 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 chr22-3pop.vcf.gz                                                
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 +                                                                +
 +   POPULATION SIZE, MIGRATION, DIVERGENCE, ASSIGNMENT, HISTORY  +
 +   Bayesian inference using the structured coalescent           +
 +                                                                +
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  Compiled for a PARALLEL COMPUTER ARCHITECTURE
  One master and 5 compute nodes are available.
  PDF output enabled [Letter-size]
  Version 6.0.1 [Mittag (merged with main Oct 11 2025)]   [October-11-2025]
  Program started at   Tue Jan  6 18:48:19 2026
         finished at Tue Jan  6 18:49:01 2026
                          


Options in use:
---------------

Analysis strategy is BAYESIAN INFERENCE
    - Population size estimation: Theta [Exponential Distribution]
    - Geneflow estimation: Migration [Exponential Distribution]
    - Divergence estimation: Divergence time [Normal Distribution with mean and]
                                             [and standard deviation sigma     ]

Proposal distribution:
Parameter group          Proposal type
-----------------------  -------------------
Population size (Theta)  Metropolis sampling
Migration rate      (M)  Metropolis sampling
Divergence Time (D)  Metropolis sampling
Divergence time spread (STD) Metropolis sampling
Genealogy                Metropolis-Hastings


Prior distribution (Proposal-delta will be tuned to acceptance frequency 0.440000):
Parameter group            Prior type   Minimum    Mean(*)    Maximum    Delta      Bins   Updatefreq
-------------------------  ------------ ---------- ---------- ---------- ---------- ------ -------
Population size (Theta_1)   Exponential    0.000000   0.100000   0.200000           -   1500  0.07143
Population size (Theta_2)   Exponential    0.000000   0.100000   0.200000           -   1500  0.07143
Population size (Theta_3)   Exponential    0.000000   0.100000   0.200000           -   1500  0.07143
Ancestor 1 to 2 (D_time)   Exponential    0.000000   0.100000   0.200000           -   1500  0.07143
Ancestor 1 to 2 (S_time)   Exponential    0.000000   0.100000   0.200000           -   1500  0.07143
Ancestor 2 to 3 (D_time)   Exponential    0.000000   0.100000   0.200000           -   1500  0.07143
Ancestor 2 to 3 (S_time)   Exponential    0.000000   0.100000   0.200000           -   1500  0.07143
Datatype: DNA sequence data

Inheritance multipliers in use for Thetas (specified # 1)
All inheritance multipliers are the same [1.000000]

Pseudo-random number generator: Mersenne-Twister                                
Random number seed (with internal timer)            154180743

Start parameters:
   First genealogy was started using a random tree
   Start parameter values were generated
Connection matrix:
m = average (average over a group of Thetas or M,
s = symmetric migration M, S = symmetric 4Nm,
0 = zero, and not estimated,
* = migration free to vary, Thetas are on diagonal
d = row population split off column population
D = split and then migration
   1 Pop1           * 0 0 
   2 Pop2           d * 0 
   3 Pop3           0 d * 



Mutation rate is constant for all loci

Markov chain settings:
   Long chains (long-chains):                              1
      Steps sampled (long-inc*samples):              1000000
      Steps recorded (long-sample):                    10000
   Static heating scheme
      4 chains with  temperatures
       1.00, 1.50, 3.00,1000000.00
      Swapping interval is 1
   Burn-in per replicate (samples*inc):               100000

Print options:
   Data file:                                         infile
   Parameter file:                     parmfile-x00dx00dx-5e
   Haplotyping is turned on:                              NO
   Output file (ASCII text):            outfile-x00dx00dx-5e
   Output file (PDF):               outfile-x00dx00dx-5e.pdf
   Print data:                                            No
   Print genealogies:                                     No



Bayesian estimates
==================

Locus Parameter        2.5%      25.0%    mode     75.0%   97.5%     median   mean
-----------------------------------------------------------------------------------
    1  Theta_1         0.00000  0.00120  0.00327  0.00507  0.00947  0.00420  0.00275
    1  Theta_2         0.00000  0.00067  0.00313  0.00547  0.06480  0.00487  0.00876
    1  Theta_3         0.00000  0.00000  0.00353  0.00973  0.13173  0.00980  0.02450
    1  D_1->2          0.00000  0.00040  0.00273  0.00493  0.03133  0.00447  0.00447
    1  S_1->2          0.00000  0.00000  0.00313  0.00613  0.07520  0.00620  0.01142
    1  D_2->3          0.00000  0.00053  0.00220  0.00373  0.00787  0.00327  0.00072
    1  S_2->3          0.00000  0.00053  0.00247  0.00427  0.01373  0.00380  0.00220
    2  Theta_1         0.00000  0.00093  0.00287  0.00453  0.00840  0.00367  0.00183
    2  Theta_2         0.00000  0.00093  0.00273  0.00440  0.00827  0.00353  0.00169
    2  Theta_3         0.00000  0.00000  0.00287  0.00547  0.10853  0.00540  0.01404
    2  D_1->2          0.00000  0.00000  0.00420  0.01547  0.10147  0.01553  0.02516
    2  S_1->2          0.00000  0.00000  0.00473  0.03973  0.15587  0.03980  0.05374
    2  D_2->3          0.00000  0.00027  0.00260  0.00480  0.03427  0.00447  0.00440
    2  S_2->3          0.00000  0.00000  0.00300  0.00627  0.08413  0.00633  0.01197
    3  Theta_1         0.00000  0.00093  0.00287  0.00453  0.00880  0.00380  0.00200
    3  Theta_2         0.00000  0.00080  0.00260  0.00427  0.00800  0.00353  0.00143
    3  Theta_3         0.00000  0.00067  0.00247  0.00400  0.00800  0.00340  0.00126
    3  D_1->2          0.00000  0.00000  0.00447  0.01773  0.10613  0.01780  0.02782
    3  S_1->2          0.00000  0.00040  0.00580  0.04560  0.16507  0.04527  0.05878
    3  D_2->3          0.00000  0.00000  0.00433  0.01827  0.10160  0.01833  0.02792
    3  S_2->3          0.00000  0.00000  0.00567  0.04640  0.16613  0.04633  0.05957
    4  Theta_1         0.00000  0.00080  0.00273  0.00440  0.00840  0.00367  0.00169
    4  Theta_2         0.00000  0.00067  0.00247  0.00400  0.00787  0.00340  0.00122
    4  Theta_3         0.00000  0.00027  0.00287  0.00507  0.08440  0.00473  0.01075
    4  D_1->2          0.00000  0.00000  0.00353  0.00907  0.07533  0.00913  0.01627
    4  S_1->2          0.00000  0.00000  0.00393  0.02133  0.13600  0.02140  0.03695
    4  D_2->3          0.00000  0.00000  0.00300  0.00760  0.08467  0.00767  0.01407
    4  S_2->3          0.00000  0.00000  0.00327  0.01747  0.14627  0.01753  0.03136
    5  Theta_1         0.00000  0.00107  0.00300  0.00480  0.00907  0.00380  0.00217
    5  Theta_2         0.00000  0.00120  0.00327  0.00520  0.01067  0.00420  0.00322
    5  Theta_3         0.00000  0.00040  0.00287  0.00507  0.06760  0.00473  0.00879
    5  D_1->2          0.00000  0.00000  0.00393  0.01400  0.09667  0.01407  0.02296
    5  S_1->2          0.00000  0.00000  0.00433  0.03520  0.16120  0.03527  0.04897
    5  D_2->3          0.00000  0.00027  0.00287  0.00520  0.03787  0.00487  0.00541
    5  S_2->3          0.00000  0.00000  0.00313  0.00707  0.08840  0.00713  0.01368
  All  Theta_1         0.00000  0.00053  0.00220  0.00360  0.00667  0.00313  0.00220
  All  Theta_2         0.00000  0.00040  0.00207  0.00347  0.00653  0.00300  0.00205
  All  Theta_3         0.00000  0.00053  0.00207  0.00347  0.00653  0.00300  0.00200
  All  D_1->2          0.00000  0.00093  0.00273  0.00427  0.00747  0.00340  0.00273
  All  S_1->2          0.00000  0.00133  0.00340  0.00520  0.01000  0.00407  0.00399
  All  D_2->3          0.00000  0.00027  0.00180  0.00320  0.00627  0.00287  0.00184
  All  S_2->3          0.00000  0.00053  0.00220  0.00360  0.00680  0.00313  0.00220
-----------------------------------------------------------------------------------



Log-Probability of the data given the model (marginal likelihood = log(P(D|thisModel))
--------------------------------------------------------------------
[Use this value for Bayes factor calculations:
BF = Exp[log(P(D|thisModel) - log(P(D|otherModel)]
shows the support for thisModel]



Locus          TI(1a)       BTI(1b)         HS(2)
-------------------------------------------------
      1      -1623.85      -1480.36      -1432.27
      2      -1583.94      -1441.96      -1404.83
      3      -1573.45      -1431.29      -1405.10
      4      -1580.57      -1438.11      -1406.19
      5      -1597.88      -1458.55      -1423.54
---------------------------------------------------------------
  All        -7967.97      -7258.54      -7132.20
[Scaling factor = -8.278839]


(1a) TI: Thermodynamic integration: log(Prob(D|Model)): Good approximation with many temperatures
(1b) BTI: Bezier-approximated Thermodynamic integration: when using few temperatures USE THIS!
(2)  HS: Harmonic mean approximation: Overestimates the marginal likelihood, poor variance



MCMC run characteristics
========================




Acceptance ratios for all parameters and the genealogies
---------------------------------------------------------------------

Parameter           Accepted changes               Ratio
Theta_1                   9302/357229            0.02604
Theta_2                  14366/357310            0.04021
Theta_3                  53917/356200            0.15137
DD_1->2                  120185/357534            0.33615
DS_1->2                  157455/356479            0.44170
DD_2->3                   65642/356888            0.18393
DS_2->3                   91877/357236            0.25719
Genealogies            1258698/2501124           0.50325



Autocorrelation for all parameters and the genealogies
-------------------------------------------------------------------

Parameter           Autocorrelation           Effective Sample size
Theta_1                   0.511                 16644.716
Theta_2                   0.480                 17954.344
Theta_3                   0.510                 16524.599
D_1->2                    0.268                 29222.370
S_1->2                    0.333                 25600.869
D_2->3                    0.359                 24185.046
S_2->3                    0.435                 20319.493
Genealogies               0.718                  8451.222
(*) averaged over loci.



Temperatures during the run using the standard heating scheme
===========================================================================

Chain Temperature               log(marginal likelihood)  log(mL_steppingstone)
    1    1.00000          -1414.38647  -1110.81138
    2    1.00000          -1422.62730  -752.18623
    3    1.00000          -1455.75192  -393.87540
    4    1.00000          -2380.49308    26.37764
