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Most Parsimonious Likelihood Exhibits Multiple Optima for Compatible Characters.

Julia Matsieva1, Katherine St John2,3

  • 1Department of Computer Science, University of California, Davis, Davis, CA, 95616-8562, USA. jmatsieva@ucdavis.edu.

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Summary
This summary is machine-generated.

Multiple optimal solutions can arise when using maximum likelihood estimators for phylogenetic tree analysis, even with compatible data. This highlights the need for careful interpretation of evolutionary history results.

Keywords:
Maximum likelihood estimatorsMaximum parsimony criteriaModels of evolutionPhylogenetic trees

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Maximum likelihood (ML) estimators are widely used for phylogenetic tree scoring to infer evolutionary histories.
  • Previous work by Steel (1994) demonstrated multiple optimal branch length sets for ML with incompatible data.
  • Parsimony methods often perform well with data compatible with the tree topology.

Purpose of the Study:

  • To investigate if multiple optima occur for ML estimators when using sequence data compatible with the tree.
  • To compare the behavior of ML estimators with parsimony methods regarding data compatibility and multiple optima.

Main Methods:

  • Analysis of maximum likelihood estimators for phylogenetic tree scoring.
  • Examination of multiple local optima for compatible character data.
  • Comparison with parsimony techniques in phylogenetic inference.

Main Results:

  • Multiple local optima were found to occur for the most parsimonious likelihood estimator, even with compatible character data.
  • This behavior mirrors findings in parsimony methods but was previously unconfirmed for ML with compatible data.
  • The study confirms that ML estimators can yield multiple solutions under specific conditions.

Conclusions:

  • Understanding the potential for multiple optima in likelihood-based phylogenetic inference is crucial.
  • Caution is advised when interpreting biological data using likelihood criteria due to potential ambiguities.
  • Further research into the properties of likelihood estimators is necessary for robust evolutionary analyses.