Comparison of Bayesian and maximum-likelihood inference of population genetic parameters
Comparison of Bayesian and maximum-likelihood inference of population genetic parameters
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DOI：
10.1093/bioinformatics/bti803
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发表时间：
2006-02-01
期刊：
影响因子：
5.8
通讯作者：
Beerli, P
中科院分区：
文献类型：
--
作者：
Beerli, P
比较不同的推理方法，如最大似然（ML）和贝叶斯推理的性能和准确性是困难的，因为推理方法是在不同的程序中实现的，通常由不同的作者编写。这两种方法都在MIGRATE程序中实现，该程序使用聚结理论估计种群遗传参数，如种群大小和迁移率。这两种推理方法使用相同的马尔可夫链蒙特卡罗算法，彼此不同的只有两个方面：参数建议分布和似然函数的最大化。使用模拟数据集，贝叶斯方法通常在准确性和覆盖率方面优于ML方法，尽管对于某些值，这两种方法在性能上是相等的。动机：基于马尔可夫链蒙特卡洛的ML框架可能会在稀疏数据上失败，并且可以提供非保守的支持间隔。一个贝叶斯框架与适当的先验分布是能够弥补这些problems.Results：程序MIGRATE扩展到不仅允许ML（-）最大似然估计的群体遗传学参数，但也使用贝叶斯框架。贝叶斯方法和ML方法之间的比较是方便的，因为这两种模式估计相同的参数下相同的人口模型和假设。
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.