Comparison of Bayesian and maximum-likelihood inference of population genetic parameters

Comparison of the performance and accuracy of different inference methods, such as maximum likelihood (ML) and Bayesian inference, is difficult because the inference methods are implemented in different programs, often written by different authors. Both methods were implemented in the program MIGRAT...

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Bibliographic Details
Published in:Bioinformatics 2006-02, Vol.22 (3), p.341-345
Main Author: BEERLI, Peter
Format: Article
Language:English
Subjects:
Bayes Theorem
Biological and medical sciences
Biological Evolution
Chromosome Mapping - methods
Computer Simulation
Fundamental and applied biological sciences. Psychology
General aspects
Genetics, Population - methods
Likelihood Functions
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Models, Statistical
Phylogeny
Software
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Summary:Comparison of the performance and accuracy of different inference methods, such as maximum likelihood (ML) and Bayesian inference, is difficult because the inference methods are implemented in different programs, often written by different authors. Both methods were implemented in the program MIGRATE, that estimates population genetic parameters, such as population sizes and migration rates, using coalescence theory. Both inference methods use the same Markov chain Monte Carlo algorithm and differ from each other in only two aspects: parameter proposal distribution and maximization of the likelihood function. Using simulated datasets, the Bayesian method generally fares better than the ML approach in accuracy and coverage, although for some values the two approaches are equal in performance. Motivation: The Markov chain Monte Carlo-based ML framework can fail on sparse data and can deliver non-conservative support intervals. A Bayesian framework with appropriate prior distribution is able to remedy some of these problems. Results: The program MIGRATE was extended to allow not only for ML(-) maximum likelihood estimation of population genetics parameters but also for using a Bayesian framework. Comparisons between the Bayesian approach and the ML approach are facilitated because both modes estimate the same parameters under the same population model and assumptions. Availability: The program is available from Contact:beerli@csit.fsu.edu
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bti803