# 19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology.

@article{Fourment201819DW, title={19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology.}, author={Mathieu Fourment and Andrew F. Magee and Chris Whidden and Arman Bilge and Frederick Albert Matsen IV and Vladimir N. Minin}, journal={Systematic biology}, year={2018} }

The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model. The marginal likelihood is the normalizing constant for the posterior density, obtained by integrating the product of the likelihood and the prior with respect to model parameters. Thus, the computational burden of computing the marginal likelihood scales with the dimension of the parameter space. In phylogenetics, where we work with tree topologies that are high…

## 33 Citations

### Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies

- Computer ScienceSystematic biology
- 2019

This paper presents an efficient parallelized method to map out the high likelihood set of phylogenetic tree topologies via systematic search, and shows that the normalized topology likelihoods are a useful proxy for the Bayesian posterior probability of those topologies.

### Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics

- Computer SciencebioRxiv
- 2017

A general parallelization strategy is introduced that distributes the power posterior MCMC simulations and the likelihood computations over available CPUs and enables the estimation of marginal likelihoods to complete in a feasible amount of time which previously needed days, weeks or even months.

### Efficient Bayesian inference of general Gaussian models on large phylogenetic trees

- Computer ScienceThe Annals of Applied Statistics
- 2021

A scalable Bayesian inference framework under a general Gaussian trait evolution model that exploits Hamiltonian Monte Carlo enables efficient sampling of the constrained model parameters and takes advantage of the tree structure for fast likelihood and gradient computations, yielding algorithmic complexity linear in the number of observations.

### Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference

- Computer ScienceArXiv
- 2023

This paper proposes an approach and an implementation framework to relax the rigidity of the prior densities by learning their parameters using a gradient-based method and a neural network-based parameterization and highlights that using neural networks improves the initialization of the optimization of thePrior density parameters.

### EFFICIENT BAYESIAN INFERENCE OF GENERAL GAUSSIAN MODELS

- Computer Science
- 2020

A scalable Bayesian framework under a general Gaussian trait evolution model that enables efficient sampling of the constrained model parameters and takes advantage of the tree structure for fast likelihood and gradient computations is presented.

### Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics

- Computer SciencebioRxiv
- 2019

It is shown that many commonly used phylogenetic models including the general time reversible (GTR) substitution model, rate heterogeneity among sites, and a range of coalescent models can be implemented using a probabilistic programming language.

### Computing Bayes: Bayesian Computation from 1763 to the 21st Century

- Computer Science
- 2020

This paper takes the reader on a chronological tour of Bayesian computation over the past two and a half centuries, and place all computational problems into a common framework, and describe all computational methods using a common notation.

### Bayesian Evaluation of Temporal Signal in Measurably Evolving Populations

- BiologybioRxiv
- 2019

The results indicate that BETS is an effective alternative to other measures of temporal signal, which has the key advantage of allowing a coherent assessment of the entire model, including the molecular clock and tree prior which are essential aspects of Bayesian phylodynamic analyses.

### Parsimony analysis of phylogenomic datasets (I): scripts and guidelines for using TNT (Tree Analysis using New Technology)

- BiologyCladistics
- 2021

The computationally most efficient and versatile parsimony software, TNT, is described, which can be used for phylogenetic and phylogenomic analyses, and a series of scripts that are specifically designed for the analysis of phylogenomic datasets are described.

### Lagged couplings diagnose Markov chain Monte Carlo phylogenetic inference

- Mathematics
- 2021

: Phylogenetic inference is an intractable statistical problem on a complex space. Markov chain Monte Carlo methods are the primary tool for Bayesian phylogenetic inference but it is challenging to…

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