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- Fredrik Ronquist, John P. Huelsenbeck
- Bioinformatics
- 2003

MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types-e.g. morphological, nucleotide, and protein-and to explore a wide variety of structured models… (More)

- John P. Huelsenbeck, Fredrik Ronquist
- Bioinformatics
- 2001

SUMMARY
The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo.
AVAILABILITY
MRBAYES, including the source code, documentation, sample data files, and an executable, is available at http://brahms.biology.rochester.edu/software.html.

- Fredrik Ronquist, Maxim Teslenko, +7 authors John P. Huelsenbeck
- Systematic biology
- 2012

Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple… (More)

- J P Huelsenbeck, F Ronquist, R Nielsen, J P Bollback
- Science
- 2001

As a discipline, phylogenetics is becoming transformed by a flood of molecular data. These data allow broad questions to be asked about the history of life, but also present difficult statistical and computational problems. Bayesian inference of phylogeny brings a new perspective to a number of outstanding issues in evolutionary biology, including the… (More)

- Gautam Altekar, Sandhya Dwarkadas, John P. Huelsenbeck, Fredrik Ronquist
- Bioinformatics
- 2004

MOTIVATION
Bayesian estimation of phylogeny is based on the posterior probability distribution of trees. Currently, the only numerical method that can effectively approximate posterior probabilities of trees is Markov chain Monte Carlo (MCMC). Standard implementations of MCMC can be prone to entrapment in local optima. Metropolis coupled MCMC [(MC)(3)], a… (More)

- Johan A A Nylander, Fredrik Ronquist, John P Huelsenbeck, José Luis Nieves-Aldrey
- Systematic biology
- 2004

The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed… (More)

- D M Hillis, J P Huelsenbeck
- The Journal of heredity
- 1992

DNA sequences and other molecular data compared among organisms may contain phylogenetic signal, or they may be randomized with respect to phylogenetic history. Some method is needed to distinguish phylogenetic signal from random noise to avoid analysis of data that have been randomized with respect to the historical relationships of the taxa being… (More)

- John Huelsenbeck, Bruce Rannala
- Systematic biology
- 2004

What does the posterior probability of a phylogenetic tree mean?This simulation study shows that Bayesian posterior probabilities have the meaning that is typically ascribed to them; the posterior probability of a tree is the probability that the tree is correct, assuming that the model is correct. At the same time, the Bayesian method can be sensitive to… (More)

- John P Huelsenbeck, Bret Larget, Richard E Miller, Fredrik Ronquist
- Systematic biology
- 2002

Only recently has Bayesian inference of phylogeny been proposed. The method is now a practical alternative to the other methods; indeed, the method appears to possess advantages over the other methods in terms of ability to use complex models of evolution, ease of interpretation of the results, and computational efficiency. However, the method should be… (More)

- John P Huelsenbeck, Peter Andolfatto
- Genetics
- 2007

Inferring population structure from genetic data sampled from some number of individuals is a formidable statistical problem. One widely used approach considers the number of populations to be fixed and calculates the posterior probability of assigning individuals to each population. More recently, the assignment of individuals to populations and the number… (More)