A Comparison of Monte Carlo Based Marginal Likelihood Estimators
Abstract
ABSTRACT Marginal likelihood plays a central role in Bayesian model comparison and hypothesis testing, but its computation is often challenging in practice. This article reviews recent Monte Carlo methods that rely on the availability of Markov chain Monte Carlo (MCMC) samples from the posterior and prior distributions along with the corresponding unnormalized kernels that can be evaluated numerically. Within this scope, we summarize the strengths, limitations, differences, and connections of different methods. Two in‐depth applications are presented to illustrate their relative performance. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods Statistical Models > Model Selection Statistical Models > Bayesian Models
Fields of Study & Subjects
Markov Chains and Monte Carlo Methods
Bayesian Methods and Mixture Models
Statistical Methods and Bayesian Inference
Publication details
- Journal / source
- Wiley Interdisciplinary Reviews Computational Statistics
- Publisher
- Wiley
- Volume
- 18
- Issue
- 1
- Publication date
- 30 January 2026
- Language
- EN