Models for estimating bayes factors with applications to phylogeny and tests of monophyly.
Models for estimating bayes factors with applications to phylogeny and tests of monophyly.
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用于估计贝叶斯因子的模型及其在系统发育和单系测试中的应用。
DOI：
10.1111/j.1541-0420.2005.00352.x
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
Sinsheimer,JanetS
中科院分区：
文献类型：
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作者：
Suchard,MarcA;Weiss,RobertE;Sinsheimer,JanetS
比较两个或多个竞争假设的贝叶斯因子通常通过构建马尔可夫链蒙特卡罗 (MCMC) 采样器来探索假设的联合空间来估计。为了获得有效的贝叶斯因子估计，（1995，皇家统计学会杂志，系列 B57,473-484）建议调整竞争假设的先验赔率，使后验赔率大约为 1，然后通过简单除法估计贝叶斯因子。一个副产品是，人们经常会产生几个独立的 MCMC 链，但实际上只有其中一个用于估计。我们通过提出三种统计模型扩展了这种方法，以整合多个链的输出。第一个假设独立采样器使用逻辑回归对先验赔率的各种选择绘制假设指标函数并对其进行建模。两个更复杂的模型通过允许 MCMC 输出内更高的滞后依赖性来放宽独立性假设。这些模型使我们能够估计贝叶斯因子计算中的不确定性，并充分利用几个不同的 MCMC 链，即使假设的先验赔率因链而异。我们应用这些方法来计算两个系统发育示例中单系性测试的贝叶斯因子。第一个示例探讨了未知病原体与一组已知病原体的关系。未知物单系关系的鉴定可能会影响临床环境中抗生素的选择。第二个例子侧重于 HIV 重组检测。对于潜在的临床应用，必须尽可能高效地完成这些类型的分析。
Bayes factors comparing two or more competing hypotheses are often estimated by constructing a Markov chain Monte Carlo (MCMC) sampler to explore the joint space of the hypotheses. To obtain efficient Bayes factor estimates, (1995,Journal of the Royal Statistical Society, Series B57,473–484) suggest adjusting the prior odds of the competing hypotheses so that the posterior odds are approximately one, then estimating the Bayes factor by simple division. A byproduct is that one often produces several independent MCMC chains, only one of which is actually used for estimation. We extend this approach to incorporate output from multiple chains by proposing three statistical models. The first assumes independent sampler draws and models the hypothesis indicator function using logistic regression for various choices of the prior odds. The two more complex models relax the independence assumption by allowing for higher-lag dependence within the MCMC output. These models allow us to estimate the uncertainty in our Bayes factor calculation and to fully use several different MCMC chains even when the prior odds of the hypotheses vary from chain to chain. We apply these methods to calculate Bayes factors for tests of monophyly in two phylogenetic examples. The first example explores the relationship of an unknown pathogen to a set of known pathogens. Identification of the unknown's monophyletic relationship may affect antibiotic choice in a clinical setting. The second example focuses on HIV recombination detection. For potential clinical application, these types of analyses must be completed as efficiently as possible.