arrow
返回

Probabilistic Graphical Model Representation in Phylogenetics

delete2014-06-20
delete97
delete
OA
AI
S
Sebastian Höhna *
T
Tracy A. Heath
B
Bastien Boussau
M
Michael J. Landis
F
Fredrik Ronquist
J
John P. Huelsenbeck
DOI:10.1093/sysbio/syu039delete
delete原文链接
delete分享
delete收藏
查看原文
摘要

摘要

En 中文
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.
Keyword:
Computation
graphical models
inference
modularization
statistical phylogenetics
tree plate
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Systematic Biology 封面图
Systematic Biology
IF:
5.7
论文数:
2.2K
被引数:
1.9W

机构

U
University of Kansas
学者数:
1.9W
论文数: 1.7W
被引数: 8.1K
U
university of california davis
学者数:
3.4W
论文数: 2.6W
被引数: 45
S
Stockholm University
学者数:
1.8W
论文数: 1.7W
被引数: 32
学者 查看更多机构
引用论文

引用论文

O− radical anions on polycrystalline MgO多晶MgO上的o-自由基阴离子
err2002-12-01
err0
PREAI
errCristiana Di Valentin; Davide Ricci; Gianfranco Pacchioni; Mario Chiesa; Maria Cristina Paganini; Elio Giamello
err分享
err收藏
Potential of eggplant peel as by-product
err2015-03-01
err0
errOAAI
errM. Kadivec; M. Kopjar; D. Žnidarčič; T. Požrl
err分享
err收藏
VITERBI ALGORITHM
err1973-01-01
err3.9K
PREAI
errFORNEY, GD
err分享
err收藏
学者 查看更多内容