Abstract
Abstract Likelihood estimation is central to many areas of the natural and physical sciences and has had a major impact on molecular phylogenetics. In this chapter we provide a concise review of some of the theoretical and computational aspects of likelihood-based phylogenetic inference. We outline the basic probabilistic model and likelihood computation algorithm, as well as extensions to more realistic models and strategics of likelihood optimization. We survey several of the theoretical underpinnings of the likelihood framework, reviewing research on consistency, identifiability, and the effect of model mis-specification, as well as advantages, and limitations, of likelihood ratio tests.
Keywords
IdentifiabilityInferenceMaximum likelihoodConsistency (knowledge bases)Computer scienceProbabilistic logicStatistical inferenceLikelihood functionApproximate Bayesian computationEconometricsMachine learningArtificial intelligenceStatisticsMathematics
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Publication Info
- Year
- 2005
- Type
- book-chapter
- Pages
- 33-62
- Citations
- 60
- Access
- Closed
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Cite This
David Bryant,
Nicolas Galtier,
Marie-Anne Poursat
(2005).
Likelihood Calculation in Molecular Phylogenetics.
, 33-62.
https://doi.org/10.1093/oso/9780198566106.003.0002
Identifiers
- DOI
- 10.1093/oso/9780198566106.003.0002