A Tutorial on Bridge Sampling

Understand

The marginal likelihood plays an important role in many areas of Bayesian statistics such as parameter estimation, model comparison, and model averaging.

• In most applications, however, the marginal likelihood is not analytically tractable and must be approximated using numerical methods.
• Here we provide a tutorial on bridge sampling (Bennett, 1976; Meng & Wong, 1996), a reliable and relatively straightforward sampling method that allows researchers to obtain the marginal likelihood for models of varying complexity.
• First, we introduce bridge sampling and three related sampling methods using the beta-binomial model as a running example.