S = k B (N A n c A + N B n c B ) (83) [ ] B A (N A N B ) G = N B µ 0 B (T,P)+N Aψ(T,P)+N A k B T n N A en B (84) 2 A N A 3 (83) N A N B µ B = µ 0 B(T,
|
|
- せとか ありの
- 7 years ago
- Views:
Transcription
1 8.5 [ ]<, > 2 A B Z(T,V,N) = d 3N A p N A!N B!(2π h) 3N A d 3N A q A d 3N B p B d 3N B q B e β(h A(p A,q A ;V )+H B (p B,q B ;V )) = Z A (T,V,N A )Z B (T,V,N B ) (74) F (T,V,N)=F A (T,V,N A )+F B (T,V,N B ) (75) V (partia pressure)p A,B P = P A (T,V,N A )+P B (T,V,N A )=c A P + c B P (76) P A P B A B c A,B c A = N A N A + N B c B = N B N A + N B (77) φ A,B (T ) µ A,B = k B T n k ( ) 3/2 BT ma,b k B T c A,B P 2π h 2 + φ A,B (T ) (78) µ 0 A,B µ A,B = µ 0 A,B + k B T n c A,B (79) (c A c B ) µ A = µ 0 A (T,P)+k BT n c A (80) µ B = µ 0 B(T,P) k B Tc A (8) (79) G = k B T (N A n c A + N B n c B ) (< 0) (82) 53
2 S = k B (N A n c A + N B n c B ) (83) [ ] B A (N A N B ) G = N B µ 0 B (T,P)+N Aψ(T,P)+N A k B T n N A en B (84) 2 A N A 3 (83) N A N B µ B = µ 0 B(T,P) c A k B T (85) µ A = ψ(t,p)+k B T n c A (86) µ 0 B (T,P) ψ(t,p) [ ]< 80, 8 > (85) µ B (T,P,c A )=µ B (T,P 2,c A2 ) (87) µ 0 B (T,P ) µ 0 B (T,P 2)=(c A c A2 )k B T (88) v B (P P 2 )=(c A c A2 )k B T (89) (osmotic pressure) P = c A v B k B T (90) 54
3 P = c A v B k B T = N A V k BT (9) ( : van t Hoff aw of osmotic pressure) [ 2 ]< > 2 2 A µ () B (T,P)=µ (2) B (T,P) (92) A δt µ () B (T + δt, P) k BTc () A = µ(2) B (T + δt, P) k BTc (2) A (93) (s () B s (2) B )δt = (c () A c (2) A )k B T (94) () (2) (s (2) B s() B )T = q δt =(c () A c (2) A ) k BT 2 q (95) µ () B (T,P + δp) k B Tc () A = µ (2) B (T,P + δp) k B Tc (2) A (96) (v () B v (2) B )δp =(c () A c (2) A )k B T (97) δp =(c () A c (2) A ) k B T v () B v (2) B 2 A c (2) A δp c () k B T A v (2) B 0 v() B (98) v(2) B c () A P 0 (T ) (99) 55
4 P 0 ( Raout s aw) [ ]< 64, 65 > A + B C (= AB) (00) G(T,P,N A,N B,N C ) dg = G G G = 0 (0) dn C N C NA,N B N A NB,N C N B NC,N A µ A + µ B = µ C (= µ AB ) (02) ( ) ν i A i = 0 (03) i ν i µ i = 0 (04) i [ ]< 72, 73 > (78) (02) ε b c AB = ( 2π h 2) ( ) 3/2 mab 3/2 P c A c B m A m B (k B T ) 5/2 e(ε b φ AB (T )+φ A (T )+φ B (T ))/k B T (05) (aw of mass action) 56
5 9 S = k B n Ω /N! p p 2 p 2 p /2! p /2! 9. [ ] 2 (Boson, boson) (Fermion, fermion) Ψ(q 2,q )=±Ψ(q,q 2 ) () ( ) 2 ψ (q) ψ 2 (q) 2 Ψ(q,q 2 )= 2! (ψ (q )ψ 2 (q 2 ) ± ψ (q 2 )ψ 2 (q )) (2) Ψ B (q,,q N )= N!N!N 2! Ψ F (q,,q N )= N! N! a permutations N! a permutations ψ k (q ) ψ kn (q N ) (3) ( ) P ψ k (q ) ψ kn (q N ) = N! det (ψ i (q ki )) (4) ( ) P N i, 2,,N i i {N i } N i 0 57
6 9.2 [ ] ( Z(T,V,N)= exp β i a possibe configurations of N= N i N i ε i ) (5) N i 0 [ ] i i) (N i =0) E =0 ii) (N i =) E = ε i Z i (T,V,µ) = N i =0 = +e β(µ ε i) e β(e(n i ) i µn i ) (6) Z(T,V,µ)= i Z i (T,V,µ) (7) Φ(T,V,µ) = k B T i n Z i (T,V,µ) = i Φ i (T,V,µ) = k B T i n ( +e β(µ ε i) ) (8) i N i = Φ i µ βe β(µ ε i) +e β(µ ε i) = β = e β(ε i µ) + i (Fermi-Dirac statistics) 58 (9)
7 (Fermi distribution function) f FD (ε) = e β(ε µ) + (0) f e T Figure 6: (k B T =) µ =, µ = µ =5 µ N = Φ µ = i e β(ε i µ) + () ( ) x (ε µ)/k B T f FD (ε) = = e β(ε µ) + e x + = ( tanh x ) 2 2 (2) ε = µ f FD =/2 f FD (ε) θ(µ ε) (3) ε : N i = f FD (ε) e β(ε µ) e βµ (βµ ) f FD (ε) e β(µ ε) (4) (Maxwe-Botzmann distribution) 59
8 [ ] i 0 Z i (T,V,µ) = = = = N i =0 e βµn i Z i (T,V,N) e βµn i e βε in i N i =0 e β(µ ε i)n i N i =0 e β(µ ε i) (5) Z(T,V,µ)= i Z i (T,V,µ) (6) Φ(T,V,µ) = k B T i n Z i (T,V,µ) = i Φ i (T,V,µ) = k B T i n ( e β(µ ε i) ) (7) i N i = Φ i µ βe β(µ ε i) = β e β(µ ε i) = e β(ε i µ) (8) i (Bose-Einstein statistics) (Bose distribution function) f BE (E) = e β(ε µ) (9) 60
9 f e T Figure 7: (k B T =) µ =, µ = 0.5, µ =0, µ N = Φ µ = i e β(ε i µ) (20) ( ) III e β(ε µ) f BE (ε) e β(µ ε) (2) f e T Figure 8: (µ =, k B T =) 6
10 9.3 [ ] {ε i } ε M N N M W = M C N = M! N!(M N )! {N } S = k B n W (22) ( k B M n M e N n N e (M N )n (M ) N ) e = k B M [n n n +( n )n( n )] (23) n = N /M N = M n (24) E = M ε n (25) ( ) S αn βe = S (α βε ) M n kb k B n ] n = M [n + α + βε n n = = 0 (26) e βε +α + (27) α β N = M e βε +α + 62 (28)
11 E = M ε e βε +α + (29) α β [ ] α β N E : S(E,V,N) ( V ) (26) T = ( S E ) V,N = ( ) ( ) S n M n E V,N V,N ( ) n = k B M (α + βε ) E V,N = k B α ( ) n M + β ( ) n M ε E E V,N V,N = k B β (30) N 2 E β /k B T µ ( ) S T = N E,V = ( ) ( ) S n M n N E,V E,V ( ) n = k B M (α + βε ) N E,V = k B α ( ) n M + β ( ) n M ε N E,V N E,V = k B α (3) α = µ/k B T [ ] {ε i } N W = M +N C N = M + N N!(M )! (32) 63
12 {N } S = k B n W [ k B (M + N )n (M + N ) e = k B M n M e N n N ] e M [( + n )n(+n ) n n n ] (33) N E ( S n kb ) αn βe = M [ n +n n α βε = 0 (34) ] n = e βε +α (35) α β 64
5 36 5................................................... 36 5................................................... 36 5.3..............................
9 8 3............................................. 3.......................................... 4.3............................................ 4 5 3 6 3..................................................
More information1 913 10301200 A B C D E F G H J K L M 1A1030 10 : 45 1A1045 11 : 00 1A1100 11 : 15 1A1115 11 : 30 1A1130 11 : 45 1A1145 12 : 00 1B1030 1B1045 1C1030
1 913 9001030 A B C D E F G H J K L M 9:00 1A0900 9:15 1A0915 9:30 1A0930 9:45 1A0945 10 : 00 1A1000 10 : 15 1B0900 1B0915 1B0930 1B0945 1B1000 1C0900 1C0915 1D0915 1C0930 1C0945 1C1000 1D0930 1D0945 1D1000
More information7 9 7..................................... 9 7................................ 3 7.3...................................... 3 A A. ω ν = ω/π E = hω. E
B 8.9.4, : : MIT I,II A.P. E.F.,, 993 I,,, 999, 7 I,II, 95 A A........................... A........................... 3.3 A.............................. 4.4....................................... 5 6..............................
More information24.15章.微分方程式
m d y dt = F m d y = mg dt V y = dy dt d y dt = d dy dt dt = dv y dt dv y dt = g dv y dt = g dt dt dv y = g dt V y ( t) = gt + C V y ( ) = V y ( ) = C = V y t ( ) = gt V y ( t) = dy dt = gt dy = g t dt
More information3
- { } / f ( ) e nπ + f( ) = Cne n= nπ / Eucld r e (= N) j = j e e = δj, δj = 0 j r e ( =, < N) r r r { } ε ε = r r r = Ce = r r r e ε = = C = r C r e + CC e j e j e = = ε = r ( r e ) + r e C C 0 r e =
More information,..,,.,,.,.,..,,.,,..,,,. 2
A.A. (1906) (1907). 2008.7.4 1.,.,.,,.,,,.,..,,,.,,.,, R.J.,.,.,,,..,.,. 1 ,..,,.,,.,.,..,,.,,..,,,. 2 1, 2, 2., 1,,,.,, 2, n, n 2 (, n 2 0 ).,,.,, n ( 2, ), 2 n.,,,,.,,,,..,,. 3 x 1, x 2,..., x n,...,,
More informationx : = : x x
x : = : x x x :1 = 1: x 1 x : = : x x : = : x x : = : x x ( x ) = x = x x = + x x = + + x x = + + + + x = + + + + +L x x :1 = 1: x 1 x ( x 1) = 1 x 2 x =1 x 2 x 1= 0 1± 1+ 4 x = 2 = 1 ± 5 2 x > 1
More information診療ガイドライン外来編2014(A4)/FUJGG2014‐01(大扉)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
More informationall.dvi
I 1 Density Matrix 1.1 ( (Observable) Ô :ensemble ensemble average) Ô en =Tr ˆρ en Ô ˆρ en Tr  n, n =, 1,, Tr  = n n  n Tr  I w j j ( j =, 1,, ) ˆρ en j w j j ˆρ en = j w j j j Ô en = j w j j Ô j emsemble
More information0.,,., m Euclid m m. 2.., M., M R 2 ψ. ψ,, R 2 M.,, (x 1 (),, x m ()) R m. 2 M, R f. M (x 1,, x m ), f (x 1,, x m ) f(x 1,, x m ). f ( ). x i : M R.,,
2012 10 13 1,,,.,,.,.,,. 2?.,,. 1,, 1. (θ, φ), θ, φ (0, π),, (0, 2π). 1 0.,,., m Euclid m m. 2.., M., M R 2 ψ. ψ,, R 2 M.,, (x 1 (),, x m ()) R m. 2 M, R f. M (x 1,, x m ), f (x 1,, x m ) f(x 1,, x m ).
More information受賞講演要旨2012cs3
アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート α β α α α α α
More information(interval estimation) 3 (confidence coefficient) µ σ/sqrt(n) 4 P ( (X - µ) / (σ sqrt N < a) = α a α X α µ a σ sqrt N X µ a σ sqrt N 2
7 2 1 (interval estimation) 3 (confidence coefficient) µ σ/sqrt(n) 4 P ( (X - µ) / (σ sqrt N < a) = α a α X α µ a σ sqrt N X µ a σ sqrt N 2 (confidence interval) 5 X a σ sqrt N µ X a σ sqrt N - 6 P ( X
More information橡Taro11-卒業論文.PDF
Recombination Generation Lifetime 13 9 1. 3. 4.1. 4.. 9 3. Recombination Lifetime 17 3.1. 17 3.. 19 3.3. 4. 1 4.1. Si 1 4.1.1. 1 4.1.. 4.. TEG 3 5. Recombination Lifetime 4 5.1 Si 4 5.. TEG 6 6. Pulse
More information20 15 14.6 15.3 14.9 15.7 16.0 15.7 13.4 14.5 13.7 14.2 10 10 13 16 19 22 1 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 2,500 59,862 56,384 2,000 42,662 44,211 40,639 37,323 1,500 33,408 34,472
More informationI? 3 1 3 1.1?................................. 3 1.2?............................... 3 1.3!................................... 3 2 4 2.1........................................ 4 2.2.......................................
More information- 2 -
- 2 - - 3 - (1) (2) (3) (1) - 4 - ~ - 5 - (2) - 6 - (1) (1) - 7 - - 8 - (i) (ii) (iii) (ii) (iii) (ii) 10 - 9 - (3) - 10 - (3) - 11 - - 12 - (1) - 13 - - 14 - (2) - 15 - - 16 - (3) - 17 - - 18 - (4) -
More information2 1980 8 4 4 4 4 4 3 4 2 4 4 2 4 6 0 0 6 4 2 4 1 2 2 1 4 4 4 2 3 3 3 4 3 4 4 4 4 2 5 5 2 4 4 4 0 3 3 0 9 10 10 9 1 1
1 1979 6 24 3 4 4 4 4 3 4 4 2 3 4 4 6 0 0 6 2 4 4 4 3 0 0 3 3 3 4 3 2 4 3? 4 3 4 3 4 4 4 4 3 3 4 4 4 4 2 1 1 2 15 4 4 15 0 1 2 1980 8 4 4 4 4 4 3 4 2 4 4 2 4 6 0 0 6 4 2 4 1 2 2 1 4 4 4 2 3 3 3 4 3 4 4
More information1 (1) (2)
1 2 (1) (2) (3) 3-78 - 1 (1) (2) - 79 - i) ii) iii) (3) (4) (5) (6) - 80 - (7) (8) (9) (10) 2 (1) (2) (3) (4) i) - 81 - ii) (a) (b) 3 (1) (2) - 82 - - 83 - - 84 - - 85 - - 86 - (1) (2) (3) (4) (5) (6)
More information2 T(x - v τ) i ix T(x + v τ) i ix x T = ((dt/dx),, ) ( q = c T (x i ) v i ( ) ) dt v ix τ v i dx i i ( (dt = cτ ) ) v 2 dx ix,, () i x = const. FIG. 2
Y. Kondo Department of Physics, Kinki University, Higashi-Osaka, Japan (Dated: September 3, 27) [] PACS numbers: I. m cm 3 24 e =.62 9 As m = 9.7 3 kg A. Drude-orentz Drude orentz N. i v i j = N q i v
More informationuntitled
Y = Y () x i c C = i + c = ( x ) x π (x) π ( x ) = Y ( ){1 + ( x )}( 1 x ) Y ( )(1 + C ) ( 1 x) x π ( x) = 0 = ( x ) R R R R Y = (Y ) CS () CS ( ) = Y ( ) 0 ( Y ) dy Y ( ) A() * S( π ), S( CS) S( π ) =
More information3 3.3. I 3.3.2. [ ] N(µ, σ 2 ) σ 2 (X 1,..., X n ) X := 1 n (X 1 + + X n ): µ X N(µ, σ 2 /n) 1.8.4 Z = X µ σ/ n N(, 1) 1.8.2 < α < 1/2 Φ(z) =.5 α z α
2 2.1. : : 2 : ( ): : ( ): : : : ( ) ( ) ( ) : ( pp.53 6 2.3 2.4 ) : 2.2. ( ). i X i (i = 1, 2,..., n) X 1, X 2,..., X n X i (X 1, X 2,..., X n ) ( ) n (x 1, x 2,..., x n ) (X 1, X 2,..., X n ) : X 1,
More information漸化式のすべてのパターンを解説しましたー高校数学の達人・河見賢司のサイト
https://www.hmg-gen.com/tuusin.html https://www.hmg-gen.com/tuusin1.html 1 2 OK 3 4 {a n } (1) a 1 = 1, a n+1 a n = 2 (2) a 1 = 3, a n+1 a n = 2n a n a n+1 a n = ( ) a n+1 a n = ( ) a n+1 a n {a n } 1,
More informationkoji07-02.dvi
007 I II III 1,, 3, 4, 5, 6, 7 5 4 1 ε-n 1 ε-n ε-n ε-n. {a } =1 a ε N N a a N= a a
More information高齢化の経済分析.pdf
( 2 65 1995 14.8 2050 33.4 1 2 3 1 7 3 2 1980 3 79 4 ( (1992 1 ( 6069 8 7079 5 80 3 80 1 (1 (Sample selection bias 1 (1 1* 80 1 1 ( (1 0.628897 150.5 0.565148 17.9 0.280527 70.9 0.600129 31.5 0.339812
More information一般演題(ポスター)
6 5 13 : 00 14 : 00 A μ 13 : 00 14 : 00 A β β β 13 : 00 14 : 00 A 13 : 00 14 : 00 A 13 : 00 14 : 00 A β 13 : 00 14 : 00 A β 13 : 00 14 : 00 A 13 : 00 14 : 00 A β 13 : 00 14 : 00 A 13 : 00 14 : 00 A
More information第85 回日本感染症学会総会学術集会後抄録(III)
β β α α α µ µ µ µ α α α α γ αβ α γ α α γ α γ µ µ β β β β β β β β β µ β α µ µ µ β β µ µ µ µ µ µ γ γ γ γ γ γ µ α β γ β β µ µ µ µ µ β β µ β β µ α β β µ µµ β µ µ µ µ µ µ λ µ µ β µ µ µ µ µ µ µ µ
More informationf (x) f (x) f (x) f (x) f (x) 2 f (x) f (x) f (x) f (x) 2 n f (x) n f (n) (x) dn f f (x) dx n dn dx n D n f (x) n C n C f (x) x = a 1 f (x) x = a x >
5.1 1. x = a f (x) a x h f (a + h) f (a) h (5.1) h 0 f (x) x = a f +(a) f (a + h) f (a) = lim h +0 h (5.2) x h h 0 f (a) f (a + h) f (a) f (a h) f (a) = lim = lim h 0 h h 0 h (5.3) f (x) x = a f (a) =
More information(CN)
ONY CN ( ) (CN) ONY VAIO o-net ony Plaza Edy 1 1 1946 0 1955 CG( ) 1979 3 1986 10 199 3 9.3 1 3000 900 13 4 1988 CB CB ( ME)1989 ( PE) 1993 ME 50 (CE) 1995 1996 (o-net) 1997 001 P P, P P b a + r + r P
More information地域総合研究第40巻第1号
* abstract This paper attempts to show a method to estimate joint distribution for income and age with copula function. Further, we estimate the joint distribution from National Survey of Family Income
More informationP1-1 P1-2 P1-3 P1-4 P1-5 P1-6 P3-1 P3-2 P3-3 P3-4 P3-5 P3-6 P5-1 P5-2 P5-3 P5-4 P5-5 P5-6 P7-1 P7-2 P7-3 P7-4 P7-5 P7-6 P9-1 P9-2 P9-3 P9-4 P9-5 P9-6 P11-1 P11-2 P11-3 P11-4 P13-1 P13-2 P13-3 P13-4 P13-5
More informationPart. 4. () 4.. () 4.. 3 5. 5 5.. 5 5.. 6 5.3. 7 Part 3. 8 6. 8 6.. 8 6.. 8 7. 8 7.. 8 7.. 3 8. 3 9., 34 9.. 34 9.. 37 9.3. 39. 4.. 4.. 43. 46.. 46..
Cotets 6 6 : 6 6 6 6 6 6 7 7 7 Part. 8. 8.. 8.. 9..... 3. 3 3.. 3 3.. 7 3.3. 8 Part. 4. () 4.. () 4.. 3 5. 5 5.. 5 5.. 6 5.3. 7 Part 3. 8 6. 8 6.. 8 6.. 8 7. 8 7.. 8 7.. 3 8. 3 9., 34 9.. 34 9.. 37 9.3.
More informationMicro-D 小型高密度角型コネクタ
Micro- 1 2 0.64 1.27 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 1.09 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 3 4 J J
More informationレイアウト 1
1 1 3 5 25 41 51 57 109 2 4 Q1 A. 93% 62% 41% 6 7 8 Q1-(1) Q2 A. 24% 13% 52% Q3 Q3 A. 68% 64 Q3-(2) Q3-(1) 9 10 A. Q3-(1) 11 A. Q3-(2) 12 A. 64% Q4 A. 47% 47% Q5 QQ A. Q Q A. 13 QQ A. 14 Q5-(1) A. Q6
More information10_11p01(Ł\”ƒ)
q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q
More informationP-12 P-13 3 4 28 16 00 17 30 P-14 P-15 P-16 4 14 29 17 00 18 30 P-17 P-18 P-19 P-20 P-21 P-22
1 14 28 16 00 17 30 P-1 P-2 P-3 P-4 P-5 2 24 29 17 00 18 30 P-6 P-7 P-8 P-9 P-10 P-11 P-12 P-13 3 4 28 16 00 17 30 P-14 P-15 P-16 4 14 29 17 00 18 30 P-17 P-18 P-19 P-20 P-21 P-22 5 24 28 16 00 17 30 P-23
More informationhttp://www2.math.kyushu-u.ac.jp/~hara/lectures/lectures-j.html 2 N(ε 1 ) N(ε 2 ) ε 1 ε 2 α ε ε 2 1 n N(ɛ) N ɛ ɛ- (1.1.3) n > N(ɛ) a n α < ɛ n N(ɛ) a n
http://www2.math.kyushu-u.ac.jp/~hara/lectures/lectures-j.html 1 1 1.1 ɛ-n 1 ɛ-n lim n a n = α n a n α 2 lim a n = 1 n a k n n k=1 1.1.7 ɛ-n 1.1.1 a n α a n n α lim n a n = α ɛ N(ɛ) n > N(ɛ) a n α < ɛ
More informationuntitled
10 log 10 W W 10 L W = 10 log 10 W 10 12 10 log 10 I I 0 I 0 =10 12 I = P2 ρc = ρcv2 L p = 10 log 10 p 2 p 0 2 = 20 log 10 p p = 20 log p 10 0 2 10 5 L 3 = 10 log 10 10 L 1 /10 +10 L 2 ( /10 ) L 1 =10
More information基礎数学I
I & II ii ii........... 22................. 25 12............... 28.................. 28.................... 31............. 32.................. 34 3 1 9.................... 1....................... 1............
More information... 3... 3... 3... 3... 4... 7... 10... 10... 11... 12... 12... 13... 14... 15... 18... 19... 20... 22... 22... 23 2
1 ... 3... 3... 3... 3... 4... 7... 10... 10... 11... 12... 12... 13... 14... 15... 18... 19... 20... 22... 22... 23 2 3 4 5 6 7 8 9 Excel2007 10 Excel2007 11 12 13 - 14 15 16 17 18 19 20 21 22 Excel2007
More informationDSGE Dynamic Stochastic General Equilibrium Model DSGE 5 2 DSGE DSGE ω 0 < ω < 1 1 DSGE Blanchard and Kahn VAR 3 MCMC 2 5 4 1 1 1.1 1. 2. 118
7 DSGE 2013 3 7 1 118 1.1............................ 118 1.2................................... 123 1.3.............................. 125 1.4..................... 127 1.5...................... 128 1.6..............
More information(1) (2) (1) (2) 2 3 {a n } a 2 + a 4 + a a n S n S n = n = S n
. 99 () 0 0 0 () 0 00 0 350 300 () 5 0 () 3 {a n } a + a 4 + a 6 + + a 40 30 53 47 77 95 30 83 4 n S n S n = n = S n 303 9 k d 9 45 k =, d = 99 a d n a n d n a n = a + (n )d a n a n S n S n = n(a + a n
More informationE B m e ( ) γma = F = e E + v B a m = 0.5MeV γ = E e m =957 E e GeV v β = v SPring-8 γ β γ E e [GeV] [ ] NewSUBARU.0 957 0.999999869 SPring-8 8.0 5656
SPring-8 PF( ) ( ) UVSOR( HiSOR( SPring-8.. 3. 4. 5. 6. 7. E B m e ( ) γma = F = e E + v B a m = 0.5MeV γ = E e m =957 E e GeV v β = v SPring-8 γ β γ E e [GeV] [ ] NewSUBARU.0 957 0.999999869 SPring-8
More informationuntitled
C08036 C08037 C08038 C08039 C08040 1. 1 2. 1 2.1 1 2.2 1 3. 1 3.1 2 4. 2 5. 3 5.1 3 5.2 3 6. 4 7. 5 8. 6 9. 7 10. 7 11. 8 C08036 8 C08037 9 C08038 10 C08039 11 C08040 12 8 2-1 2-2 T.P. 1 1 3-1 34 9 28
More information330
330 331 332 333 334 t t P 335 t R t t i R +(P P ) P =i t P = R + P 1+i t 336 uc R=uc P 337 338 339 340 341 342 343 π π β τ τ (1+π ) (1 βτ )(1 τ ) (1+π ) (1 βτ ) (1 τ ) (1+π ) (1 τ ) (1 τ ) 344 (1 βτ )(1
More information4 4. A p X A 1 X X A 1 A 4.3 X p X p X S(X) = E ((X p) ) X = X E(X) = E(X) p p 4.3p < p < 1 X X p f(i) = P (X = i) = p(1 p) i 1, i = 1,,... 1 + r + r
4 1 4 4.1 X P (X = 1) =.4, P (X = ) =.3, P (X = 1) =., P (X = ) =.1 E(X) = 1.4 +.3 + 1. +.1 = 4. X Y = X P (X = ) = P (X = 1) = P (X = ) = P (X = 1) = P (X = ) =. Y P (Y = ) = P (X = ) =., P (Y = 1) =
More information