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- W1556088785 abstract "Maximum-likelihood (ML) estimation of phylogenies has reached a rather high level of sophistication because of algorithmic advances, improvements in models of sequence evolution, and improvements in statistical approaches and application of cluster computing. Here, I provide a brief basic background in application of the general principle of ML estimation to phylogenetics and provide an example of selecting among a nested set of ML models using a dynamic approach to hierarchical likelihood-ratio tests. I focus attention on PAUP* because it provides unique ease of switching among alternative optimality criteria (e.g., minimum evolution, parsimony, and ML). Further, examples of parametric bootstrap tests are provided that demonstrate statistical tests of phylogenetic hypotheses and model adequacy, in an absolute rather than relative sense. The increasing availability of clustered, parallelized computation makes use of such parametric approaches feasible." @default.
- W1556088785 created "2016-06-24" @default.
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- W1556088785 date "2005-01-01" @default.
- W1556088785 modified "2023-10-06" @default.
- W1556088785 title "Maximum-Likelihood Methods for Phylogeny Estimation" @default.
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- W1556088785 doi "https://doi.org/10.1016/s0076-6879(05)95039-8" @default.
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