Bayesian Inference in Phylogenetics

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Who are the authors of the publication 'Bayesian Inference of Phylogenetics Revisited'?

Christopher Randle, Mark E Mort, and Daniel Crawford. Signup and view all the answers

What is the primary focus of the article published in Taxon in February 2005?

The article focuses on developments and concerns regarding Bayesian inference in phylogenetics. Signup and view all the answers

Which university is associated with author Christopher Randle?

Sam Houston State University. Signup and view all the answers

What volume and issue number was the article 'Bayesian Inference of Phylogenetics Revisited' published in?

It was published in Volume 54, Issue 1. Signup and view all the answers

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<a href="http://www.jstor.org/stable/25065298">http://www.jstor.org/stable/25065298</a>. Signup and view all the answers

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The International Association for Plant Taxonomy (IAPT) published it in Taxon. Signup and view all the answers

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The publication has 15 citations. Signup and view all the answers

What is the DOI number for the article 'Bayesian Inference of Phylogenetics Revisited'?

The DOI number is 10.2307/25065298. Signup and view all the answers

Study Notes

Bayesian Inference of Phylogenetics

Bayesian methods have become prominent in phylogenetic studies using molecular evidence.
A recent trend sees Bayesian methods used in seven out of nine regular articles in the Systematic Biology journal.
The MrBayes program, a tool for Bayesian inference, has been cited 833 times (as of December 13, 2004).
Bayesian methods are computationally less demanding than likelihood methods, particularly for large datasets.
Bayesian inference estimates the posterior probability distribution, providing a direct measure of hypotheses.
Hypothesis testing using Bayesian methods includes monophyly, ancestral character state inference and rates of change across branches, among others.

Model Choice in Phylogenetic Inference

A robust process model is necessary in phylogenetic inference to measure probability.
Common models like Jukes-Cantor 69.
Model selection seeks a good fit to the data with a minimal number of parameters.
Under/over-parameterized models result in poor fit to data.
Methods like Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are alternatives to specific model testing.
Employing an inadequate model can lead to errors in both Bayesian and likelihood analyses. This can include providing high support for false clades, and under-supporting true clades.

Convergence and Mixing in Bayesian MCMC

Convergence refers to the iterative process where the posterior distribution obtained by Markov chain Monte Carlo simulation approaches the exact distribution from Bayes Theorem.
Mixing evaluates the efficiency of the Markov chain in exploring the parameter space.
Lack of adequate exploration leads to a wider posterior distribution.
MCMC chains are sampled over multiple generations.
Results for larger datasets can increase the time needed for convergence.
Metropolis-coupled MCMC(MC3) is used to address sampling issues in a larger parameter space.

Prior Probabilities

A prior probability assesses the likelihood of an hypothesis before considering data.
Most often, a "flat" or uniform prior is used in Bayesian analyses as an indicator of ignorance.
Uniform distribution assumes all hypotheses are equally probable.
Flat priors have minimal effect on the posterior, placing more importance on the data.
Clade size is relevant when establishing uniform priors.
Bayesian posterior estimations of clade support can be affected by uniform tree priors.

Clade Support Accuracy

Bayesian clade support is sometimes higher than resampling support in numerous studies.
The reason for these variations is unclear.
This disparity (inappropriately high support) is considered to be a "type I error" and to have a less favourable interpretation compared to resampling methods.
Underparameterization models can increase type I errors in Bayesian analyses.

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Description

Explore the role of Bayesian methods in phylogenetic inference, particularly with molecular evidence. This quiz highlights recent trends, tools like MrBayes, and the importance of model choice in analyzing phylogenetic data.