Computational methods for Bayesian model choice

This document discusses several computational methods for Bayesian model choice, including importance sampling, cross-model solutions, nested sampling, and approximate Bayesian computation (ABC) model choice. It introduces Bayes factors and marginal likelihoods as key quantities for Bayesian model comparison, and describes how Bayesian model choice involves allocating probabilities to different models and computing the marginal likelihood or evidence for each model.