This is an up-to-date introduction to, and overview of, marginal likelihood computation for
model selection and hypothesis testing. Computing normalizing constants of probability …

The model evidence is a vital quantity in the comparison of statistical models under the
Bayesian paradigm. This study presents a review of commonly used methods. We outline …

Recent developments in marginal likelihood estimation for model selection in the field of
Bayesian phylogenetics and molecular evolution have emphasized the poor performance of …

Abstract Approximate Bayesian Computation (ABC) methods, also known as likelihood-free
techniques, have appeared in the past ten years as the most satisfactory approach to …

Recent implementations of path sampling (PS) and stepping-stone sampling (SS) have
been shown to outperform the harmonic mean estimator (HME) and a posterior simulation …

The marginal likelihood is commonly used for comparing different evolutionary models in
Bayesian phylogenetics and is the central quantity used in computing Bayes Factors for …

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian
Modeling Using WinBUGS provides an easily accessible introduction to the use of …

While there have been few theoretical contributions on the Markov Chain Monte Carlo
(MCMC) methods in the past decade, current understanding and application of MCMC to the …

For many biological investigations, groups of individuals are genetically sampled from
several geographic locations. These sampling locations often do not reflect the genetic …

The standard approach to Bayesian inference is based on the assumption that the
distribution of the data belongs to the chosen model class. However, even a small violation …