Data from: 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: (1)
reproducibility of an analysis, (2) model development and (3) software
design. Moreover, a unified, clear and understandable framework for model
representation lowers the barrier for beginners and non-specialists 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.