Syst Zool 22:240–249įelsenstein J (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. Acta Biotheor 5:1–40įelsenstein J (1973) Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. J R Stat Soc B 32:155–174įeller W (1939) Die grundlagen der volterraschen theorie des kampfer ums dasein in wahrsheinlichkeits theoretischen behandlung. Evolution 21:550–570Įdwards AWF (1970) Estimation of the branch points of a branching diffusion process (with discussion). Proc R Soc Lond 226:271–302Ĭavalli-Sforza LL, Edwards AWF (1967) Phylogenetic analysis: models and estimation procedures. Springer-Verlag, New Yorkīishop MJ, Friday AE (1985) Evolutionary trees from nucleic acid and protein sequences. The results of the method are found to be insensitive to changes in the rate parameter of the branching process.īerger JO (1985) Statistical decision theory and Bayesian analysis. The best trees estimated by the new method are the same as those from the maximum likelihood analysis of separate topologies, but the posterior probabilities are quite different from the bootstrap proportions. Two example data sets are analyzed to infer the phylogenetic relationship of human, chimpanzee, gorilla, and orangutan. The posterior probability provides a natural measure of the reliability of the estimated phylogeny. We refer to this as the maximum posterior probability (MAP) tree. The posterior probabilities of different phylogenies are calculated and the phylogeny with the highest posterior probability is chosen as the best estimate of the evolutionary relationship among species. Parameters of the branching model and the substitution model are estimated by maximum likelihood.
Nucleotide substitution is modeled by a continuous-time Markov process. The birth-death process is used as a model of speciation and extinction to specify the prior distribution of phylogenies and branching times.
A new method is presented for inferring evolutionary trees using nucleotide sequence data.
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