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Example sentences for: mrbayes
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Because bootstrap analysis using ML is not computationally feasible for large data sets [ 85 ] , we conducted a Bayesian analysis as an alternative employing optimization parameters similar to the nucleotide substitution model that we used for ML [ 86 87 ] . The distinction between ML and Bayesian inference is that Bayesian provides probabilities for hypotheses, not probabilities of data given a hypothesis [ 85 87 88 ] . Bayesian analysis uses Markov Chain Monte Carlo (MCMC) methods to approximate posterior probability distributions that are a direct estimation of branch support because they are the true probabilities of the resulting clades under the assumed models, unlike bootstrap values [ 87 88 ] . Additionally, bootstrap values and posterior probabilities derived from Bayesian analyses for multiple data sets appear to be correlated [ 88 ] . The Bayesian analysis was conducted with the software MrBayes 2.0 [ 89 ] . A GTR substitution model with 6 rate frequencies was selected as the most similar model to the Trn+G substitution model (the latter model is not available in MrBayes).
Posterior Probabilities calculated with MrBayes program
Additional analyses of the putative nodL gene were conducted with Version 3.0b4 of the MrBayes software [ 56 ] . For these analyses, the Jones-Taylor-Thornton model of amino-acid replacement [ 57 ] was adopted and variation of replacement rates among sites was incorporated by a discretized gamma distribution with four rate categories [ 58 ] . Each Markov chain Monte Carlo analysis used four heated chains and employed a burn-in period of 10,000 cycles, followed by 990,000 additional cycles.
For the genome quartet #8 we calculated posterior probabilities under the model which takes ASRV into account with Strimmer and von Haeseler's [ 24 ] approach and with the MrBayes program version 2.01 [ 31 ] . TREE-PUZZLE 5.0 [ 56 ] was used to calculate posterior probabilities according to Strimmer and von Haeseler [ 24 ] . A discrete approximation of the gamma distribution [ 57 ] was used to describe ASRV.
Posterior probabilities were also calculated with MrBayes version 2.01 [ 31 ] . Each QuartOP was analyzed with two simultaneous Markov chains for 25,000 cycles under the JTT substitution model [ 55 ] without ASRV.
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