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Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

Systematic Biology · 2018 · Vol. 67(5) · pp. 901–904
Andrew RambautAlexei J. DrummondDong XieGuy BaeleMarc A. Suchard

Abstract

Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.

Genomics and Phylogenetic StudiesEvolution and Paleontology StudiesGenetic diversity and population structureMarkov chain Monte CarloPosterior probabilityBayesian probabilityBayesian inferenceComputer scienceInferenceArtificial intelligenceBiology

MeSH terms

Bayes TheoremMarkov ChainsModels, GeneticMonte Carlo MethodPhylogenySoftwareEvolution, Molecular

Funding

  • National Science Foundation
  • Wellcome Trust
  • European Commission
  • KU Leuven
  • National Institutes of Health
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