Make the most of your samples: Bayes factor estimators for high-dimensional models of sequence evolution.

ABSTRACT:

Background

Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio of two marginal likelihoods (one for each model). Recently introduced techniques to estimate (log) marginal likelihoods, such as path sampling and stepping-stone sampling, offer increased accuracy over the traditional harmonic mean estimator at an increased computational cost. Most often, each model's marginal likelihood will be estimated individually, which leads the resulting Bayes factor to suffer from errors associated with each of these independent estimation processes.

Results

We here assess the original 'model-switch' p

SUBMITTER: Baele G 

PROVIDER: S-EPMC3651733 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature