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	Tail Paradox, Partial Identifiability, and Influential Priors in Bayesian Branch Length Inference
	Rannala, Bruce ; Zhu, Tianqi ; Yang, Ziheng
	2012
关键词	Bayesian phylogenetics Markov chain Monte Carlo branch lengths identifiability tail paradox compound dirichlet distribution PHYLOGENETIC INFERENCE METROPOLIS ALGORITHMS TREES CONVERGENCE DIVERGENCE SEQUENCES HASTINGS MODELS RATES TIMES
英文摘要	Recent studies have observed that Bayesian analyses of sequence data sets using the program MrBayes sometimes generate extremely large branch lengths, with posterior credibility intervals for the tree length (sum of branch lengths) excluding the maximum likelihood estimates. Suggested explanations for this phenomenon include the existence of multiple local peaks in the posterior, lack of convergence of the chain in the tail of the posterior, mixing problems, and misspecified priors on branch lengths. Here, we analyze the behavior of Bayesian Markov chain Monte Carlo algorithms when the chain is in the tail of the posterior distribution and note that all these phenomena can occur. In Bayesian phylogenetics, the likelihood function approaches a constant instead of zero when the branch lengths increase to infinity. The flat tail of the likelihood can cause poor mixing and undue influence of the prior. We suggest that the main cause of the extreme branch length estimates produced in many Bayesian analyses is the poor choice of a default prior on branch lengths in current Bayesian phylogenetic programs. The default prior in MrBayes assigns independent and identical distributions to branch lengths, imposing strong (and unreasonable) assumptions about the tree length. The problem is exacerbated by the strong correlation between the branch lengths and parameters in models of variable rates among sites or among site partitions. To resolve the problem, we suggest two multivariate priors for the branch lengths (called compound Dirichlet priors) that are fairly diffuse and demonstrate their utility in the special case of branch length estimation on a star phylogeny. Our analysis highlights the need for careful thought in the specification of high-dimensional priors in Bayesian analyses.; Biochemistry & Molecular Biology; Evolutionary Biology; Genetics & Heredity; SCI(E); 0; ARTICLE; 1; 325-335; 29
语种	英语
出处	SCI
出版者	分子生物学与进化
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/393995]
专题	数学科学学院
推荐引用方式 GB/T 7714	Rannala, Bruce,Zhu, Tianqi,Yang, Ziheng. Tail Paradox, Partial Identifiability, and Influential Priors in Bayesian Branch Length Inference. 2012-01-01.

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