CPU Time Applications

1. Modeling Bayesian Phylogenetic Inference in Protein Data Analysis by Using Mr. Bayes, Proml, Consensus Applications

2. Mr. Bayes vs. Proml (maximum likelihood)

3. CPU time/Mr. Bayes/Proml

4. Diff. of Maximum Likelihood (Mr. Bayes – Proml) vs. CPU (sec)

5. Diff. of Maximum Likelihood (Mr. Bayes – Proml) vs. CPU (sec)

6. Linear Regression in Testing Datasets

7. Testing Datasets Plus One/Two Long Branch’s Datasets

8. Linear Regression After Bayesian Correction for Testing Datasets & One/Two Long Branch’s Datasets

9. Phylogeny for All Testing Datasets

10. Phylogeny for All Datasets

11. One Long Branch Datasets

12. Two Long Branches Datasets

13. Phylogeny (sequence length from Proml)

14. One/Two Long Branch’s Datasets (Maximum Likelihood)

17. Testing Datasets y(Mr.Bayes)= 1.058351726x(Proml)+14.79771 0.999724 correl 14.79771 intercept 1.058352 slope Testing datasets in linear regression between Mr. Bayes and Proml) 104.6243 131.7778 878.5355 sd 226.959 296.3333 1576.467 mean diff(Mr.Bayes-Proml) character CPU Testing samples:

18. 0.996717 correl 0.109857 f-test 5343.856 intercept 3.47E-05 t-test 0.492193 slope Linear regression between experimental samples: 364.0589 179.5717 sd 13122.86 11802.88 mean CD(two long branches) AB(one long branch) Experimental samples:

19. Linear Regression for All y(Mr. Bayes)= 1.058352x(Proml)+14.79764 0.999959 correl 14.79764 intercept 1.058352 slope Linear regression for all datasets(including experimental and testing) 385.302 190.05 sd 13903.5 12506.4 mean CD(two long branches) AB(one long branch) After Bayesian modeling

21. Histogram AB10J

23. Histogram CD10J