Probabilistic graphical model representation in phylogenetics.
Probabilistic graphical model representation in phylogenetics.
复制标题
系统发育学中的概率图形模型表示。
DOI：
10.1093/sysbio/syu039
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发表时间：
2014-09
影响因子：
6.5
通讯作者：
Huelsenbeck JP
中科院分区：
文献类型：
--
作者：
Höhna S;Heath TA;Boussau B;Landis MJ;Ronquist F;Huelsenbeck JP
近年来，在统计遗传学中探索的模型空间迅速扩大，强调了对统计模型表示和软件开发新方法的需求。清晰的沟通和所选模型的表示对于以下方面至关重要：（i）分析的可重复性，（ii）模型开发，以及（iii）软件设计。此外，一个统一的，清晰的和可理解的模型表示框架降低了初学者和非专业人士掌握复杂的系统发育模型，包括他们的假设和参数/变量的依赖性的障碍。图形建模是近年来在统计文献中流行的统一框架。其核心思想是将复杂的模型分解为条件独立的分布。其优势在于这种形式主义的可理解性、灵活性和适应性，以及基于它的大量计算工作。图形模型非常适合教授统计模型，促进系统发生学家之间的交流，以及开发通用软件用于模拟和统计推理。在这里，我们提供了一个介绍图形模型的遗传学家和扩展标准的图形模型表示的领域的遗传学。我们引入了一个新的图形模型组件，树板，捕捉不断变化的结构对应的系统发育树的子图。我们描述了一系列的系统发育模型，使用图形模型框架，并引入模块，以简化大型和复杂的模型中的标准组件的表示。系统发生模型图可以很容易地用于模拟、最大似然推断和贝叶斯推断，例如使用后验分布的Metropolis-Hastings或Gibbs采样。[计算;图形模型;推理;模块化;统计遗传学;树板。]
Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (i) reproducibility of an analysis, (ii) model development, and (iii) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and nonspecialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis–Hastings or Gibbs sampling of the posterior distribution. [Computation; graphical models; inference; modularization; statistical phylogenetics; tree plate.]
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影响因子：
1.7
作者：
Friedman, N;Ninio, M;Pupko, T
通讯作者：
Pupko, T
影响因子：
2.7
作者：
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通讯作者：
HASTINGS, WK
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10.7
作者：
Bollback, JP
通讯作者：
Bollback, JP
影响因子：
10.7
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通讯作者：
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影响因子：
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作者：
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通讯作者：
Hohna, Sebastian