Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian phylogenetic inference. Approaches to estimate marginal likelihoods have garnered increased attention over the past decade. In particular, the introduction of path sampling (PS) and stepping-stone sampling (SS) into Bayesian phylogenetics has tremendously improved the accuracy of model selection. These sampling techniques are now used to evaluate complex evolutionary and population genetic models on empirical data sets, but considerable computational demands hamper their widespread adoption. Further, when very diffuse, but proper priors are specified for model parameters, numerical issues complicate the exploration of the priors, a necessary st...
Bayesian inference methods rely on numerical algorithms for both model selection and parameter infer...
Bayesian inference methods rely on numerical algorithms for both model selection and parameter infer...
Bayesian inference methods rely on numerical algorithms for both model selection and parameter infer...
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian ph...
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian ph...
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian ph...
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian ph...
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian ph...
By providing a framework of accounting for the shared ancestry inherent to all life, phylogenetics i...
Abstract.—The marginal likelihood is commonly used for comparing different evolutionary models in Ba...
Abstract.—The marginal likelihood is commonly used for comparing different evolutionary models in Ba...
The Bayes factor is commonly used for comparing different evolutionary rate models and different top...
The Bayes factor is commonly used for comparing different evolutionary rate models and different top...
It is widely accepted that species diversified in a tree like pattern from a common descendant and t...
Bayesian phylogenetic methods are generating noticeable enthusiasm in the field of molecular systema...
Bayesian inference methods rely on numerical algorithms for both model selection and parameter infer...
Bayesian inference methods rely on numerical algorithms for both model selection and parameter infer...
Bayesian inference methods rely on numerical algorithms for both model selection and parameter infer...
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian ph...
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian ph...
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian ph...
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian ph...
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian ph...
By providing a framework of accounting for the shared ancestry inherent to all life, phylogenetics i...
Abstract.—The marginal likelihood is commonly used for comparing different evolutionary models in Ba...
Abstract.—The marginal likelihood is commonly used for comparing different evolutionary models in Ba...
The Bayes factor is commonly used for comparing different evolutionary rate models and different top...
The Bayes factor is commonly used for comparing different evolutionary rate models and different top...
It is widely accepted that species diversified in a tree like pattern from a common descendant and t...
Bayesian phylogenetic methods are generating noticeable enthusiasm in the field of molecular systema...
Bayesian inference methods rely on numerical algorithms for both model selection and parameter infer...
Bayesian inference methods rely on numerical algorithms for both model selection and parameter infer...
Bayesian inference methods rely on numerical algorithms for both model selection and parameter infer...