π “” “” π( 480 ) 12288 24576 π 3.14159261864<π<3.141592706934
7 π(2)
π
Buffon
Buffon 1777 2a 2l ( 1 )
- 27 -
(N)(n) Buffon
Buffon
M y M φ l sinφ y sinφ 0y 0φπ 1
l
a
a
l
aN
Fig 1. The Diagram of Buffon’s needle experiment
an
l 22 (,2004)
P l π a
Buffon (Buffon’s needle) (2,4) y φ P π
π r
2 r x2 + y2 r2 –r x,y r () x2 + y2 r2( ) (n)
-r r
area of a circle
4r2( N
N
π
231 Research Bulletin of KDARES Vol.23(1)
(2007) 10 7.05% 10 1000 26%(260 ) 7.05% (1)
( 3) I 3.
)()(
I
N
3 .....
Z 3N k (Boltzmann)T 10 () 103N 20 1060
),( jiv
- 30 -
231 Research Bulletin of KDARES Vol.23(1)
1052 3.17 × 1044
Ising (degree of freedom)
3D (denature) 100 2100 1030
1 1 1018 3.17 × 1010 3D Wolynes(1995)(Funnel)
(12)
(1999) (Hydrophilic)(Hydrophobic)(Polar) H P HP (Importance
Sampling) ( 4)
4. Fig 4. Several possible directions for reversing
the protein chain (, 1999)
231 Research Bulletin of KDARES Vol.23(1)
Efron 1979 n X b
X*1X*b X* n X
75 15 15
15 10000 15 0.0654 75 15 10000 0.0540 5
5. ()() Fig 5. The sampling distribution of Monte Carlo (left) and
the bootstrap method (right).(,2007)
- 33 -
(1)
(Simulated Annealing, SA)
Kirkpatrick
E(x) T E E’ P = min(1,
kTe E'-E
kTe E'-E
>1 P 1 E’>E
P = kTe E'-E
3D
(Genetic Algorithm, GA)
1975 (Genetic Algorithm)
(3)
2.FLSD F LSD
3.TSDTurkey (Turkey’s Significant Difference)
4.DMRTDuncan (Duncan’s Multiple Range Test)
5.BLSD LSD (Bayesian LSD)
15 1 (RCBD) 346 8 4 Yij = μ + τi + βj + εij μ 100τi βj i j εij
N(0,10)
1000 15 × 4 × 1000 = 60000 5
- 36 -
- 37 -
1. 15 Table 1. Sets of true treatment means used in simulation
study. (Carmer and Swanson, 1971)
5 (type-IIIIII error)
TSD F LSD DMRT FLSD BLSD FLSD BLSD F DMRT Carmer Swanson FLSD
student-t (7)
F F LSD
231 Research Bulletin of KDARES Vol.23(1)
1.
2. × Table 2. Genetic models used in the deterministic
simulations. Each table cell represents a two-locus genotype and
its genotypic value is given. (Jannink, 2003)
Jannink(2003) × AB × 2 10 1
Jannink(2003) 10 () 24 8 a=1 ε=2 r=0.5 Ve=500 SP=50% AB PA=PB=0.5 0
6
- 38 -
(8)
6. (Jannink, 2003) Fig. 6. Response to selection in the population
mean under additive and epistatic models.
2.
(donor) Stam Zeven(1981) (recurrent) (11) M.
Humberto(2000) 1. 4 2 2. 1 1 3. 2 1 200cM (centimorgans) 1cM (BC1
BC2)3 200 5000 Stam Zeven(1981)
7 (10)
1 2 (crossover)
- 39 -
7. (M. Humberto, 2000) Fig. 7. Graphical representation of the
introgression results, generated by Monte Carlo simulations
of marker-based selection in backcross breeding. The continuous
line represents the theoretical probabilities of donor genome along
the given chromosome, as calculated by the model herein presented.
The dots show the observed frequencies as obtained by the
simulations.
- 40 -
1. . 2007. 3.10.11.Bootstrap. .
2. . 2004. π. 28(2):70-82.
3. . 2004. .
24(2):307-319.
405-409.
6. (). 1990. . pp. 149-170.
7. Carmer, S.G., and M.R. Swanson. 1971. Detection of differences
between
means: A Monte Carlo study of five pairwise multiple comparison
procedures.
Agron. J. 63:940-945.
8. Jannink, J.L. 2003. Selection dynamics and limits under additive
x additive
epistatic gene action. Crop Sci. 43: 489-497.
9. Mohammadi, S.A. and B.M. Prasanna. 2003. Analysis of genetic
diversity in
crop plants-salient statistical tools and considerations. Crop Sci.
43:1235-
1248.
10. M. Humberto, Reyes-Valdés. 2000. A model for marker-based
selection in
gene introgression breeding programs. Crop Sci. 40:91-98.
11. Stam, P., and A.C. Zeven. 1981. The theoretical proportion of
the donor
genome in near-isogenic lines of self-fertilizers bred by
back-crossing.
Euphytica 30:227–238.
12. Wolynes P.G., Onuchic J.N., Thirumalai D. 1995. Navigating the
folding
routes. Science 267:1619-1620.