AWTY (are we there yet?): a system for graphical exploration of MCMC convergence in Bayesian phylogenetics
Florida State University · University of California, Davis
Abstract
UNLABELLED: A key element to a successful Markov chain Monte Carlo (MCMC) inference is the programming and run performance of the Markov chain. However, the explicit use of quality assessments of the MCMC simulations-convergence diagnostics-in phylogenetics is still uncommon. Here, we present a simple tool that uses the output from MCMC simulations and visualizes a number of properties of primary interest in a Bayesian phylogenetic analysis, such as convergence rates of posterior split probabilities and branch lengths. Graphical exploration of the output from phylogenetic MCMC simulations gives intuitive and often crucial information on the success and reliability of the analysis. The tool presented here…
Citation impact
- FWCI
- 155.16
- Percentile
- 100%
- References
- 13
Authors
4- JAJohan A. A. NylanderCorresponding
Florida State University, University of California, Davis
- JCJames C. Wilgenbusch
Florida State University, University of California, Davis
- DLDan L. Warren
Florida State University, University of California, Davis
- DLDavid L. Swofford
Florida State University, University of California, Davis
Topics & keywords
- Markov chain Monte Carlo
- Computer science
- Bayesian probability
- Bayesian inference
- Convergence (economics)
- Markov chain
- Inference
- Data mining