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Maximum Likelihood and Minimum-Steps Methods for Estimating Evolutionary Trees from Data on Discrete Characters
Vol. 22, No. 3 (Sep., 1973), pp. 240-249
Stable URL: http://www.jstor.org/stable/2412304
Page Count: 10
You can always find the topics here!Topics: Maximum likelihood estimation, Topology, Evolution, Statistical estimation, Parsimony, Maximum likelihood estimators, Consistent estimators, Point estimators, Data models, Population estimates
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The general maximum likelihood approach to the statistical estimation of phylogenies is outlined, for data in which there are a number of discrete states for each character. The details of the maximum likelihood method will depend on the details of the probabilistic model of evolution assumed. There are a very large number of possible models of evolution. For a few of the simpler models, the calculation of the likelihood of an evolutionary tree is outlined. For these models, the maximum likelihood tree will be the same as the "most parsimonious" (or minimum-steps) tree if the probability of change during the evolution of the group is assumed a priori to be very small. However, most sets of data require too many assumed state changes per character to be compatible with this assumption. Farris (1973) has argued that maximum likelihood and parsimony methods are identical under a much less restrictive set of assumptions. It is argued that the present methods are preferable to his, and a counterexample to his argument is presented. An algorithm which enables rapid calculation of the likelihood of a phylogeny is described.
Systematic Zoology © 1973 Oxford University Press