摘要翻译:
从生物序列中推断进化树和进化速率通常使用性状变化的连续时间马尔可夫模型。马尔可夫过程沿着一棵未知的树演化,而观测只从树的尖端产生。在大多数实际数据集中都存在费率的异质性,这是通过使用灵活的混合模型来解释的,在这种模型中,每个站点都允许有自己的费率。目前在数据分析中常用的模型的可识别性很少得到严格的证明,尽管半参数模型的不可识别性被证明,而一般参数模型(GTR+Gamma+I)的可识别性证明是不正确的。在这里,我们证明了一个最广泛使用的模型(GTR+Gamma)对于一般参数和对于4状态(DNA)模型的所有参数选择都是可识别的。这是第一个证明具有连续速率分布的系统发生模型可识别性的证明。
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英文标题:
《Identifiability of a Markovian model of molecular evolution with
Gamma-distributed rates》
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作者:
Elizabeth S. Allman, Cecile Ane, John A. Rhodes
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最新提交年份:
2008
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分类信息:
一级分类:Mathematics 数学
二级分类:Statistics Theory 统计理论
分类描述:Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计:例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类:Quantitative Biology 数量生物学
二级分类:Populations and Evolution 种群与进化
分类描述:Population dynamics, spatio-temporal and epidemiological models, dynamic speciation, co-evolution, biodiversity, foodwebs, aging; molecular evolution and phylogeny; directed evolution; origin of life
种群动力学;时空和流行病学模型;动态物种形成;协同进化;生物多样性;食物网;老龄化;分子进化和系统发育;定向进化;生命起源
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一级分类:Statistics 统计学
二级分类:Statistics Theory 统计理论
分类描述:stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近,贝叶斯推论,决策理论,估计,基础,推论,检验。
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英文摘要:
Inference of evolutionary trees and rates from biological sequences is commonly performed using continuous-time Markov models of character change. The Markov process evolves along an unknown tree while observations arise only from the tips of the tree. Rate heterogeneity is present in most real data sets and is accounted for by the use of flexible mixture models where each site is allowed its own rate. Very little has been rigorously established concerning the identifiability of the models currently in common use in data analysis, although non-identifiability was proven for a semi-parametric model and an incorrect proof of identifiability was published for a general parametric model (GTR+Gamma+I). Here we prove that one of the most widely used models (GTR+Gamma) is identifiable for generic parameters, and for all parameter choices in the case of 4-state (DNA) models. This is the first proof of identifiability of a phylogenetic model with a continuous distribution of rates.
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PDF链接:
https://arxiv.org/pdf/709.0531