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Probabilistic Graphical Model Representation in Phylogenetics

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 a... Full description

Journal Title: Systematic Biology 2014-09-01, Vol.63 (5), p.753-771
Main Author: Höhna, Sebastian
Other Authors: Heath, Tracy A , Boussau, Bastien , Landis, Michael J , Ronquist, Fredrik , Huelsenbeck, John P
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: England: Oxford University Press
ID: ISSN: 1063-5157
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recordid: cdi_swepub_primary_oai_DiVA_org_su_110769
title: Probabilistic Graphical Model Representation in Phylogenetics
format: Article
creator:
  • Höhna, Sebastian
  • Heath, Tracy A
  • Boussau, Bastien
  • Landis, Michael J
  • Ronquist, Fredrik
  • Huelsenbeck, John P
subjects:
  • Algorithms
  • Biological Sciences
  • Biological Systematics
  • Biologisk systematik
  • Biologiska vetenskaper
  • Classification - methods
  • Computation
  • Computer Simulation
  • Determinism
  • Diversity of life
  • Evolution
  • graphical models
  • Inference
  • Life Sciences
  • Livets mångfald
  • Matematik
  • Mathematics
  • Modeling
  • Models
  • Models, Statistical
  • modularization
  • Natural Sciences
  • Naturvetenskap
  • Phylogenetics
  • Phylogeny
  • Plant roots
  • Populations
  • Probabilistic modeling
  • Probability
  • Quantitative Biology
  • Random variables
  • Regular
  • Regular Articles
  • Simulation
  • Software
  • Statistical inference
  • statistical phylogenetics
  • Statistics
  • Systematic biology
  • Topology
  • tree plate
ispartof: Systematic Biology, 2014-09-01, Vol.63 (5), p.753-771
description: 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.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 1063-5157
fulltext: fulltext
issn:
  • 1063-5157
  • 1076-836X
  • 1076-836X
url: Link


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descriptionRecent 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.
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subjectAlgorithms ; Biological Sciences ; Biological Systematics ; Biologisk systematik ; Biologiska vetenskaper ; Classification - methods ; Computation ; Computer Simulation ; Determinism ; Diversity of life ; Evolution ; graphical models ; Inference ; Life Sciences ; Livets mångfald ; Matematik ; Mathematics ; Modeling ; Models ; Models, Statistical ; modularization ; Natural Sciences ; Naturvetenskap ; Phylogenetics ; Phylogeny ; Plant roots ; Populations ; Probabilistic modeling ; Probability ; Quantitative Biology ; Random variables ; Regular ; Regular Articles ; Simulation ; Software ; Statistical inference ; statistical phylogenetics ; Statistics ; Systematic biology ; Topology ; tree plate
ispartofSystematic Biology, 2014-09-01, Vol.63 (5), p.753-771
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0Copyright © 2014 Society of Systematic Biologists
1The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. 2014
2The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.
3Distributed under a Creative Commons Attribution 4.0 International License
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descriptionRecent 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.
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abstractRecent 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.
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