Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7 - PubMed (original) (raw)

Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

Andrew Rambaut et al. Syst Biol. 2018.

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.

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Figures

Figure 1.

Figure 1.

Overview of Tracer functionality and individual parameter visualizations: a) Main Tracer panel upon loading a single trace file; b) boxplot representation of two continuous parameters; corresponding c) kernel density estimates; d) violin plots; e) the actual traces connecting the parameter values visited by the Markov chain.

Figure 2.

Figure 2.

Multi-parameter visualizations of: a) the joint probability distribution of two integer variables through a bubble chart; b) the marginal density of multiple integer or categorical variables through frequency plots; c) two continuous variables through a classic scatter or correlation plot; d) multiple (formula image) continuous variables using large correlation matrices.

Figure 3.

Figure 3.

Estimating the effective population sizes over time using a Bayesian skygrid demographic reconstruction for rabies virus in North America.

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