomial distribution : : β + β x i link functi kubostat7f p statistaical models appeared in the class 7 (f) kubo@eeshokudaiacjp https://googl/z9cjy 7 : 7 : The development of linear models Hierarchical Baesian Model Be more flexible Generalized Linear

More information

kubostat2017c p (c) Poisson regression, a generalized linear model (GLM) : :

kubostat2017c p (c) Poisson regression, a generalized linear model (GLM) : : kubostat2017c p.1 2017 (c), a generalized linear model (GLM) : kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 kubostat2017c (http://goo.gl/76c4i) 2017 (c) 2017 11 14 1 / 47 agenda

More information

/ *1 *1 c Mike Gonzalez, October 14, Wikimedia Commons.

/ *1 *1 c Mike Gonzalez, October 14, Wikimedia Commons. 2010 05 22 1/ 35 2010 2010 05 22 *1 kubo@ees.hokudai.ac.jp *1 c Mike Gonzalez, October 14, 2007. Wikimedia Commons. 2010 05 22 2/ 35 1. 2. 3. 2010 05 22 3/ 35 : 1.? 2. 2010 05 22 4/ 35 1. 2010 05 22 5/

More information

kubostat2017e p.1 I 2017 (e) GLM logistic regression : : :02 1 N y count data or

kubostat2017e p.1 I 2017 (e) GLM logistic regression : : :02 1 N y count data or kubostat207e p. I 207 (e) GLM kubo@ees.hokudai.ac.jp https://goo.gl/z9ycjy 207 4 207 6:02 N y 2 binomial distribution logit link function 3 4! offset kubostat207e (https://goo.gl/z9ycjy) 207 (e) 207 4

More information

(1.2) T D = 0 T = D = 30 kn 1.2 (1.4) 2F W = 0 F = W/2 = 300 kn/2 = 150 kn 1.3 (1.9) R = W 1 + W 2 = = 1100 N. (1.9) W 2 b W 1 a = 0

(1.2) T D = 0 T = D = 30 kn 1.2 (1.4) 2F W = 0 F = W/2 = 300 kn/2 = 150 kn 1.3 (1.9) R = W 1 + W 2 = = 1100 N. (1.9) W 2 b W 1 a = 0 1 1 1.1 1.) T D = T = D = kn 1. 1.4) F W = F = W/ = kn/ = 15 kn 1. 1.9) R = W 1 + W = 6 + 5 = 11 N. 1.9) W b W 1 a = a = W /W 1 )b = 5/6) = 5 cm 1.4 AB AC P 1, P x, y x, y y x 1.4.) P sin 6 + P 1 sin 45

More information

(2/24) : 1. R R R

(2/24) : 1. R R R R? http://hosho.ees.hokudai.ac.jp/ kubo/ce/2004/ : kubo@ees.hokudai.ac.jp (2/24) : 1. R 2. 3. R R (3/24)? 1. ( ) 2. ( I ) : (p ) : cf. (power) p? (4/24) p ( ) I p ( ) I? ( ) (5/24)? 0 2 4 6 8 A B A B (control)

More information

10.00mm 2 A4 0 A4 MS 72pt 25mm MS 25mm MS 2

10.00mm 2 A4 0 A4 MS 72pt 25mm MS 25mm MS 2 Silhouette Studio silhouette CAMEO Silhouette Studio silhouette CAMEO Windows Silhouette Studio mm mm F mm OK 1 10.00mm 2 A4 0 A4 MS 72pt 25mm MS 25mm MS 2 3 4 Silhouette Silhouette Silhouette silhouette

More information

c a a ca c c% c11 c12

c a a ca c c% c11 c12 c a a ca c c% c11 c12 % s & % c13 c14 cc c16 c15 %s & % c211 c21% c212 c21% c213 c21% c214 c21% c215 c21% c216 c21% c23 & % c24 c25 c311 c311 % c% c % c312 %% a c31 c315 c32 c33 c34 % c35 c36 c411 c N

More information

1 1 H Li Be Na M g B A l C S i N P O S F He N Cl A e K Ca S c T i V C Mn Fe Co Ni Cu Zn Ga Ge As Se B K Rb S Y Z Nb Mo Tc Ru Rh Pd Ag Cd In Sn Sb T e

1 1 H Li Be Na M g B A l C S i N P O S F He N Cl A e K Ca S c T i V C Mn Fe Co Ni Cu Zn Ga Ge As Se B K Rb S Y Z Nb Mo Tc Ru Rh Pd Ag Cd In Sn Sb T e No. 1 1 1 H Li Be Na M g B A l C S i N P O S F He N Cl A e K Ca S c T i V C Mn Fe Co Ni Cu Zn Ga Ge As Se B K Rb S Y Z Nb Mo Tc Ru Rh Pd Ag Cd In Sn Sb T e I X e Cs Ba F Ra Hf Ta W Re Os I Rf Db Sg Bh

More information

k3 ( :07 ) 2 (A) k = 1 (B) k = 7 y x x 1 (k2)?? x y (A) GLM (k

k3 ( :07 ) 2 (A) k = 1 (B) k = 7 y x x 1 (k2)?? x y (A) GLM (k 2012 11 01 k3 (2012-10-24 14:07 ) 1 6 3 (2012 11 01 k3) kubo@ees.hokudai.ac.jp web http://goo.gl/wijx2 web http://goo.gl/ufq2 1 3 2 : 4 3 AIC 6 4 7 5 8 6 : 9 7 11 8 12 8.1 (1)........ 13 8.2 (2) χ 2....................

More information

1 Tokyo Daily Rainfall (mm) Days (mm)

1 Tokyo Daily Rainfall (mm) Days (mm) ( ) r-taka@maritime.kobe-u.ac.jp 1 Tokyo Daily Rainfall (mm) 0 100 200 300 0 10000 20000 30000 40000 50000 Days (mm) 1876 1 1 2013 12 31 Tokyo, 1876 Daily Rainfall (mm) 0 50 100 150 0 100 200 300 Tokyo,

More information

Q E Q T a k Q Q Q T Q =

Q E Q T a k Q Q Q T Q = i 415 q q q q Q E Q T a k Q Q Q T Q = 10 30 j 19 25 22 E 23 R 9 i i V 25 60 1 20 1 18 59R1416R30 3018 1211931 30025R 10T1T 425R 11 50 101233 162 633315 22E1011 10T q 26T10T 12 3030 12 12 24 100 1E20 62

More information

kubo2015ngt6 p.2 ( ( (MLE 8 y i L(q q log L(q q 0 ˆq log L(q / q = 0 q ˆq = = = * ˆq = 0.46 ( 8 y 0.46 y y y i kubo (ht

kubo2015ngt6 p.2 ( ( (MLE 8 y i L(q q log L(q q 0 ˆq log L(q / q = 0 q ˆq = = = * ˆq = 0.46 ( 8 y 0.46 y y y i kubo (ht kubo2015ngt6 p.1 2015 (6 MCMC kubo@ees.hokudai.ac.jp, @KuboBook http://goo.gl/m8hsbm 1 ( 2 3 4 5 JAGS : 2015 05 18 16:48 kubo (http://goo.gl/m8hsbm 2015 (6 1 / 70 kubo (http://goo.gl/m8hsbm 2015 (6 2 /

More information

: Bradley-Terry Burczyk

: Bradley-Terry Burczyk 58 (W15) 2011 03 09 kubo@ees.hokudai.ac.jp http://goo.gl/edzle 2011 03 09 (2011 03 09 19 :32 ) : Bradley-Terry Burczyk ? ( ) 1999 2010 9 R : 7 (1) 8 7??! 15 http://www.atmarkit.co.jp/fcoding/articles/stat/07/stat07a.html

More information

k2 ( :35 ) ( k2) (GLM) web web 1 :

k2 ( :35 ) ( k2) (GLM) web   web   1 : 2012 11 01 k2 (2012-10-26 16:35 ) 1 6 2 (2012 11 01 k2) (GLM) kubo@ees.hokudai.ac.jp web http://goo.gl/wijx2 web http://goo.gl/ufq2 1 : 2 2 4 3 7 4 9 5 : 11 5.1................... 13 6 14 6.1......................

More information

2 A4 0 A4 10.00mm 2 MS 72pt 25mm

2 A4 0 A4 10.00mm 2 MS 72pt 25mm Silhouette Studio Silhouette CAMEO 2 Silhouette Studio Silhouette CAMEO 2 Windows Silhouette Studio 1 2 A4 0 A4 10.00mm 2 MS 72pt 25mm MS 25mm MS 3 Silhouette Silhouette 2 1 2 Silhouette CAMEO 2 4 1 0

More information

,, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i

,, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i Armitage.? SAS.2 µ, µ 2, µ 3 a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 µ, µ 2, µ 3 log a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 * 2 2. y t y y y Poisson y * ,, Poisson 3 3. t t y,, y n Nµ,

More information

23 1 Section ( ) ( ) ( 46 ) , 238( 235,238 U) 232( 232 Th) 40( 40 K, % ) (Rn) (Ra). 7( 7 Be) 14( 14 C) 22( 22 Na) (1 ) (2 ) 1 µ 2 4

23 1 Section ( ) ( ) ( 46 ) , 238( 235,238 U) 232( 232 Th) 40( 40 K, % ) (Rn) (Ra). 7( 7 Be) 14( 14 C) 22( 22 Na) (1 ) (2 ) 1 µ 2 4 23 1 Section 1.1 1 ( ) ( ) ( 46 ) 2 3 235, 238( 235,238 U) 232( 232 Th) 40( 40 K, 0.0118% ) (Rn) (Ra). 7( 7 Be) 14( 14 C) 22( 22 Na) (1 ) (2 ) 1 µ 2 4 2 ( )2 4( 4 He) 12 3 16 12 56( 56 Fe) 4 56( 56 Ni)

More information

12-7 12-7 12-7 12-7 12-8 12-10 12-10 12-10 12-11 12-12 12-12 12-14 12-15 12-17 12-18 10 12-19 12-20 12-20 12-21 12-22 12-22 12-23 12-25 12-26 12-26 12-29 12-30 12-30 12-31 12-33 12-34 12-3 12-35 12-36

More information

講義のーと : データ解析のための統計モデリング. 第2回

講義のーと :  データ解析のための統計モデリング. 第2回 Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20

More information

56cm 1 15 1960 2 8 2 2 1 2008 1992 2 1992 2 3562mm 3773mm 2 1980 1991 2008 2007 2003 5 2 3 2003 2005 2008 2010 2005 2008 2012 2010 2012 4 7 4 5 2 1975 1994 8 2008 NPO 2 2010 3 2013 2016 3 2008 2009 14

More information

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 5 2 5 24 () () () () () 1 1 150 50 50 1 1 ( 15,000 ) 150 ( 15,000 ) 100 50 50 1 1 ( 6,000 ) 150 ( 6,000 ) 100 50 50 1 1 150 1 1 150 100 0.25

More information

1

1 1 2 3 4 . 5 6 7 8 9 10 11 12 .. 13 .. 14 15 16 17 18 19 20 .. 223 ( ) 218 1 21 1 225 72 63 154 141 54 24 40 274 186 226 197 507 22 23 24 25 26 27 28 29 .... 30 SPC 31 32 33 34 35 36 . 37 38 39 .......

More information

1 2

1 2 1 2 4 3 5 6 8 7 9 10 12 11 0120-889-376 r 14 13 16 15 0120-0889-24 17 18 19 0120-8740-16 20 22 21 24 23 26 25 28 27 30 29 32 31 34 33 36 35 38 37 40 39 42 41 44 43 46 45 48 47 50 49 52 51 54 53 56 55 58

More information

3 5 6 7 7 8 9 5 7 9 4 5 6 6 7 8 8 8 9 9 3 3 3 3 8 46 4 49 57 43 65 6 7 7 948 97 974 98 99 993 996 998 999 999 4 749 7 77 44 77 55 3 36 5 5 4 48 7 a s d f g h a s d f g h a s d f g h a s d f g h j 83 83

More information

財務情報

財務情報 50 Financial Information >> 52 54 66 68 69 71 72 51 2004 4,625,151 2,605,343 2,019,807 N/A 373,435 139,401 234,034 (7,603) 2005 2006 2007 2008 2008 4,664,514 4,637,657 4,769,387 6,409,727 $ 63,976 2,650,586

More information

x, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y)

x, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y) x, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 1 1977 x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y) ( x 2 y + xy 2 x 2 2xy y 2) = 15 (x y) (x + y) (xy

More information

untitled

untitled 1 211022 2 11150 211022384 3 1000 23% 77% 10% 10% 5% 20% 15% 40% 5% 3% 8% 16% 15% 42% 5% 6% 4 =1000 = 66 5 =1000 = 59 6 52%(42% 1000 7 56% 41% 40% 97% 3% 11%, 2% 3%, 41 7% 49% 30%, 18%, 40%, 83% =1000

More information

X X X Y R Y R Y R MCAR MAR MNAR Figure 1: MCAR, MAR, MNAR Y R X 1.2 Missing At Random (MAR) MAR MCAR MCAR Y X X Y MCAR 2 1 R X Y Table 1 3 IQ MCAR Y I

X X X Y R Y R Y R MCAR MAR MNAR Figure 1: MCAR, MAR, MNAR Y R X 1.2 Missing At Random (MAR) MAR MCAR MCAR Y X X Y MCAR 2 1 R X Y Table 1 3 IQ MCAR Y I (missing data analysis) - - 1/16/2011 (missing data, missing value) (list-wise deletion) (pair-wise deletion) (full information maximum likelihood method, FIML) (multiple imputation method) 1 missing completely

More information

. ev=,604k m 3 Debye ɛ 0 kt e λ D = n e n e Ze 4 ln Λ ν ei = 5.6π / ɛ 0 m/ e kt e /3 ν ei v e H + +e H ev Saha x x = 3/ πme kt g i g e n

. ev=,604k m 3 Debye ɛ 0 kt e λ D = n e n e Ze 4 ln Λ ν ei = 5.6π / ɛ 0 m/ e kt e /3 ν ei v e H + +e H ev Saha x x = 3/ πme kt g i g e n 003...............................3 Debye................. 3.4................ 3 3 3 3. Larmor Cyclotron... 3 3................ 4 3.3.......... 4 3.3............ 4 3.3...... 4 3.3.3............ 5 3.4.........

More information

総研大恒星進化概要.dvi

総研大恒星進化概要.dvi The Structure and Evolution of Stars I. Basic Equations. M r r =4πr2 ρ () P r = GM rρ. r 2 (2) r: M r : P and ρ: G: M r Lagrange r = M r 4πr 2 rho ( ) P = GM r M r 4πr. 4 (2 ) s(ρ, P ) s(ρ, P ) r L r T

More information

2.

2. 2. 10 2. 2. 1995/12006/111995/42006/12 2. 10 1995120061119954200612 02505 025 05 025 02505 0303 02505 250100 250 200 100200 5010050 100200 100 100 50100 100200 50100 10 75100100 0250512 02505 1 025051205

More information

1 1 x y = y(x) y, y,..., y (n) : n y F (x, y, y,..., y (n) ) = 0 n F (x, y, y ) = 0 1 y(x) y y = G(x, y) y, y y + p(x)y = q(x) 1 p(x) q(

1 1 x y = y(x) y, y,..., y (n) : n y F (x, y, y,..., y (n) ) = 0 n F (x, y, y ) = 0 1 y(x) y y = G(x, y) y, y y + p(x)y = q(x) 1 p(x) q( 1 1 y = y() y, y,..., y (n) : n y F (, y, y,..., y (n) ) = 0 n F (, y, y ) = 0 1 y() 1.1 1 y y = G(, y) 1.1.1 1 y, y y + p()y = q() 1 p() q() (q() = 0) y + p()y = 0 y y + py = 0 y y = p (log y) = p log

More information

Part () () Γ Part ,

Part () () Γ Part , Contents a 6 6 6 6 6 6 6 7 7. 8.. 8.. 8.3. 8 Part. 9. 9.. 9.. 3. 3.. 3.. 3 4. 5 4.. 5 4.. 9 4.3. 3 Part. 6 5. () 6 5.. () 7 5.. 9 5.3. Γ 3 6. 3 6.. 3 6.. 3 6.3. 33 Part 3. 34 7. 34 7.. 34 7.. 34 8. 35

More information

& 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable),

& 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable), .... Deeping and Expansion of Large-Scale Random Fields and Probabilistic Image Processing Kazuyuki Tanaka The mathematical frameworks of probabilistic image processing are formulated by means of Markov

More information

) km 200 m ) ) ) ) ) ) ) kg kg ) 017 x y x 2 y 5x 5 y )

) km 200 m ) ) ) ) ) ) ) kg kg ) 017 x y x 2 y 5x 5 y ) 001 ) g 20 g 5 300 g 7 002 720 g 2 ) g 003 0.8 m 2 ) cm 2 004 12 15 1 3 1 ) 005 5 0.8 0.4 ) 6 006 5 2 3 66 ) 007 1 700 12 ) 008 0.315 ) 009 500 g ) kg 0.2 t 189 kg 17.1 kg 010 5 1 2 cm 3 cm )km 2-1 - 011

More information

06佐々木雅哉_4C.indd

06佐々木雅哉_4C.indd 3 2 3 2 4 5 56 57 3 2013 9 2012 16 19 62.2 17 2013 7 170 77 170 131 58 9 10 59 3 2 10 15 F 12 12 48 60 1 3 1 4 7 61 3 7 1 62 T C C T C C1 2 3 T C 1 C 1 T C C C T T C T C C 63 3 T 4 T C C T C C CN T C C

More information

V 0 = + r pv (H) + qv (T ) = + r ps (H) + qs (T ) = S 0 X n+ (T ) = n S n+ (T ) + ( + r)(x n n S n ) = ( + r)x n + n (d r)s n = ( + r)v n + V n+(h) V

V 0 = + r pv (H) + qv (T ) = + r ps (H) + qs (T ) = S 0 X n+ (T ) = n S n+ (T ) + ( + r)(x n n S n ) = ( + r)x n + n (d r)s n = ( + r)v n + V n+(h) V I (..2) (0 < d < + r < u) X 0, X X = 0 S + ( + r)(x 0 0 S 0 ) () X 0 = 0, P (X 0) =, P (X > 0) > 0 0 H, T () X 0 = 0, X (H) = 0 us 0 ( + r) 0 S 0 = 0 S 0 (u r) X (T ) = 0 ds 0 ( + r) 0 S 0 = 0 S 0 (d r)

More information

Chapter9 9 LDPC sum-product LDPC 9.1 ( ) 9.2 c 1, c 2, {0, 1, } SUM, PROD : {0, 1, } {0, 1, } SUM(c 1, c 2,, c n ) := { c1 + + c n (c n0 (1 n

Chapter9 9 LDPC sum-product LDPC 9.1 ( ) 9.2 c 1, c 2, {0, 1, } SUM, PROD : {0, 1, } {0, 1, } SUM(c 1, c 2,, c n ) := { c1 + + c n (c n0 (1 n 9 LDPC sum-product 9.1 9.2 LDPC 9.1 ( ) 9.2 c 1, c 2, {0, 1, } SUM, PROD : {0, 1, } {0, 1, } SUM(c 1, c 2,, c n ) := { c1 + + c n (c n0 (1 n 0 n)) ( ) 0 (N(0 c) > N(1 c)) PROD(c 1, c 2,, c n ) := 1 (N(0

More information

kubostat2017b p.1 agenda I 2017 (b) probability distribution and maximum likelihood estimation :

kubostat2017b p.1 agenda I 2017 (b) probability distribution and maximum likelihood estimation : kubostat2017b p.1 agenda I 2017 (b) probabilit distribution and maimum likelihood estimation kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 1 : 2 3? 4 kubostat2017b (http://goo.gl/76c4i)

More information

The Recording Industry in Japan Statistics Analysis Trends 2005

The Recording Industry in Japan Statistics Analysis Trends 2005 The Recording Industry in Japan Statistics Analysis Trends 8cmCD cmcd cmcd CD DVD LD DVD LD,99,76,6 6,,6 66,47 78,9,9 8,8 4, 7,7,4 4,6 78,9, 6 9 894 8,98 9 8,999 7 6 4, 78,67,68 7, 6,,7, 8,8 6,9 4,77,68

More information

‚åŁÎ“·„´Šš‡ðŠp‡¢‡½‹âfi`fiI…A…‰…S…−…Y…•‡ÌMarkovŸA“½fiI›ð’Í

‚åŁÎ“·„´Šš‡ðŠp‡¢‡½‹âfi`fiI…A…‰…S…−…Y…•‡ÌMarkovŸA“½fiI›ð’Í Markov 2009 10 2 Markov 2009 10 2 1 / 25 1 (GA) 2 GA 3 4 Markov 2009 10 2 2 / 25 (GA) (GA) L ( 1) I := {0, 1} L f : I (0, ) M( 2) S := I M GA (GA) f (i) i I Markov 2009 10 2 3 / 25 (GA) ρ(i, j), i, j I

More information

untitled

untitled . 23 3 2 1 2 3 21 21 22 9 1 23 18 4 5 6 5 40 20 7 1 1 8 9 10 11 12 50cm 13 10 11 14 2011 3 11 15 16 ml mm tel 079-557-0039 & fax 079-557-1888 17 248-240- 98-48- 170-98- 98- ml 85-1,085-18 19 20 21 22 23

More information

36 th IChO : - 3 ( ) , G O O D L U C K final 1

36 th IChO : - 3 ( ) , G O O D L U C K final 1 36 th ICh - - 5 - - : - 3 ( ) - 169 - -, - - - - - - - G D L U C K final 1 1 1.01 2 e 4.00 3 Li 6.94 4 Be 9.01 5 B 10.81 6 C 12.01 7 N 14.01 8 16.00 9 F 19.00 10 Ne 20.18 11 Na 22.99 12 Mg 24.31 Periodic

More information

( )

( ) 5 60 2 1 54 ( ) 0.8 2 37 3 180 4 1 9 123654789 1 2 3 4 5 6 7 8 9 5 32 4 9 3 8 2 5 6 0 7 30 36 24 8 8 6 450 3 9 26 5 2 2016 2013-2015 14 10 ABC 24DEF BCADAB BEF A F E B D C 11 4 4 1 5 5 2 6 6 3 12 54 24

More information

80 4 r ˆρ i (r, t) δ(r x i (t)) (4.1) x i (t) ρ i ˆρ i t = 0 i r 0 t(> 0) j r 0 + r < δ(r 0 x i (0))δ(r 0 + r x j (t)) > (4.2) r r 0 G i j (r, t) dr 0

80 4 r ˆρ i (r, t) δ(r x i (t)) (4.1) x i (t) ρ i ˆρ i t = 0 i r 0 t(> 0) j r 0 + r < δ(r 0 x i (0))δ(r 0 + r x j (t)) > (4.2) r r 0 G i j (r, t) dr 0 79 4 4.1 4.1.1 x i (t) x j (t) O O r 0 + r r r 0 x i (0) r 0 x i (0) 4.1 L. van. Hove 1954 space-time correlation function V N 4.1 ρ 0 = N/V i t 80 4 r ˆρ i (r, t) δ(r x i (t)) (4.1) x i (t) ρ i ˆρ i t

More information

kubostat1g p. MCMC binomial distribution q MCMC : i N i y i p(y i q = ( Ni y i q y i (1 q N i y i, q {y i } q likelihood q L(q {y i } = i=1 p(y i q 1

kubostat1g p. MCMC binomial distribution q MCMC : i N i y i p(y i q = ( Ni y i q y i (1 q N i y i, q {y i } q likelihood q L(q {y i } = i=1 p(y i q 1 kubostat1g p.1 1 (g Hierarchical Bayesian Model kubo@ees.hokudai.ac.jp http://goo.gl/7ci The development of linear models Hierarchical Bayesian Model Be more flexible Generalized Linear Mixed Model (GLMM

More information

CRA3689A

CRA3689A AVIC-DRZ90 AVIC-DRZ80 2 3 4 5 66 7 88 9 10 10 10 11 12 13 14 15 1 1 0 OPEN ANGLE REMOTE WIDE SET UP AVIC-DRZ90 SOURCE OFF AV CONTROL MIC 2 16 17 1 2 0 0 1 AVIC-DRZ90 2 3 4 OPEN ANGLE REMOTE SOURCE OFF

More information

76 3 B m n AB P m n AP : PB = m : n A P B P AB m : n m < n n AB Q Q m A B AQ : QB = m : n (m n) m > n m n Q AB m : n A B Q P AB Q AB 3. 3 A(1) B(3) C(

76 3 B m n AB P m n AP : PB = m : n A P B P AB m : n m < n n AB Q Q m A B AQ : QB = m : n (m n) m > n m n Q AB m : n A B Q P AB Q AB 3. 3 A(1) B(3) C( 3 3.1 3.1.1 1 1 A P a 1 a P a P P(a) a P(a) a P(a) a a 0 a = a a < 0 a = a a < b a > b A a b a B b B b a b A a 3.1 A() B(5) AB = 5 = 3 A(3) B(1) AB = 3 1 = A(a) B(b) AB AB = b a 3.1 (1) A(6) B(1) () A(

More information

4. ϵ(ν, T ) = c 4 u(ν, T ) ϵ(ν, T ) T ν π4 Planck dx = 0 e x 1 15 U(T ) x 3 U(T ) = σt 4 Stefan-Boltzmann σ 2π5 k 4 15c 2 h 3 = W m 2 K 4 5.

4. ϵ(ν, T ) = c 4 u(ν, T ) ϵ(ν, T ) T ν π4 Planck dx = 0 e x 1 15 U(T ) x 3 U(T ) = σt 4 Stefan-Boltzmann σ 2π5 k 4 15c 2 h 3 = W m 2 K 4 5. A 1. Boltzmann Planck u(ν, T )dν = 8πh ν 3 c 3 kt 1 dν h 6.63 10 34 J s Planck k 1.38 10 23 J K 1 Boltzmann u(ν, T ) T ν e hν c = 3 10 8 m s 1 2. Planck λ = c/ν Rayleigh-Jeans u(ν, T )dν = 8πν2 kt dν c

More information