large:
This is large in speed 2.
It runs an MCMC algorithm
Reading options from mcmctree.ctl..
Reading master tree.
(((a, b), ((c, d), e)), (f, (g, (h, i))));
Reading sequence data.. 1 loci
*** Locus 1 ***
ns = 9 ls = 215833
Reading sequences, sequential format..
Reading seq # 1: a
Reading seq # 2: b
Reading seq # 3: c
Reading seq # 4: d
Reading seq # 5: e
Reading seq # 6: f
Reading seq # 7: g
Reading seq # 8: h
Reading seq # 9: i
215833 site patterns read, 200000000 sites
Counting frequencies..
215833 patterns, clean
442025984 bytes for conP, 0 bytes for conP0
0 burnin, sampled every 1, 20 samples
Approximating posterior (Settings: cleandata=1. print=0 saveconP=1.)
getting initial values to start MCMC.
priors:
G(6.0000, 2.0000) for kappa
G(1.0000, 1.0000) for alpha
G(2.0000, 2.0000) for rgene
Initial parameters (np = 11):
0.945 0.606 0.448 0.563 0.372 0.872 0.640 0.452 0.739 2.152 0.588
lnL0 = -2197386427.52
Starting MCMC...
finetune steps (time rate mixing para RatePara?): 0.001 0.001 0.800 0.010
paras: times, rates, sigma2, correl, kappa, alpha
5% 0.50 0.00 1.00 0.50 0.832 0.534 0.394 0.495 0.327 -2197178046.0 0:05
10% 0.50 0.00 1.00 0.50 0.798 0.512 0.378 0.475 0.314 -2196902682.8 0:10
15% 0.50 0.33 0.67 0.50 0.787 0.505 0.372 0.468 0.309 -2196706637.4 0:14
20% 0.53 0.25 0.75 0.62 0.783 0.503 0.370 0.466 0.308 -2196363815.4 0:19
25% 0.55 0.20 0.60 0.50 0.781 0.501 0.369 0.464 0.307 -2196270005.3 0:23
30% 0.54 0.33 0.50 0.50 0.779 0.500 0.368 0.463 0.306 -2195987932.5 0:28
35% 0.52 0.43 0.43 0.50 0.778 0.500 0.368 0.463 0.306 -2195823430.1 0:33
40% 0.52 0.38 0.38 0.50 0.778 0.499 0.368 0.462 0.306 -2195414814.2 0:37
45% 0.51 0.33 0.33 0.44 0.777 0.499 0.367 0.462 0.305 -2195262951.6 0:41
50% 0.53 0.40 0.30 0.45 0.777 0.499 0.367 0.462 0.305 -2194870827.1 0:46
55% 0.49 0.36 0.36 0.45 0.766 0.492 0.362 0.455 0.301 -2194829851.6 0:51
60% 0.50 0.42 0.42 0.42 0.767 0.493 0.362 0.456 0.301 -2194659620.3 0:55
65% 0.49 0.46 0.38 0.42 0.769 0.493 0.363 0.456 0.302 -2194147677.2 1:00
70% 0.48 0.50 0.36 0.43 0.770 0.494 0.363 0.457 0.302 -2193958431.0 1:05
75% 0.48 0.53 0.33 0.43 0.771 0.495 0.364 0.457 0.302 -2193662346.9 1:09
80% 0.48 0.50 0.38 0.44 0.766 0.492 0.361 0.454 0.300 -2193210952.2 1:14
85% 0.49 0.53 0.35 0.44 0.762 0.489 0.359 0.452 0.299 -2192798742.2 1:19
90% 0.49 0.50 0.33 0.47 0.758 0.487 0.357 0.450 0.297 -2192286505.2 1:24
95% 0.48 0.53 0.32 0.45 0.754 0.485 0.356 0.448 0.296 -2192133162.8 1:28
100% 0.49 0.55 0.35 0.45 0.753 0.484 0.355 0.447 0.295 -2191882540.7 1:33
Time used: 1:33
This is small in speed 2.
It calculates 20 transition-probability matrices of size 400x400.
(A) repeated squaring
1/ 20 P00(4.19) = 0.386203729 0:02
2/ 20 P00(2.53) = 0.537066951 0:05
3/ 20 P00(0.84) = 0.799213805 0:07
4/ 20 P00(0.68) = 0.832898484 0:10
5/ 20 P00(3.70) = 0.399355478 0:12
6/ 20 P00(0.46) = 0.889539120 0:15
7/ 20 P00(4.31) = 0.335684843 0:17
8/ 20 P00(4.82) = 0.325967311 0:20
9/ 20 P00(0.93) = 0.793737266 0:22
10/ 20 P00(0.38) = 0.918190395 0:25
11/ 20 P00(0.91) = 0.801778102 0:27
12/ 20 P00(3.30) = 0.429690709 0:29
13/ 20 P00(0.01) = 0.996570165 0:32
14/ 20 P00(0.33) = 0.924427670 0:34
15/ 20 P00(2.25) = 0.593340081 0:37
16/ 20 P00(0.45) = 0.904184404 0:39
17/ 20 P00(4.96) = 0.297681422 0:42
18/ 20 P00(0.89) = 0.794941530 0:44
19/ 20 P00(0.49) = 0.885739119 0:47
20/ 20 P00(2.26) = 0.583081265 0:49
(B) eigensolution
1/ 20 P00(4.19) = 0.386203676 0:50
2/ 20 P00(2.53) = 0.537066931 0:50
3/ 20 P00(0.84) = 0.799213804 0:51
4/ 20 P00(0.68) = 0.832898483 0:52
5/ 20 P00(3.70) = 0.399355428 0:53
6/ 20 P00(0.46) = 0.889539119 0:53
7/ 20 P00(4.31) = 0.335684773 0:54
8/ 20 P00(4.82) = 0.325967237 0:55
9/ 20 P00(0.93) = 0.793737264 0:55
10/ 20 P00(0.38) = 0.918190395 0:56
11/ 20 P00(0.91) = 0.801778101 0:57
12/ 20 P00(3.30) = 0.429690667 0:57
13/ 20 P00(0.01) = 0.996570165 0:58
14/ 20 P00(0.33) = 0.924427670 0:59
15/ 20 P00(2.25) = 0.593340068 0:59
16/ 20 P00(0.45) = 0.904184403 1:00
17/ 20 P00(4.96) = 0.297681337 1:01
18/ 20 P00(0.89) = 0.794941529 1:02
19/ 20 P00(0.49) = 0.885739119 1:02
20/ 20 P00(2.26) = 0.583081251 1:03
本文介绍了使用MCMC算法进行参数估计的过程,并记录了不同阶段的采样参数和似然值变化情况。此外,还对比了两种不同方法计算转移概率矩阵的速度差异。

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