Understanding the tripartite approach to Bayesian divergence time estimation:
Placing evolutionary events in the context of geological time is a fundamental goal in paleobiology and macroevolution. In this Element we describe the tripartite model used for Bayesian estimation of time calibrated phylogenetic trees. The model can be readily separated into its component models: t...
Gespeichert in:
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| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2020
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| Schriftenreihe: | Cambridge elements. Elements of paleontology
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| Links: | https://doi.org/10.1017/9781108954365 |
| Zusammenfassung: | Placing evolutionary events in the context of geological time is a fundamental goal in paleobiology and macroevolution. In this Element we describe the tripartite model used for Bayesian estimation of time calibrated phylogenetic trees. The model can be readily separated into its component models: the substitution model, the clock model and the tree model. We provide an overview of the most widely used models for each component and highlight the advantages of implementing the tripartite model within a Bayesian framework. |
| Umfang: | 1 Online-Ressource (39 Seiten) |
| ISBN: | 9781108954365 |
| ISSN: | 2517-780X |
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| spelling | Warnock, Rachel C. M. Understanding the tripartite approach to Bayesian divergence time estimation Rachel C.M. Warnock, April M. Wright Cambridge Cambridge University Press 2020 1 Online-Ressource (39 Seiten) txt c cr Cambridge elements. Elements of paleontology 2517-780X Placing evolutionary events in the context of geological time is a fundamental goal in paleobiology and macroevolution. In this Element we describe the tripartite model used for Bayesian estimation of time calibrated phylogenetic trees. The model can be readily separated into its component models: the substitution model, the clock model and the tree model. We provide an overview of the most widely used models for each component and highlight the advantages of implementing the tripartite model within a Bayesian framework. Wright, April M. Erscheint auch als Druck-Ausgabe 9781108949422 |
| spellingShingle | Warnock, Rachel C. M. Understanding the tripartite approach to Bayesian divergence time estimation |
| title | Understanding the tripartite approach to Bayesian divergence time estimation |
| title_auth | Understanding the tripartite approach to Bayesian divergence time estimation |
| title_exact_search | Understanding the tripartite approach to Bayesian divergence time estimation |
| title_full | Understanding the tripartite approach to Bayesian divergence time estimation Rachel C.M. Warnock, April M. Wright |
| title_fullStr | Understanding the tripartite approach to Bayesian divergence time estimation Rachel C.M. Warnock, April M. Wright |
| title_full_unstemmed | Understanding the tripartite approach to Bayesian divergence time estimation Rachel C.M. Warnock, April M. Wright |
| title_short | Understanding the tripartite approach to Bayesian divergence time estimation |
| title_sort | understanding the tripartite approach to bayesian divergence time estimation |
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