• Corpus ID: 16939981

An alternative marginal likelihood estimator for phylogenetic models

  title={An alternative marginal likelihood estimator for phylogenetic models},
  author={Serena Arima and Luca Tardella},
  journal={arXiv: Computation},
Bayesian phylogenetic methods are generating noticeable enthusiasm in the field of molecular systematics. Many phylogenetic models are often at stake and different approaches are used to compare them within a Bayesian framework. The Bayes factor, defined as the ratio of the marginal likelihoods of two competing models, plays a key role in Bayesian model selection. We focus on an alternative estimator of the marginal likelihood whose computation is still a challenging problem. Several… 

Figures and Tables from this paper


Exploring fast computational strategies for probabilistic phylogenetic analysis.
Attention is drawn as to how MCMC techniques can be embedded within normal approximation strategies for more economical statistical computation, and several MCMC-based methods used in the statistical literature for such estimation are reviewed.
Performance-based selection of likelihood models for phylogeny estimation.
This work develops a novel approach to model selection, which is based on the Bayesian information criterion, but incorporates relative branch-length error as a performance measure in a decision theory (DT) framework.
Computing Bayes factors using thermodynamic integration.
The present article proposes to employ another method, based on an analogy with statistical physics, called thermodynamic integration, which is applied to the comparison of several alternative models of amino-acid replacement, indicating that modeling pattern heterogeneity across sites tends to yield better models than standard empirical matrices.
Markov Chasin Monte Carlo Algorithms for the Bayesian Analysis of Phylogenetic Trees
We further develop the Bayesian framework for analyzing aligned nucleotide sequence data to reconstruct phylogenies, assess uncertainty in the reconstructions, and perform other statistical
Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo.
The reversible jump Markov chain Monte Carlo algorithm described here allows estimation of phylogeny (and other phylogenetic model parameters) to be performed while accounting for uncertainty in the model of DNA substitution.
Empirical problems of the hierarchical likelihood ratio test for model selection.
  • D. Pol
  • Biology
    Systematic biology
  • 2004
Given the sensitivity of the LRT, some possible solutions to model selection (within the hypothesis testing approach) are outlined, and alternative model-selection criteria are discussed.
Selecting the best-fit model of nucleotide substitution.
It is shown here that a best-fit model can be readily identified and should be routine in any phylogenetic analysis that uses models of evolution.
Potential applications and pitfalls of Bayesian inference of phylogeny.
The Bayesian inference of phylogeny appears to possess advantages over the other methods in terms of ability to use complex models of evolution, ease of interpretation of the results, and computational efficiency.
Accounting for uncertainty in the tree topology has little effect on the decision-theoretic approach to model selection in phylogeny estimation.
This paper proposes two extensions to the decision-theoretic (DT) approach that relax the fixed-topology restriction and shows that varying the topology does not have a major impact on model choice, and uses the simpler methods in choosing a model for analyzing the data is more computationally feasible.
Approximate Bayesian-inference With the Weighted Likelihood Bootstrap
We introduce the weighted likelihood bootstrap (WLB) as a way to simulate approximately from a posterior distribution. This method is often easy to implement, requiring only an algorithm for