J
John P. Huelsenbeck
Researcher at University of California, Berkeley
Publications - 106
Citations - 93015
John P. Huelsenbeck is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Markov chain Monte Carlo & Phylogenetic tree. The author has an hindex of 69, co-authored 105 publications receiving 84401 citations. Previous affiliations of John P. Huelsenbeck include University of California, San Diego & Smithsonian Institution.
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MrBayes 3: Bayesian phylogenetic inference under mixed models
TL;DR: MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models to analyze heterogeneous data sets and explore a wide variety of structured models mixing partition-unique and shared parameters.
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MRBAYES: Bayesian inference of phylogenetic trees
TL;DR: The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo, and an executable is available at http://brahms.rochester.edu/software.html.
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MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice across a Large Model Space
Fredrik Ronquist,Maxim Teslenko,Paul van der Mark,Daniel L. Ayres,Aaron E. Darling,Sebastian Höhna,Bret Larget,Liang Liu,Marc A. Suchard,John P. Huelsenbeck +9 more
TL;DR: The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly, and provides more output options than previously, including samples of ancestral states, site rates, site dN/dS rations, branch rates, and node dates.
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Bayesian inference of phylogeny and its impact on evolutionary biology
TL;DR: Bayesian inference of phylogeny brings a new perspective to a number of outstanding issues in evolutionary biology, including the analysis of large phylogenetic trees and complex evolutionary models and the detection of the footprint of natural selection in DNA sequences.
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Bayesian Phylogenetic Analysis of Combined Data
TL;DR: A Bayesian MCMC approach to the analysis of combined data sets was developed and its utility in inferring relationships among gall wasps based on data from morphology and four genes was explored, supporting the utility of morphological data in multigene analyses.