A hierarchical Bayesian model for calibrating estimates of species divergence times.

Systematic Biology
Tracy A Heath

Abstract

In Bayesian divergence time estimation methods, incorporating calibrating information from the fossil record is commonly done by assigning prior densities to ancestral nodes in the tree. Calibration prior densities are typically parametric distributions offset by minimum age estimates provided by the fossil record. Specification of the parameters of calibration densities requires the user to quantify his or her prior knowledge of the age of the ancestral node relative to the age of its calibrating fossil. The values of these parameters can, potentially, result in biased estimates of node ages if they lead to overly informative prior distributions. Accordingly, determining parameter values that lead to adequate prior densities is not straightforward. In this study, I present a hierarchical Bayesian model for calibrating divergence time analyses with multiple fossil age constraints. This approach applies a Dirichlet process prior as a hyperprior on the parameters of calibration prior densities. Specifically, this model assumes that the rate parameters of exponential prior distributions on calibrated nodes are distributed according to a Dirichlet process, whereby the rate parameters are clustered into distinct parameter categories...Continue Reading

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Citations

Dec 18, 2013·Genetics·Justin P BlumenstielCasey M Bergman
Jul 11, 2014·Proceedings of the National Academy of Sciences of the United States of America·Tracy A HeathTanja Stadler
Jun 22, 2014·Systematic Biology·Sebastian HöhnaJohn P Huelsenbeck
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Sep 5, 2020·Molecular Biology and Evolution·Uyen Mai, Siavash Mirarab
Jun 25, 2017·American Journal of Botany·Eric B Knox, Chunjiao Li

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